Benchmarking Novel NBS Genes: Strategies for Validating Disease Resistance Genes in Biomedical Research

Abstract

This article provides a comprehensive framework for benchmarking novel Nucleotide-Binding Site (NBS) genes against established disease resistance genes. Aimed at researchers and drug development professionals, it covers the foundational biology of NBS-LRR genes, explores advanced methodological pipelines for gene discovery and characterization, addresses common troubleshooting and optimization challenges in benchmarking studies, and outlines rigorous validation and comparative analysis techniques. The synthesis of these core intents offers a critical roadmap for integrating novel resistance genes into therapeutic development and precision medicine strategies, ensuring robust and reproducible genomic findings.

The NBS Gene Family: Understanding the Foundation of Disease Resistance

Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) genes constitute one of the largest and most critical gene families in plant innate immunity, serving as intracellular sentinels that detect pathogen invasion and activate robust defense responses. These genes encode proteins that function as key receptors in Effector-Triggered Immunity (ETI), a sophisticated plant defense mechanism that recognizes pathogen effector molecules and initiates signaling cascades leading to hypersensitive response (HR) and localized programmed cell death [1] [2]. The NBS-LRR family has undergone remarkable diversification across plant species through gene duplication, birth-and-death evolution, and diversifying selection, resulting in complex genomic architectures that enable plants to recognize rapidly evolving pathogens [3] [4]. Understanding the classification, distribution, signaling mechanisms, and experimental characterization of these genes provides the foundation for benchmarking novel NBS genes against established resistance genes and developing crop varieties with enhanced disease resistance.

Classification and Genomic Distribution of NBS-LRR Genes

Structural Classification and Domain Architecture

NBS-LRR genes are classified based on their N-terminal domain organization into distinct subfamilies with different signaling pathways and evolutionary patterns. The major classes include:

• TNL (TIR-NBS-LRR): Characterized by an N-terminal Toll/Interleukin-1 Receptor (TIR) domain that often signals through Enhanced Disease Susceptibility 1 (EDS1)
• CNL (CC-NBS-LRR): Features a coiled-coil (CC) domain at the N-terminus and may utilize both EDS1 and Non-Race-Specific Disease Resistance (NDR1) signaling pathways
• RNL (RPW8-NBS-LRR): Contains Resistance to Powdery Mildew 8 (RPW8) domain and often functions downstream in signal transduction [5] [6]

Additionally, truncated variants exist across all classes, including TN (TIR-NBS), CN (CC-NBS), and N (NBS-only) types, which may function as adaptors or regulators for full-length NBS-LRR proteins [5]. The central NBS (NB-ARC) domain binds and hydrolyzes nucleotides, serving as a molecular switch between inactive and active states, while the C-terminal LRR domain is primarily responsible for pathogen recognition specificity through protein-protein interactions [1] [2].

Comparative Genomic Distribution Across Plant Species

The NBS-LRR gene family demonstrates remarkable variation in size and composition across plant species, reflecting diverse evolutionary paths and adaptation to specific pathogen pressures. The table below summarizes the distribution of NBS-LRR genes across recently studied plant species:

Table 1: NBS-LRR Gene Distribution Across Plant Species

Plant Species	Total NBS Genes	TNL	CNL	Other/Partial	Key Pathogen Resistance
Nicotiana benthamiana	156	5	25	126	Multiple viral pathogens [5]
Nicotiana tabacum	603	~15*	~140*	~448*	Black shank, bacterial wilt [7]
Vernicia montana (Resistant)	149	12	98	39	Fusarium wilt [8] [9]
Vernicia fordii (Susceptible)	90	0	49	41	Fusarium wilt [8] [9]
Manihot esculenta (Cassava)	327	34	128	165	Cassava mosaic disease [2]

*Estimated based on percentage distribution provided in source material

Several evolutionary patterns have been observed in NBS-LRR gene families, including "consistent expansion" in potato and soybean, "expansion followed by contraction" in tomato and yellowhorn, and "shrinking" patterns in pepper and some Rosaceae species [6]. The absence of TNL genes has been documented in certain eudicot species, including Vernicia fordii and Sesamum indicum, representing lineage-specific losses that may reflect alternative pathogen recognition strategies [8] [9].

Experimental Protocols for NBS-LRR Gene Identification and Characterization

Bioinformatics Identification Pipeline

Genome-wide identification of NBS-LRR genes employs a standardized bioinformatics workflow combining homology searches and domain verification:

• Sequence Retrieval: Obtain complete genome sequences and annotated protein datasets from species-specific databases or repositories such as Phytozome or NCBI.

• HMMER Search: Perform Hidden Markov Model searches using HMMER v3.1b2 or later with the NB-ARC domain model (PF00931) from the Pfam database, applying an E-value cutoff of <1×10⁻²⁰ for initial identification [7] [2].

• Domain Verification: Confirm putative NBS-LRR genes using:

  • Pfam database for TIR (PF01582), RPW8 (PF05659), and LRR domains (PF00560, PF07723, PF07725, PF12799)
  • NCBI Conserved Domain Database (CDD) for coiled-coil domains
  • Paircoil2 or COILS with P-score cutoff of 0.03 for CC domain prediction [2]

• Classification and Annotation: Categorize validated genes into subfamilies based on domain architecture and annotate with genomic position and structural features.

• Phylogenetic Analysis: Construct phylogenetic trees using Maximum Likelihood method in MEGA6 or MEGA11 with 1000 bootstrap replicates based on aligned NB-ARC domain sequences [2].

Figure 1: Bioinformatics Pipeline for NBS-LRR Gene Identification

Functional Characterization Experiments

Functional validation of NBS-LRR genes requires both in planta and molecular biology approaches:

Gene Expression Analysis:

• RNA Extraction: Isolate RNA from pathogen-infected and control tissues at multiple time points
• Transcript Quantification: Perform RNA-seq analysis with Hisat2 alignment and Cufflinks/Cuffdiff for differential expression or use qRT-PCR with reference genes for validation
• Promoter Analysis: Identify cis-regulatory elements using PlantCARE database on 1500bp upstream sequences [5] [7]

Functional Validation:

• Transient Overexpression: Use Agrobacterium-mediated transient expression in Nicotiana benthamiana to assess hypersensitive response induction
• Stable Transformation: Develop transgenic lines overexpressing candidate NBS-LRR genes for resistance phenotyping
• Virus-Induced Gene Silencing (VIGS): Knock down candidate genes in resistant varieties to confirm loss of function [10] [8]

Phenotypic Assessment:

• Disease Scoring: Monitor symptom development using standardized disease indices
• Pathogen Biomass Quantification: Measure pathogen load through qPCR of pathogen-specific genes
• Hypersensitive Response Documentation: Record cell death symptoms and conduct trypan blue staining for cell death visualization [10]

Signaling Mechanisms and Pathway Engineering

NBS-LRR Activation and Signal Transduction

NBS-LRR proteins function as intracellular immune receptors that monitor pathogen effectors through direct or indirect recognition mechanisms. The guard hypothesis proposes that NBS-LRR proteins "guard" host proteins that are modified by pathogen effectors, while the decoy hypothesis suggests that some NBS-LRR proteins interact with host proteins that mimic effector targets but lack functional domains [1]. The activation mechanism involves:

• Effector Recognition: The LRR domain detects pathogen effectors either through direct binding or by monitoring the status of guarded host proteins.

• Nucleotide-Dependent Conformational Change: Upon recognition, the NBS domain undergoes a conformational shift from ADP-bound (inactive) to ATP-bound (active) state.

• Oligomerization and Signaling Complex Formation: Activated NBS-LRR proteins form oligomers and interact with downstream signaling components, with TNL and CNL proteins often engaging distinct signaling pathways.

• Defense Activation: Signaling cascades lead to transcriptional reprogramming, production of antimicrobial compounds, and hypersensitive response to restrict pathogen spread [5] [1].

Figure 2: NBS-LRR Protein Activation Pathway

Pathway Engineering and Synthetic Biology

Engineering NBS-LRR genes for enhanced disease resistance requires understanding of their signaling networks and regulatory mechanisms. Key approaches include:

• Gene Stacking: Combining multiple R genes with different recognition specificities to provide broader and more durable resistance
• Promoter Engineering: Modifying promoter sequences to fine-tune expression patterns and amplitude
• Chimeric Receptor Creation: Swapping LRR domains between NBS-LRR proteins to alter recognition specificities
• Pathway Component Modulation: Co-expressing downstream signaling components to enhance resistance output

Recent studies demonstrate that overexpression of specific NBS-LRR genes can confer resistance to challenging pathogens. For instance, transgenic tobacco plants overexpressing NtRPP13 showed significantly enhanced resistance to Ralstonia solanacearum, with elevated levels of jasmonic acid and salicylic acid and upregulation of defense-related marker genes [10].

The Scientist's Toolkit: Essential Research Reagents

Table 2: Essential Research Reagents for NBS-LRR Gene Studies

Reagent/Tool	Application	Specifications	Key Features
HMMER Suite	Domain identification	Version 3.1b2+	Hidden Markov Model search with PF00931 (NB-ARC)
Pfam Database	Domain annotation	Release 27+	Curated domain models (TIR, LRR, RPW8)
MEME Suite	Motif discovery	Version 5.0+	Identifies conserved motifs in protein families
PlantCARE	Cis-element analysis	Online tool	Promoter element identification in 1500bp upstream
VIGS Vectors	Functional validation	TRV-based systems	Virus-Induced Gene Silencing in Nicotiana
Agrobacterium Strains	Transient expression	GV3101, LBA4404	Protein localization and HR assays
MEGA Software	Phylogenetic analysis	Version 6+	Evolutionary relationships with bootstrap support
Phytozome	Genomic data	JGI portal	Curated plant genomes and annotations
Bis(2-methoxyethyl) phthalate-3,4,5,6-D4	Bis(2-methoxyethyl) Phthalate-3,4,5,6-D4|CAS 1398065-54-7	Bis(2-methoxyethyl) phthalate-3,4,5,6-D4 is a deuterated internal standard for plasticizer analysis. For Research Use Only. Not for human or veterinary use.	Bench Chemicals
Naloxonazine dihydrochloride	Naloxonazine dihydrochloride, MF:C38H44Cl2N4O6, MW:723.7 g/mol	Chemical Reagent	Bench Chemicals

The systematic identification and functional characterization of NBS-LRR genes provides crucial insights for benchmarking novel resistance genes against established immune receptors. Effective benchmarking requires multidimensional assessment including phylogenetic position, expression dynamics, subcellular localization, and functional validation against target pathogens. The expanding toolkit of genomic technologies, particularly the integration of machine learning approaches for R gene prediction [1], promises to accelerate the discovery and engineering of NBS-LRR genes for crop improvement. Future research directions should focus on understanding the precise mechanisms of effector recognition, decoding the signaling networks downstream of different NBS-LRR classes, and developing engineering strategies that provide broad-spectrum resistance without yield penalties. As our knowledge of these remarkable immune receptors grows, so does our capacity to develop sustainable crop protection strategies based on natural plant immunity mechanisms.

Plant immunity relies on a sophisticated surveillance system where Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) proteins serve as critical intracellular immune receptors. These proteins, encoded by one of the largest gene families in plants, recognize pathogen-specific effector molecules to initiate robust defense responses collectively termed effector-triggered immunity (ETI) [11]. The NBS-LRR family is categorized into distinct subfamilies based on variations in their N-terminal domains: those with a Coiled-Coil (CC) domain (CNL), those with a Toll/Interleukin-1 Receptor (TIR) domain (TNL), and those featuring a Resistance to Powdery Mildew 8 (RPW8) domain (RNL) [6] [5]. This classification is not merely structural but reflects fundamental differences in signaling pathways and immune functions. A comprehensive understanding of these subfamilies—their distribution, evolution, and mechanisms—provides the essential foundation for benchmarking novel NBS genes and engineering disease-resistant crops.

Structural Characteristics and Domain Architecture

The functional specialization of NBS-LRR subfamilies is rooted in their distinct protein architectures. All three subfamilies share a central Nucleotide-Binding Site (NBS) domain, responsible for ATP/GTP binding and hydrolysis, and a C-terminal Leucine-Rich Repeat (LRR) domain, which is primarily involved in pathogen recognition [11] [4]. The defining difference lies in their N-terminal domains, which dictate specific signaling partners and immune outputs.

• CNL Proteins: The N-terminal Coiled-Coil (CC) domain is often involved in triggering cell death signaling and can play a role in protein-protein interactions [12].
• TNL Proteins: The N-terminal Toll/Interleukin-1 Receptor (TIR) domain possesses enzymatic activity and is crucial for initiating a defense signaling cascade [11] [12].
• RNL Proteins: This smaller subfamily is characterized by an N-terminal RPW8 domain. RNLs do not typically function as primary pathogen sensors but act as helper NLRs that transduce immune signals downstream of both CNLs and TNLs [6] [5].

The following diagram illustrates the canonical domain structures and the simplified signaling pathways associated with each NBS-LRR subfamily.

Beyond the typical full-length proteins, many plant genomes contain a significant number of "atypical" or "irregular" NBS-LRR genes. These variants may lack the N-terminal domain (NL-type), the LRR domain (CN-type, TN-type, N-type), or other regions, potentially functioning as regulators or decoys in the plant immune network [11] [5].

Comparative Genomic Analysis and Evolutionary Patterns

Genome-wide comparative analyses across diverse plant species reveal that the CNL, TNL, and RNL subfamilies exhibit remarkable variation in their copy numbers and evolutionary trajectories. These dynamics are driven by species-specific events of gene duplication and loss, which are crucial for adapting to local pathogen pressures.

Table 1: Genomic Distribution of NBS-LRR Subfamilies Across Selected Plant Species

Species	Total NBS-LRRs	CNL	TNL	RNL	Key Observations	Citation
Arabidopsis thaliana	207	61	101	7 (inferred)	Model for TNL and CNL diversity; used for phylogenetic comparison.	[11] [6]
Salvia miltiorrhiza	196	61	2	1	Marked reduction in TNL and RNL members compared to other dicots.	[11]
Nicotiana benthamiana	156	25	5	4 (RPW8-N)	Low count of TNL-type genes; 60 atypical N-type genes identified.	[5]
Oryza sativa (Rice)	505-508	275 (inferred)	0	0	Complete absence of TNL subfamily, a hallmark of monocots.	[11] [6]
Oryza australiensis	Not Specified	Present	0	0	Confirms TNL loss in Poaceae; genome is a source of novel R genes.	[13]
12 Rosaceae Species	2188 (total)	Variable	Variable	Variable	Displayed independent "expansion" and "contraction" patterns.	[6]

The evolutionary patterns of NBS-LRR genes are highly dynamic. Studies have identified several distinct patterns across plant families, including "consistent expansion" (e.g., potato), "expansion followed by contraction" (e.g., tomato), and "shrinking" (e.g., pepper) [6]. A notable finding is the complete absence of TNL genes in monocots like rice, wheat, and maize, while they are prevalent in dicots like Arabidopsis thaliana [11] [6] [3]. Furthermore, comparative analysis within the Salvia genus revealed a dramatic reduction in TNL and RNL members, suggesting lineage-specific degeneration of these subfamilies [11]. These distribution patterns highlight the fluid and adaptive nature of the NBS-LRR gene family.

Experimental Protocols for Identification and Classification

Accurate identification and classification of NBS-LRR genes are fundamental to their characterization. The following protocols are widely used in the field.

Genome-Wide Identification Pipeline

The standard workflow for identifying NBS-LRR genes from a sequenced genome involves a combination of domain-based searches and manual curation [11] [6] [5].

• HMMER Search: Perform a search against the proteome of the target species using the Hidden Markov Model (HMM) profile for the NB-ARC domain (PF00931) from the Pfam database. A typical E-value cutoff is 1.0 or more stringent (e.g., 1*10^-20) [5] [3].
• BLAST Search: Conduct a complementary BLASTp search using a set of known NBS-LRR protein sequences as queries, with an E-value threshold of 1.0 [6].
• Merge and Remove Redundancy: Combine the results from both searches and remove duplicate entries.
• Domain Verification: Submit the candidate sequences to domain analysis tools such as PfamScan, SMART, or NCBI's Conserved Domain Database (CDD) to confirm the presence of the NBS domain and identify N-terminal (TIR, CC, RPW8) and C-terminal (LRR) domains. This step classifies the genes into CNL, TNL, RNL, or atypical categories [6] [5].

Phylogenetic and Motif Analysis

For evolutionary and structural insights:

• Multiple Sequence Alignment: Align the full-length protein sequences or the NBS domains using tools like ClustalW or MAFFT [5].
• Phylogenetic Tree Construction: Construct a phylogenetic tree using Maximum Likelihood or Neighbor-Joining methods implemented in software like MEGA. Bootstrap analysis (e.g., 1000 replicates) should be used to assess node support [11] [5].
• Conserved Motif Analysis: Analyze the protein sequences for conserved motifs using the MEME suite. Typically, 10 conserved motifs are identified within the NBS domain, which correspond to classic subdomains like P-loop, RNBS-A, RNBS-B, etc. [6] [5].

Functional Mechanisms and Signaling Pathways

The CNL, TNL, and RNL subfamilies activate plant immunity through interconnected but distinct signaling pathways. The following diagram details the core mechanisms of this process, from pathogen recognition to the activation of defense responses.

As illustrated, the core mechanism involves a conserved "switch" model upon pathogen perception. The NBS domain undergoes a conformational change from an ADP-bound (inactive) state to an ATP-bound (active) state, which triggers downstream signaling [5]. TNL proteins typically rely on the EDS1-PAD4 signaling node, while CNL proteins often activate pathways through CC-domain interactions and can be assisted by RNL "helper" proteins like ADR1, which amplify the immune signal [11] [6]. Both pathways ultimately lead to the activation of defense responses, including the hypersensitive response (HR), a form of programmed cell death that confines the pathogen at the infection site [11] [4].

Advancing research in NBS-LRR gene characterization relies on a suite of bioinformatic and experimental tools.

Table 2: Essential Resources for NBS-LRR Gene Research

Resource / Tool Name	Type	Primary Function in NBS-LRR Research	Key Features / Applications
PRGminer	Bioinformatics Tool	High-throughput prediction and classification of plant resistance genes from protein sequences.	Uses deep learning (CNN); achieves >95% accuracy; classifies into 8 R-gene classes. [12]
HMMER (NB-ARC PF00931)	Algorithm / Profile	Identification of candidate NBS-LRR genes from genomic or proteomic data.	Foundation of most identification pipelines; uses Hidden Markov Models for domain detection. [11] [5]
MEME Suite	Bioinformatics Tool	Discovery of conserved protein motifs in NBS-LRR sequences.	Identifies structural motifs like P-loop, RNBS-A, etc.; helps define subfamily characteristics. [6] [5]
Phytozome / Ensemble Plants	Database	Source of genomic data and annotated protein sequences for comparative analysis.	Provides high-quality genome assemblies for a wide range of plant species. [12]
Virus-Induced Gene Silencing (VIGS)	Experimental Method	Functional validation of NBS-LRR genes through transient knock-down.	Used to demonstrate the role of specific NBS genes in disease resistance. [14]
OrthoFinder	Bioinformatics Tool	Evolutionary analysis and grouping of NBS genes into orthogroups across species.	Identifies core, species-specific, and rapidly evolving NBS gene lineages. [14]
Pfam & CDD	Database	Domain annotation and verification of predicted NBS-LRR genes.	Critical for classifying genes into CNL, TNL, RNL, and atypical types. [6] [5]

The systematic classification of NBS genes into CNL, TNL, and RNL subfamilies provides an indispensable framework for benchmarking novel resistance genes. This comparative guide underscores that these subfamilies are defined by non-interchangeable structural domains, follow distinct signaling pathways, and exhibit dynamic evolutionary patterns across the plant kingdom. Future research leveraging the outlined experimental protocols and toolkit will continue to unravel the complexity of this gene family. Integrating this knowledge with advanced genome engineering and breeding techniques will accelerate the development of crops with durable and broad-spectrum disease resistance, a critical goal for global food security.

Within the broader context of benchmarking novel nucleotide-binding site (NBS) genes against known resistance genes, understanding their genomic distribution is fundamental. The organization of these genes—whether clustered in tandem arrays or dispersed as singletons—provides critical insights into their evolutionary dynamics and functional potential. This guide objectively compares these distinct organizational patterns across plant species, synthesizing empirical data on their prevalence, structural characteristics, and experimental approaches for their identification. Such comparative analysis is essential for researchers aiming to isolate novel R-genes and understand the evolutionary mechanisms that shape plant immune systems.

Quantitative Comparison of Cluster and Singleton Organization

The genomic arrangement of NBS-encoding genes varies significantly across plant species, influenced by evolutionary history and selective pressures. The table below summarizes the distribution of clustered versus singleton NBS genes from recent genome-wide studies.

Table 1: Comparative Genomic Distribution of NBS-LRR Genes Across Plant Species

Plant Species	Total NBS Genes	Clustered Genes	Singleton Genes	Key Distribution Features	Primary Duplication Type
Akebia trifoliata [15]	73	41 (56%)	23 (32%)	Uneven distribution, mostly at chromosome ends	Tandem and dispersed duplications
Capsicum annuum (Pepper) [16]	252	136 (54%)	116 (46%)	47 clusters; Chromosome 3 has highest density (38 genes)	Tandem duplications and genomic rearrangements
Dioscorea rotundata (Yam) [17]	167	124 (74%)	43 (26%)	25 multigene clusters; No TNL genes detected	Tandem duplication
Asparagus officinalis [18]	27	Information Missing	Information Missing	Marked contraction compared to wild relatives	Information Missing
Sorghum bicolor [19]	88	Highly clustered	Information Missing	Clustering mainly due to local duplications	Local duplications
Asparagus setaceus (Wild) [18]	63	Information Missing	Information Missing	Expanded repertoire compared to cultivated relative	Information Missing

The data reveals that clustered organization is the dominant pattern across species, with cluster rates ranging from 54% to 74% of all NBS genes. This prevalence underscores the importance of tandem duplication and local recombination events in generating diversity within this gene family. The exceptional case of Dioscorea rotundata, which completely lacks TNL-type genes, illustrates how lineage-specific evolutionary paths can dramatically reshape the R-gene repertoire [17]. Furthermore, the contraction observed in domesticated Asparagus officinalis compared to its wild relatives suggests that artificial selection during cultivation may reduce NBS gene diversity, potentially impacting disease resistance [18].

Experimental Protocols for Identification and Classification

Genome-Wide Identification Pipeline

A standardized bioinformatics workflow has emerged for the comprehensive identification and classification of NBS-LRR genes in plant genomes. The following diagram illustrates this multi-step process, which integrates sequence similarity searches and domain-based validation:

Figure 1: Workflow for NBS-LRR Gene Identification and Classification

The process begins with HMMER searches using the conserved NB-ARC domain (Pfam: PF00931) as query, followed by BLAST analysis against reference NBS proteins from model organisms like Arabidopsis thaliana [15] [18]. Candidate sequences identified through both methods are merged and deduplicated. These are then subjected to domain validation using tools like InterProScan and NCBI's Conserved Domain Database (CDD) to confirm the presence of characteristic NBS domains [19]. Classification into subfamilies (CNL, TNL, RNL) is performed by identifying N-terminal domains (CC, TIR, or RPW8) using Pfam and coiled-coil prediction tools like COILS or nCoil [15] [16]. Finally, genomic distribution analysis maps the physical locations to identify cluster arrangements, typically defined as multiple NBS genes separated by fewer than eight non-NBS genes [19].

Advanced Deep Learning Approaches

Beyond traditional homology-based methods, deep learning approaches now offer complementary tools for R-gene discovery. PRGminer represents this new paradigm, employing a two-phase prediction system: Phase I distinguishes R-genes from non-R-genes using dipeptide composition features, achieving 95.72% accuracy on independent testing; Phase II classifies the predicted R-genes into eight structural classes including CNL, TNL, KIN, RLP, LECRK, RLK, LYK, and TIR [20]. This method is particularly valuable for identifying divergent R-genes with low sequence homology to known references, expanding our capacity to discover novel resistance genes that might be missed by conventional BLAST-based approaches.

Structural and Functional Implications of Organizational Patterns

Molecular Characteristics of Clustered versus Singleton Genes

The organizational status of NBS genes (clustered versus singleton) correlates with distinct structural and functional properties. The table below compares key characteristics of these organizational patterns:

Table 2: Structural and Functional Characteristics of Cluster vs. Singleton Organizations

Characteristic	Clustered NBS Genes	Singleton NBS Genes
Evolutionary Mechanism	Primarily tandem duplications [15] [17]	Dispersed duplications, segmental duplications [15]
Sequence Diversity	High diversity due to frequent recombination [19]	Lower diversity, higher conservation [17]
Expression Patterns	Often tissue-specific or developmentally regulated [15]	More constitutive expression patterns [17]
Functional Attributes	Rapid evolution of novel pathogen specificities [19]	Conservation of ancestral resistance functions [17]
Conserved Motifs	All eight conserved NBS motifs present [15]	Same conserved motifs but potentially different selection pressures
Response to Selection	Diversifying selection for new recognition capabilities	Purifying selection to maintain existing functions

Clustered NBS genes exhibit distinct evolutionary dynamics compared to singletons. They display accelerated evolution through frequent sequence exchanges, unequal crossing over, and gene conversion events, creating diversity for pathogen recognition [19]. This is particularly evident in "mixed clusters" containing genes from different phylogenetic clades, which facilitate the generation of novel resistance specificities through modular evolution [19]. In pepper genomes, clusters vary from homogeneous (containing genes from the same subfamily) to heterogeneous (containing genes from different subfamilies like CN, NL, and N), suggesting different evolutionary trajectories and potential functional cooperation [16].

Singleton NBS genes often represent more ancient, conserved lineages maintained across species. In Dioscorea rotundata, phylogenetic analysis revealed a conservatively evolved ancestral lineage orthologous to the Arabidopsis RPM1 gene, suggesting maintenance of critical immune functions [17]. These genes typically experience purifying selection that preserves their specific resistance capabilities across evolutionary timescales, in contrast to the diversifying selection observed in clustered genes.

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagents and Computational Tools for NBS Gene Analysis

Reagent/Tool	Specific Application	Function/Utility
HMMER Suite [15] [18]	NB-ARC domain identification	Detects conserved NBS domains using hidden Markov models
InterProScan [19]	Multi-domain architecture analysis	Integrates multiple databases for comprehensive domain annotation
MEME Suite [15] [18]	Conserved motif discovery	Identifies conserved sequence motifs in NBS domains
PRGminer [20]	Deep learning-based R-gene prediction	Classifies R-genes using dipeptide composition features
OrthoFinder [18]	Orthologous group inference	Identifies conserved NBS genes across related species
BEDTools [18]	Genomic interval analysis	Determines physical clustering and gene arrangements
PlantCARE [18]	Promoter cis-element analysis	Identifies defense-related regulatory elements in promoters
Scopolamine butylbromide	Scopolamine butylbromide, CAS:149-64-4, MF:C21H30BrNO4, MW:440.4 g/mol	Chemical Reagent
Decamethonium chloride	Decamethonium chloride, CAS:3198-38-7, MF:C16H38Cl2N2, MW:329.4 g/mol	Chemical Reagent

This toolkit enables researchers to progress from initial genome mining to functional characterization of NBS genes. The combination of traditional homology-based tools with emerging deep learning approaches like PRGminer provides complementary strengths for comprehensive R-gene annotation [20]. Integration of multiple tools is often necessary to overcome annotation challenges posed by the repetitive nature and complex genomic structure of NBS gene clusters.

Evolutionary Dynamics and Breeding Implications

The evolutionary forces shaping NBS gene organization have direct implications for crop improvement strategies. The following diagram illustrates the evolutionary trajectory and breeding potential of NBS genes:

Figure 2: Evolutionary and Breeding Pathway of NBS Genes

Comparative genomics reveals that NBS gene clusters serve as hotbeds for rapid evolution of pathogen recognition capabilities. The high density of similar sequences in clusters facilitates ectopic recombination, gene conversion, and unequal crossing over, generating novel resistance specificities through the shuffling of existing genetic variation [19]. This dynamic is evidenced by the prevalence of mixed clusters containing phylogenetically divergent NBS genes, which create opportunities for modular evolution of recognition specificities [19].

Domestication has significantly impacted NBS gene repertoires, as illustrated by the contrast between cultivated Asparagus officinalis (27 NLR genes) and its wild relative A. setaceus (63 NLR genes) [18]. This domestication-associated contraction of the NLR repertoire, coupled with reduced expression of retained genes following pathogen challenge, likely contributes to increased disease susceptibility in cultivated varieties. Orthologous analysis identified only 16 conserved NLR gene pairs between these species, highlighting which genes were preserved during domestication [18].

For breeding applications, clustering patterns provide valuable genomic signatures for marker development and gene pyramiding strategies. Chromosomal regions with high NBS gene density represent priority targets for introgression of broad-spectrum resistance. Furthermore, the identification of conservatively evolved singleton genes orthologous to known resistance genes (like the RPM1 ortholog in yam) enables targeted conservation of ancestral resistance functions in breeding programs [17].

Gene duplication is a fundamental mechanism for generating genetic novelty and driving evolutionary innovation. Within the broader context of benchmarking novel nucleotide-binding site (NBS) genes against known resistance genes, understanding the distinct evolutionary dynamics of tandem and dispersed duplication events becomes paramount. These duplication mechanisms create the raw genetic material upon which natural selection acts, enabling the functional diversification of gene families critical for plant immunity, including disease-resistance (R) genes [21]. This guide provides a comparative analysis of tandem and dispersed duplication events, focusing on their identification, functional consequences, and implications for the evolution of plant resistance genes, particularly within the NBS-LRR family.

Mechanistic and Functional Comparison of Duplication Types

Tandem duplications occur when genes are duplicated in close proximity on the same chromosome, often through unequal crossing over during recombination. In contrast, dispersed duplications involve the insertion of duplicated gene copies to different genomic locations, frequently via retrotransposition or DNA-based transposition [22] [23]. The distinct mechanisms of origin predispose these duplicates to different evolutionary fates and functional roles.

Table 1: Key Characteristics of Tandem and Dispersed Duplication Events

Feature	Tandem Duplication	Dispersed Duplication
Genomic Arrangement	Clustered, adjacent genes on the same chromosome [23]	Scattered copies across different genomic locations [22]
Primary Mechanism	Unequal crossing over, replication errors [23]	Retrotransposition, DNA transposon activity [22]
Regulatory Context	Often share regulatory elements [23]	Subject to new regulatory environments [22]
Common Evolutionary Fates	Dosage amplification, subfunctionalization, neofunctionalization [23]	Neofunctionalization, pseudogenization [23]
Prevalence in NBS-LRR Genes	Very high; leads to gene clusters [21] [24]	Less common compared to tandem duplication [21]
Role in Evolutionary Conflict	Fuels arms races via rapid, localized expansion [22]	Facilitates functional partitioning and resolution of trade-offs [22]

The following diagram illustrates the primary mechanisms and initial functional consequences of tandem and dispersed gene duplication events.

Quantitative Analysis of Duplication Patterns in Plant Genomes

Genome-wide studies reveal the significant impact of duplication events on the architecture of plant genomes, particularly for disease-resistance gene families. A comparative analysis of 34 plant species, from mosses to monocots and dicots, identified 12,820 NBS-domain-containing genes, which were classified into 168 distinct domain architecture classes [21]. This diversity encompasses both classical patterns (e.g., NBS, NBS-LRR, TIR-NBS-LRR) and species-specific structural patterns, underscoring the extensive diversification driven by duplication events [21].

Orthogroup (OG) analysis of these genes revealed 603 core and species-specific orthogroups, with evidence of tandem duplications playing a major role in their expansion and diversification [21]. Expression profiling demonstrated that specific OGs (e.g., OG2, OG6, OG15) were upregulated in various tissues under biotic and abiotic stresses, linking duplication-driven diversification to functional adaptation in defense responses [21].

Table 2: Genomic and Functional Impact of Duplication Events in Plant R Genes

Metric	Findings	Experimental Support
Total NBS Genes Identified	12,820 genes across 34 species [21]	Genome-wide comparative analysis [21]
Domain Architecture Classes	168 classes identified [21]	Domain architecture pattern analysis [21]
Orthogroups (OGs)	603 OGs with core and unique groups [21]	Orthologous group analysis [21]
Role in Disease Resistance	Putative upregulation under biotic/abiotic stress [21]	Expression profiling in tolerant/susceptible plants [21]
Genetic Variation	6,583 unique variants in tolerant cotton accession [21]	Genetic variation analysis in Gossypium hirsutum [21]
Functional Validation	VIGS of GaNBS (OG2) demonstrated role in virus tittering [21]	Virus-Induced Gene Silencing (VIGS) [21]

Experimental Protocols for Identifying and Validating Duplication Events

Protocol 1: Identification of Segmental Duplications with SegMantX

Purpose: To detect diverged segmental duplications in genomic sequences, which are challenging to identify with standard alignment tools due to sequence amelioration [25].

• Input Preparation: Obtain the genomic sequence of interest (e.g., a plant chromosome or plasmid) in FASTA format.
• Self-Similarity Search: Perform an all-vs-all sequence alignment of the genome against itself using a local alignment tool like BLASTn. This generates initial seeds of local similarity hits.
• Alignment Chaining: Process the BLASTn output with SegMantX. The algorithm calculates a scaled gap metric (Di,j = (li + lj)/gi,j) between consecutive local alignments, where li and lj are the lengths of alignment hits and gi,j is the gap between them [25].
• Parameter Setting: Apply thresholds for maximum gap length (m) and maximum scaled gap (s) to control the chaining process. This avoids chaining short, widely spaced alignments common in repetitive regions.
• Output Analysis: SegMantX outputs chained segments representing putative segmental duplications. The output can be visualized to show the syntenic arrangement of duplicated regions and their sequence similarity [25].

Protocol 2: Functional Validation Using Virus-Induced Gene Silencing (VIGS)

Purpose: To rapidly assess the function of a candidate NBS gene, identified through duplication analysis, in plant disease resistance [21].

• Candidate Gene Selection: Select a target NBS gene (e.g., GaNBS from orthogroup OG2) based on duplication, expression, or genetic variation data [21].
• VIGS Vector Construction: Clone a ~200-500 bp fragment of the target gene into a VIGS vector (e.g., based on Tobacco Rattle Virus).
• Plant Inoculation: Introduce the recombinant VIGS vector into susceptible but not yet symptomatic plants. This is typically done by agrobacterium-mediated infiltration or in vitro transcription followed by rubbing.
• Phenotypic Assessment: Challenge the silenced plants with the pathogen of interest (e.g., cotton leaf curl disease virus). Monitor disease symptoms and progression over time.
• Molecular Confirmation: Quantify viral titer in silenced plants compared to control plants using qPCR or digital PCR. Confirm the reduction of the target NBS gene transcript levels via RT-qPCR to verify silencing efficiency [21].

Visualization of Research Workflows

The following diagram outlines the integrated workflow from the initial identification of gene duplications to the functional validation of candidate genes, providing a roadmap for researchers in the field.

Table 3: Key Reagents and Tools for Studying Gene Duplication and NBS Gene Function

Reagent/Tool	Function/Application	Example/Reference
SegMantX	Bioinformatics tool for detecting diverged segmental duplications via local alignment chaining [25]	[25]
VIGS Vectors	Viral vectors for transient gene silencing in plants; allows rapid functional screening [21]	Tobacco Rattle Virus (TRV)-based vectors [21]
BLAST Suite	Standard tool for initial sequence similarity searches and identification of paralogous genes [25]	BLASTn, tBLASTn [25]
Orthogroup Analysis	Framework for classifying genes into orthologous groups across species, identifying core and lineage-specific duplications [21]	OrthoFinder, OrthoMCL [21]
Near-Isogenic Lines (NILs)	Plant lines that are genetically identical except for a small chromosomal segment containing the R gene of interest; crucial for map-based cloning [24]	Used in cloning Sr33 and Lr22a [24]
BAC Libraries	Large-insert DNA libraries used for physical mapping and sequencing of targeted genomic regions [24]	Used in cloning Sr33 [24]

The Role of NBS Genes in Effector-Triggered Immunity (ETI)

Effector-Triggered Immunity (ETI) represents a sophisticated plant defense system wherein nucleotide-binding site (NBS) leucine-rich repeat (LRR) genes play the central role in pathogen recognition and immune activation. These genes encode intracellular immune receptors that detect specific pathogen effector proteins, initiating a robust defense response that typically culminates in programmed cell death to restrict pathogen spread [26]. The NBS gene family constitutes one of the largest and most variable resistance (R) gene families in plants, with significant implications for developing durable disease resistance in crops [14]. Understanding the diversity, evolution, and functional mechanisms of NBS genes provides a critical foundation for benchmarking novel NBS genes against established resistance genes, enabling more strategic approaches to crop improvement and disease management.

The structural architecture of NBS-LRR proteins reveals their functional specialization. These proteins typically contain three fundamental components: an N-terminal signaling domain (either TIR or CC), a central NB-ARC nucleotide-binding adaptor domain, and a C-terminal LRR domain responsible for pathogen recognition [14] [26]. This modular design allows these proteins to operate as molecular switches, transitioning between ADP-bound (inactive) and ATP-bound (active) states to regulate immune signaling [26]. The remarkable diversity of NBS genes across plant species, coupled with their rapid evolution, presents both challenges and opportunities for researchers aiming to harness their potential for crop protection.

NBS Gene Diversity and Evolution Across Plant Species

Genomic Landscape and Structural Classification

NBS genes exhibit extraordinary diversity across the plant kingdom, with recent studies identifying 12,820 NBS-domain-containing genes across 34 species ranging from mosses to monocots and dicots [14]. These genes display significant structural variation, classified into 168 distinct domain architecture patterns encompassing both classical configurations (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) [14]. This diversity reflects continuous evolutionary adaptation to changing pathogen pressures.

Table 1: NBS Gene Distribution Across Selected Plant Species

Plant Species	Family/Type	Total NLR Genes	CNL	TNL	RNL	Reference
Arabidopsis thaliana	Brassicaceae	~165	~150	~12	~3	[27]
Brassica napus	Brassicaceae/Oilseed crop	~464	Not specified	Not specified	Not specified	[27]
Salvia miltiorrhiza	Medicinal plant	196	Majority	Markedly reduced	Markedly reduced	[28]
Asparagus officinalis	Horticultural crop	27	Not specified	Not specified	Not specified	[18]
Asparagus setaceus	Wild relative	63	Not specified	Not specified	Not specified	[18]
Solanum lycopersicum	Solanaceae	363	231	132	Not specified	[29]

Comparative genomic analyses reveal striking patterns in NLR repertoire evolution. Notably, wild species typically maintain expanded NLR repertoires compared to their domesticated counterparts. In the Asparagus genus, researchers identified 63, 47, and 27 NLR genes in A. setaceus, A. kiusianus, and domesticated A. officinalis, respectively, demonstrating a marked contraction of the NLR gene family during domestication [18]. This reduction likely contributes to the increased disease susceptibility observed in cultivated varieties, suggesting that artificial selection for yield and quality traits may have inadvertently compromised immune function.

Evolution and Selection Pressures

NBS genes evolve through diverse mechanisms including whole-genome duplication (WGD) and small-scale duplications (SSD) such as tandem, segmental, and transposon-mediated duplications [14]. These genes are often organized in clusters of tandemly duplicated genes, although they can also appear as singular loci dispersed throughout the genome [29]. This genomic arrangement facilitates rapid evolution and generation of novel recognition specificities through recombination and diversifying selection.

Orthogroup analysis across multiple plant species has identified 603 orthogroups (OGs), with some core orthogroups (OG0, OG1, OG2) being widely conserved and others (OG80, OG82) representing highly species-specific innovations [14]. Expression profiling has demonstrated that certain core orthogroups (OG2, OG6, OG15) show putative upregulation across different tissues under various biotic and abiotic stresses, suggesting their fundamental importance in plant immunity [14]. The continuous evolutionary arms race between plants and pathogens drives this diversification, with pathogen effectors evolving to evade recognition while plant NLRs evolve new detection capabilities.

Methodological Framework for NBS Gene Identification and Analysis

Genome-Wide Identification Approaches

Accurate identification of NBS genes presents significant challenges due to their complex genomic organization, low expression levels, and sequence similarity to repetitive elements [29]. Conventional protein motif/domain-based search (PDS) methods often prove imprecise, as repeat masking prior to automatic genome annotation frequently prevents comprehensive NBS gene detection [29]. To address these limitations, researchers have developed specialized bioinformatic pipelines:

• Full-length Homology-based R-gene Prediction (HRP): This method employs a two-level homology search, first using protein domains to identify an initial set of R-genes in automated gene predictions, then using these R-genes for full-length homology searches in the genome assembly [29]. When tested on the tomato genome, HRP identified 363 NB-LRR genes, outperforming the manually curated RenSeq method which identified 326 genes [29].

• Integrated HMM and BLAST Approach: A comprehensive strategy combining Hidden Markov Model (HMM) searches using the conserved NB-ARC domain (Pfam: PF00931) with local BLASTp analyses against reference NLR protein sequences, applying a stringent E-value cutoff of 1e-10 [18]. Candidate sequences identified through both methods are validated through domain architecture analysis using InterProScan and NCBI's Batch CD-Search.

• Orthogroup Analysis: Using tools like OrthoFinder v2.5.1 with DIAMOND for fast sequence similarity searches and the MCL clustering algorithm for grouping sequences into orthogroups [14]. This approach facilitates evolutionary studies and comparative analysis of NBS genes across multiple species.

Expression and Functional Validation

Following identification, NBS genes require functional validation to confirm their role in immunity. Key experimental approaches include:

• Expression Profiling: Analyzing transcriptomic data from RNA-seq databases (e.g., IPF database, CottonFGD, Cottongen) under various conditions including tissue-specific expression, abiotic stress, and biotic stress challenges [14]. The Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values are categorized to identify stress-responsive NBS genes.

• Virus-Induced Gene Silencing (VIGS): Silencing candidate NBS genes in resistant plants to demonstrate their functional role in disease resistance. For example, silencing of GaNBS (OG2) in resistant cotton demonstrated its putative role in reducing virus titers [14].

• Transgenic Complementation: Overexpressing candidate NBS genes in susceptible plants to confer resistance. For instance, overexpression of GmTNL16 (Glyma.16G135500) in soybean hairy roots significantly reduced Phytophthora sojae biomass compared to controls [30].

• Protein Interaction Studies: Conducting protein-ligand and protein-protein interaction assays to demonstrate direct binding between NBS proteins and pathogen effectors or host proteins. Studies have shown strong interaction of some putative NBS proteins with ADP/ATP and different core proteins of the cotton leaf curl disease virus [14].

Figure 1: Experimental workflow for comprehensive NBS gene identification and functional validation, integrating bioinformatic and experimental approaches.

NBS Gene Activation and Immune Signaling Mechanisms

Pathogen Recognition Strategies

NBS-LRR proteins employ sophisticated strategies for pathogen detection, primarily through two distinct mechanisms:

• Direct Recognition: Some NBS-LRR proteins physically bind pathogen effector proteins. For example, the rice Pi-ta protein directly interacts with the effector AVR-Pita from Magnaporthe grisea, while flax L proteins bind directly to AvrL567 effectors from flax rust fungus [26]. These direct interactions typically involve the LRR domain of the NBS-LRR protein, which forms a binding surface for effector recognition.

• Indirect Recognition (Guard Model): Many NBS-LRR proteins detect pathogen effectors indirectly by monitoring the status of host proteins that are modified by effectors. The Arabidopsis RPM1 protein detects Pseudomonas syringae effectors AvrRpm1 and AvrB through their modification of the host protein RIN4, while RPS5 detects the protease AvrPphB through its cleavage of the host kinase PBS1 [26]. This indirect strategy allows plants to monitor a limited number of key host targets rather than evolving specific receptors for each rapidly evolving effector.

Signaling Activation and Immune Output

Upon pathogen recognition, NBS-LRR proteins undergo conformational changes that trigger immune signaling. The current model suggests that association with either a modified host protein or a pathogen protein leads to conformational alterations in the amino-terminal and LRR domains, promoting the exchange of ADP for ATP by the NBS domain [26]. This nucleotide exchange activates downstream signaling through mechanisms that remain incompletely understood but typically result in a hypersensitive response (HR) and systemic acquired resistance (SAR).

Table 2: Experimentally Validated NBS-Mediated ETI Responses

NBS Gene	Plant Species	Pathogen Effector	Pathogen	Recognition Mechanism	Validation Method
GmTNL16	Soybean	Unknown	Phytophthora sojae	Regulated by gma-miR1510	Overexpression, miRNA knockdown [30]
RPS5	Arabidopsis	AvrPphB	Pseudomonas syringae	Indirect (via PBS1 cleavage)	Genetic analysis, interaction studies [26]
RPM1	Arabidopsis	AvrRpm1, AvrB	Pseudomonas syringae	Indirect (via RIN4 modification)	Genetic analysis, interaction studies [26]
Pi-ta	Rice	AVR-Pita	Magnaporthe grisea	Direct binding	Yeast two-hybrid [26]
RRS1	Arabidopsis	PopP2	Ralstonia solanacearum	Direct binding	Split-ubiquitin yeast two-hybrid [26]
Sentinel	Engineered	Various	Various	Engineered endophyte system	OxyR regulatory circuit [31]

Recent research has revealed that NBS gene function is regulated at multiple levels, including transcriptional and post-transcriptional mechanisms. MicroRNAs have been identified that target the nucleotide sequences encoding conserved motifs within NLRs, including the P-loop, providing an additional layer of regulation that may enable plant species to maintain extensive NLR repertoires without exhausting functional NLR loci [14]. For example, in soybean, gma-miR1510 regulates Glyma.16G135500 (GmTNL16), with miR1510 expression reduced upon P. sojae infection, reflecting induced expression of GmTNL16 that confers resistance [30].

Figure 2: NBS-mediated Effector-Triggered Immunity signaling pathways showing direct and indirect pathogen recognition mechanisms.

Comparative ETI Landscapes Across Plant Species

Conservation and Divergence of Immune Responses

Systematic analysis of ETI conservation across plant species reveals both qualitative and quantitative patterns in immune response preservation. Research comparing Arabidopsis thaliana with two closely related oilseed crops, Brassica napus (canola) and Camelina sativa (false flax), demonstrated that 15 of 19 (79%) and 18 of 19 (95%) ETI responses were conserved in B. napus and C. sativa, respectively [27]. The level of immune conservation was inversely related to evolutionary divergence from A. thaliana, with the more closely related C. satina losing ETI responses to only one effector family, while the more distantly related B. napus lost responses to four effector families [27].

Notably, while qualitative conservation (presence/absence of response) was largely maintained, quantitative aspects (strength of response) showed greater variation. The rank order of immune response strength was not well-maintained across species and diverged increasingly with evolutionary distance from A. thaliana [27]. This suggests that while core ETI functionality persists, its regulation and magnitude have undergone species-specific adaptation.

Domesticated vs. Wild Species Comparison

Comparative analyses between domesticated crops and their wild relatives reveal significant impacts of artificial selection on NBS gene repertoires and function. In the Asparagus genus, domesticated A. officinalis possesses only 27 NLR genes compared to 63 and 47 in wild relatives A. setaceus and A. kiusianus, respectively [18]. This represents a striking 57-77% reduction in NLR gene count during domestication.

Pathogen inoculation assays demonstrate functional consequences of this genetic erosion: domesticated A. officinalis was susceptible to Phomopsis asparagi infection, while A. setaceus remained asymptomatic [18]. Transcriptomic analysis revealed that the majority of preserved NLR genes in A. officinalis showed either unchanged or downregulated expression following fungal challenge, indicating potential functional impairment in disease resistance mechanisms [18]. These findings suggest that artificial selection for yield and quality traits has inadvertently compromised immune function in cultivated species.

Emerging Applications and Research Tools

Engineering Novel Disease Resistance Strategies

Recent advances in understanding NBS gene function have enabled innovative approaches to engineering disease resistance:

• Engineered Sentinel Endophytes: Researchers have genetically engineered plant endophytes, termed "Sentinels," to heterologously express effectors that are recognized by the host's corresponding NLR [31]. Using an OxyR regulatory circuit, effector expression is activated by reactive oxygen species—a common signal during pathogen infection. This system enables ETI activation against pathogens lacking recognizable effectors, effectively broadening the spectrum of effector-triggered immunity [31].

• MicroRNA Regulation: Manipulation of microRNAs that target NBS genes presents another strategy for modulating plant immunity. The identification of gma-miR1510 regulation of GmTNL16 in soybean provides a proof-of-concept for this approach [30]. Knockdown of miR1510 using short tandem target mimic technology enhanced resistance to Phytophthora sojae, demonstrating the potential of miRNA manipulation for crop protection.

• HRP-Based Gene Discovery: The full-length Homology-based R-gene Prediction (HRP) method enables more comprehensive identification of NBS genes in plant genomes [29]. This approach has proven particularly valuable for R-gene allele mining, as demonstrated by the identification of previously undiscovered Fom-2 homologs in five Cucurbita species, facilitating development of improved cultivars with enhanced disease resistance.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents and Resources for NBS Gene Studies

Reagent/Resource	Function/Application	Example Use Case	Reference
OrthoFinder v2.5.1	Orthogroup analysis and evolutionary studies	Clustering of NBS genes into orthogroups across species	[14]
HRP Pipeline	Comprehensive R-gene identification	Full-length NB-LRR gene prediction in tomato and Beta species	[29]
VIGS Systems	Functional validation through gene silencing	Silencing of GaNBS (OG2) in resistant cotton	[14]
InterProScan & NCBI CD-Search	Domain architecture validation	Identification of NB-ARC and associated domains	[18]
Sentinel Endophyte System	Engineered resistance via modified microbiota	Broad-spectrum ETI activation in various plants	[31]
STTM Technology	MicroRNA knockdown for gene regulation	Inhibition of gma-miR1510 to enhance GmTNL16 expression	[30]
PlantCARE Database	cis-element prediction in promoter regions	Identification of defense-related regulatory elements	[18]
Erythromycin A enol ether	Erythromycin A enol ether, CAS:33396-29-1, MF:C37H65NO12, MW:715.9 g/mol	Chemical Reagent	Bench Chemicals
N-Boc-N-methylethylenediamine	N-Boc-N-methylethylenediamine, CAS:121492-06-6; 202207-78-1; 548-73-2, MF:C8H18N2O2, MW:174.244	Chemical Reagent	Bench Chemicals

The comprehensive analysis of NBS genes in effector-triggered immunity reveals both the remarkable conservation of core mechanisms and the dynamic evolution that generates species-specific diversity. Effective benchmarking of novel NBS genes against established resistance genes requires multidimensional assessment including genomic context, evolutionary history, expression patterns, and functional validation. The methodologies and frameworks presented here provide a roadmap for systematic evaluation of NBS gene candidates.

Future directions in NBS gene research will likely focus on harnessing natural diversity through cross-species comparative genomics, engineering expanded recognition specificities through synthetic biology approaches, and developing strategies for durable resistance that anticipates pathogen evolution. The integration of advanced bioinformatic tools with high-throughput functional validation platforms will accelerate the identification and deployment of effective R genes in crop improvement programs. As our understanding of NBS gene regulation and signaling mechanisms deepens, so too will our ability to design precisely tuned immune responses that provide robust disease resistance without compromising plant growth and productivity.

Diversity and Presence-Absence Variation in Plant Genomes

In plant genomes, Presence-Absence Variation (PAV) describes a phenomenon where specific genomic sequences, including entire genes, are present in some individuals but entirely absent from others within a species [32]. This form of structural variation is a significant driver of phenotypic diversity and adaptation. Notably, PAVs are frequently enriched in genes associated with environmental responses, particularly in disease resistance gene families such as the Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) genes [32] [33]. The NBS-LRR family is the largest class of known plant resistance (R) proteins, serving as critical guards that detect diverse pathogens including bacteria, fungi, viruses, and oomycetes [34]. These proteins function as intracellular immune receptors, often monitoring the status of host proteins that are targeted by pathogen effectors [34]. Benchmarking newly identified NBS genes against well-characterized R genes is therefore essential for understanding the complete landscape of disease resistance in plants and for identifying novel genes with potential applications in crop improvement and drug development.

Quantitative Benchmarking of PAV and NBS-LRR Genes Across Species

The scale and impact of PAV and NBS-LRR diversity can be quantified through genome-wide analyses. The table below summarizes key quantitative findings from recent studies in various plant species, providing a benchmark for evaluating novel gene discoveries.

Table 1: Genome-Wide Studies of PAV and NBS-LRR Genes

Plant Species	Scale of Analysis	Key Quantitative Findings	Reference
Peach (Prunus persica)	100 accessions	Identified 2.52 Mb of non-reference sequences and 923 novel genes via PAV. PAV-based GWAS mapped loci for traits like petiole length and chilling requirements.	[32]
Wild Tomato (Solanum pimpinellifolium)	Genome-wide	Identified 245 NBS-LRR genes. ~59.6% reside in gene clusters, mostly via tandem duplications. Uneven distribution across 12 chromosomes.	[35]
Akebia trifoliata	Genome-wide	Found only 73 NBS genes (50 CNL, 19 TNL, 4 RNL). 64 mapped genes unevenly distributed; 41 in clusters, 23 as singletons.	[36]
Rice (Oryza sativa)	Elite restorer lines	Characterized a PAV at the Se locus containing two complementary genes (ORF3, ORF4) that cause hybrid sterility, acting as a reproductive barrier between indica and japonica subspecies.	[33]
Mango (Mangifera indica)	16 isolated RGAs	Nucleotide diversity index (Pi) of 0.362 with 236 variation sites among 16 Resistance Gene Analogues (RGAs). Homology ranged from 44.4% to 98.5%.	[37]

The NBS-LRR family can be divided into major subfamilies based on N-terminal domains: the TIR-NBS-LRR (TNL) and CC-NBS-LRR (CNL) subfamilies, with the CNL subfamily further subdivided [36] [34]. A third, smaller subfamily is the RPW8-NBS-LRR (RNL). The number and proportion of these subfamilies vary significantly between species, as shown in the comparative table below.

Table 2: Comparative Analysis of NBS-LRR Gene Subfamilies

Species	Total NBS-LRRs	CNL Subfamily	TNL Subfamily	RNL Subfamily	Notable Evolutionary Features
Arabidopsis thaliana (Reference)	~150	~53	~112	Not specified	Model for dicot NBS-LRR evolution; contains both TNL and CNL.
Solanum pimpinellifolium (Wild Tomato)	245	Majority (CNL expansion)	Minority	Not specified	~60% of genes in clusters; species-specific CNL expansion.
Akebia trifoliata	73	50	19	4	TNLs have more exons than CNLs; expansion via tandem (33) and dispersed (29) duplications.
Oryza sativa (Rice)	>400	All (CNL-only)	0 (Absent)	Not specified	TNLs are completely absent from cereal genomes, a major lineage-specific difference.	[34]

Experimental Protocols for PAV Discovery and NBS Gene Characterization

Genome-Wide PAV Identification and Association Analysis

The following workflow outlines a robust protocol for identifying PAVs and linking them to agronomic traits, as demonstrated in peach [32].

Detailed Protocol Steps:

• Population Selection and Resequencing: Select a diverse population of plant accessions (e.g., 100 peach accessions). Extract high-quality genomic DNA and perform whole-genome resequencing to generate high-coverage sequencing reads for each individual [32].
• Sequence Alignment and PAV Calling: Align the resequencing reads from all accessions to a high-quality reference genome using aligners like BWA or Bowtie2. Identify PAVs by detecting genomic regions that are consistently unalignable in a subset of individuals, indicating the absence of that sequence. Specialized tools for structural variant detection can be employed for this step [32].
• Functional Annotation of Novel Genes: Analyze the sequences within the identified PAV regions that are absent from the reference genome. Use ab initio gene prediction tools and homology searches to annotate these sequences and identify novel genes not present in the reference pan-genome [32].
• PAV-Based Genome-Wide Association Study (GWAS): Use the PAV profile (presence/absence of each sequence across all accessions) as genotypic data. Perform association analysis with meticulously collected phenotypic data on agronomic traits (e.g., disease resistance, morphology, chilling requirements) to identify PAVs significantly linked to traits of interest [32].
• Functional Validation: For candidate genes identified through GWAS, conduct further validation. This includes tissue-specific expression analysis using RT-PCR or RNA-seq, and functional characterization via gene transformation (e.g., overexpression or knock-out) experiments to confirm the gene's role in the trait [32].

Isolation and Characterization of NBS-LRR Genes

For the targeted study of NBS-LRR genes, a PCR-based approach using degenerate primers is a standard method.

Detailed Protocol Steps:

• DNA Isolation and Primer Design: Extract genomic DNA using a standard method like CTAB. Design degenerate primers based on highly conserved amino acid motifs within the NBS domain, such as the P-loop (e.g., GGVGKTT) and kinase-2 (e.g., LVLDDVW) domains. Degeneracy accounts for the codon redundancy for these conserved amino acids [37].
• PCR Amplification and Cloning: Perform PCR amplification using the degenerate primers and the extracted DNA as a template. Resolve the PCR products on an agarose gel, and purify fragments of the expected size (~250-500 bp). Clone these fragments into a plasmid vector (e.g., pGEM-T Easy Vector) and transform into E. coli for propagation [37].
• Sequence Analysis and Polymorphism Assessment: Sequence multiple positive clones from the library. Use bioinformatics software (e.g., DNASTAR, DNAMAN) to align the sequences and remove duplicates. Analyze the allelic variation using software like DnaSP to calculate nucleotide diversity indices (Pi) and identify variation sites [37].
• Classification and Phylogenetic Analysis: Translate the nucleotide sequences into amino acid sequences. Use motif analysis tools (e.g., PFAM, COILS) to identify domains (TIR, CC, NBS, LRR) and classify the genes into subfamilies (TNL, CNL, RNL). Construct a phylogenetic tree to understand evolutionary relationships [37] [35] [36].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagents for PAV and NBS-LRR Gene Analysis

Reagent / Solution	Function / Application	Example Use Case
High-Quality Reference Genome	Baseline for alignment and variant calling; essential for defining PAVs.	Used in peach PAV study to identify 2.52 Mb of non-reference sequence [32].
Degenerate Primers	Amplify diverse members of a gene family by targeting conserved domains despite sequence variation.	Designed against P-loop/kinase-2 motifs to isolate 16 NBS-LRR RGAs from mango [37].
pGEM-T Easy Vector	TA-cloning vector for efficient ligation and propagation of PCR-amplified fragments.	Used for cloning the 250 bp PCR fragments of mango NBS-LRR RGAs [37].
PFAM Database & HMM Profile	Identify and verify protein domains (e.g., NBS: PF00931) in candidate genes from sequence data.	Critical for classifying NBS genes in Akebia trifoliata and wild tomato [35] [36].
CRB (China Rice Blast) Strains	A standardized set of pathogen isolates used for phenotyping and assessing broad-spectrum resistance.	Used to confirm broad-spectrum blast resistance in elite rice restorer lines SH548, SH882, and WSSM [38].
Paclitaxel octadecanedioate	Paclitaxel octadecanedioate, MF:C65H83NO17, MW:1150.3 g/mol	Chemical Reagent
Br-PEG3-ethyl acetate	Br-PEG3-ethyl acetate, MF:C10H19BrO5, MW:299.16 g/mol	Chemical Reagent

The integration of PAV discovery and NBS-LRR gene benchmarking provides a powerful framework for understanding plant immune system diversity and evolution. The quantitative data and standardized protocols presented here offer researchers a roadmap for identifying and characterizing novel resistance genes. The enrichment of PAVs in resistance genes, coupled with the extensive diversity of the NBS-LRR family, underscores their combined importance in plant adaptation and defense. Future research, leveraging pangenome sequencing and advanced pathogen phenotyping, will continue to uncover the vast repertoire of resistance genes available for developing durable disease-resistant crops and informing broader strategies in plant-based drug development.

Advanced Pipelines for NBS Gene Discovery and Characterization

The accurate prediction of resistance (R) genes, particularly those encoding the nucleotide-binding site and leucine-rich repeat (NBS-LRR) domains, is fundamental for understanding plant-pathogen interactions and advancing disease-resistant crop development [39] [15]. For decades, bioinformatic tools have been indispensable in identifying and characterizing these genes on a genome-wide scale. Traditional methods, primarily based on sequence homology, have provided a strong foundation. However, the emergence of deep learning is revolutionizing the field, offering new capabilities for predicting gene function and regulatory effects with unprecedented accuracy [40]. This guide objectively compares the performance of established and novel tools for R gene prediction, providing a framework for benchmarking novel NBS genes within the context of evolutionary and functional genomics research.

Traditional Tools for R Gene Identification

BLAST-Based Homology Search

The Basic Local Alignment Search Tool (BLAST) suite represents the cornerstone of sequence homology-based identification [41] [42].

• BLASTP is used to compare a protein query sequence against a protein database, making it ideal for identifying known R genes and their close homologs based on amino acid sequence similarity [42].
• BLASTX translates a nucleotide query sequence in all six reading frames and compares it against a protein database. This is particularly valuable for identifying potential R genes in newly sequenced or unannotated genomic regions, such as expressed sequence tags (ESTs) [42].
• tBLASTN compares a protein query sequence against a nucleotide database dynamically translated in all six reading frames. This tool is crucial for finding homologous protein-coding regions in unannotated nucleotide sequences, including draft genome assemblies [42].

Typical Workflow and Output: A standard BLAST analysis produces several key metrics for evaluating matches [42]:

• Expect Value (E-value): The number of alignments expected by chance with a particular score; lower E-values (closer to zero) indicate greater statistical significance.
• Percent Identity: The degree of similarity between the query and matched sequences.
• Query Coverage: The percentage of the query sequence length included in the alignment.

While highly specific for finding genes similar to known R genes, BLAST-based methods have limitations in identifying highly divergent or novel R gene families and require existing knowledge for effective querying [40].

Hidden Markov Models (HMMs) and Domain-Based Identification

A more refined approach involves identifying R genes through their conserved protein domains using Hidden Markov Models (HMMs). This method involves scanning protein sequences against pre-defined HMM profiles for domains like the NB-ARC (PF00931), TIR (PF01582), and LRR (PF08191) from databases such as Pfam [15] [43].

Standard Experimental Protocol for Genome-Wide NBS-LRR Identification [15] [43]:

• Data Retrieval: Obtain the complete proteome or predicted protein sequences for the target organism.
• HMM Search: Use tools like HMMER to scan all protein sequences against the NB-ARC (PF00931) HMM profile. Use a trusted cutoff E-value (e.g., 1.0 or more stringent) to generate a list of candidate NBS-containing proteins.
• Domain Architecture Classification: Further analyze candidate sequences to classify them into subfamilies:
  • Use NCBI CDD or Pfam to identify the presence of TIR, RPW8, and LRR domains.
  • Use coiled-coil prediction tools like COILS or Paircoil2 (with a threshold score of 0.025) to identify CC domains, which are often not detected by Pfam.
• Final Curation: Manually curate the list to remove redundant entries and validate the domain architecture of each candidate NBS-LRR gene.

Table 1: Key Research Reagent Solutions for Traditional R Gene Identification

Research Reagent / Tool	Type	Primary Function in R Gene Research
BLAST Suite [41] [42]	Software Suite	Identifying sequences with significant homology to known R genes or protein domains.
Pfam Database [43]	HMM Profile Database	Providing curated HMM profiles for conserved domains like NB-ARC (PF00931), TIR, and LRR.
HMMER [43]	Software Tool	Scanning protein sequences against HMM profiles to identify domain matches.
InterProScan [39]	Software Tool	Integrating multiple protein signature databases for comprehensive functional analysis.
MCScanX [39]	Software Tool	Identifying collinearity blocks and gene duplication events within and across genomes.

The Rise of Deep Learning in Genome Annotation

Deep learning (DL) frameworks address several limitations of traditional methods by learning complex patterns directly from sequence data, enabling ab initio prediction of gene structures and functions without relying solely on homology [40]. These models, particularly those using convolutional neural networks (CNNs) and transformers, can predict coding regions, splicing sites, and regulatory elements with high accuracy.

Specialized Deep Learning Tools: Helixer for Gene Prediction

Helixer is a DL tool specifically designed for ab initio structural genome annotation. It uses deep neural networks and a hidden Markov model to predict base-wise gene features (intergenic, UTR, CDS, intron) and produces primary gene models in GFF3 format directly from DNA sequence [44].

Key Experimental Protocol for Helixer [44]:

• Input Preparation: Submit a genome assembly in FASTA format. Minimum sequence length per record is 25 kbp.
• Model Selection: Choose a pre-trained model based on the evolutionary lineage of your species (land_plant, vertebrate, invertebrate, or fungi).

• Execution: Run the one-step inference command:

• Output: The main output is a GFF3 file containing the coordinates of all predicted gene features.

Helixer's performance is enhanced by using a GPU, with lineage-specific recommended subsequence lengths (e.g., 213,840 for vertebrates, 64,152 for land plants) to capture typical gene structures [44].

Generalized Deep Learning Models: AlphaGenome for Variant Effect Prediction

AlphaGenome is a more recent, unifying DL model that predicts the regulatory impact of genetic variants across a wide range of biological processes [45]. It is complementary to tools like Helixer, as it focuses on interpreting the function of non-coding regions and the effects of sequence variation.

Key Capabilities of AlphaGenome [45]:

• Long Context, High Resolution: Processes up to 1 million DNA base pairs and makes predictions at single-base-pair resolution.
• Multimodal Prediction: Simultaneously predicts thousands of molecular properties, including transcription levels, splice junctions, and protein-binding sites.
• Efficient Variant Scoring: Contrasts predictions for mutated and unmutated sequences to score variant impact in under a second.

AlphaGenome has demonstrated state-of-the-art performance, outperforming specialized models on 22 out of 24 evaluations for single-sequence prediction and matching or exceeding top models on 24 out of 26 evaluations for variant effect prediction [45].

Comparative Performance Analysis

To benchmark the performance of traditional and deep learning tools, researchers can evaluate them on common tasks such as identifying known NBS-LRR genes and predicting novel ones.

Table 2: Quantitative Comparison of R Gene Prediction Tools

Tool	Underlying Methodology	Primary Application	Key Performance Metrics	Typical Data Requirements
BLASTP [42]	Local Sequence Alignment	Homology-based gene identification	High specificity for known families; E-value, % Identity, Query Coverage [42]	Protein sequence of a known R gene
HMMER (Pfam) [43]	Hidden Markov Models	Domain-centric gene identification	High accuracy in identifying NBS domains; sensitive to domain architecture [15] [43]	Whole proteome or genomic sequences
Helixer [44]	Deep Neural Networks	Ab initio structural annotation	Base-wise prediction accuracy; Gene model completeness [44]	Genome assembly in FASTA format
AlphaGenome [45]	Transformer-based Model	Regulatory variant effect prediction	State-of-the-art AUC in splice/junction/expression prediction [45]	Reference genome sequence and variant(s) of interest

Benchmarking Experimental Design for Novel NBS Gene Validation [15] [14]:

• Gene Set Compilation: Identify candidate NBS genes from a target species using HMMER and BLAST against a custom database of known R genes.
• Cross-Tool Validation: Run the same genomic sequence through Helixer to compare its ab initio predictions with the homology-based candidates.
• Evolutionary Analysis: Use OrthoFinder to identify orthogroups and MCScanX to analyze synteny and gene duplication events (tandem vs. whole-genome) among the identified genes [39] [14].
• Expression Profiling: Validate predictions using RNA-seq data from tissues under biotic/abiotic stress. Calculate FPKM values and perform differential expression analysis. Genes with significant upregulation under pathogen challenge are strong functional candidates [14].
• Functional Validation: For high-confidence candidates, use Virus-Induced Gene Silencing (VIGS) to knock down gene expression and assess the resulting change in disease resistance phenotype [14].

Integrated Workflow and Logical Relationships

The following diagram illustrates the logical workflow and relationship between different tools and analyses in a comprehensive R gene benchmarking study.

The toolkit for R gene prediction has expanded dramatically, from the foundational, homology-based BLAST to sophisticated deep learning models like Helixer and AlphaGenome. Traditional methods offer precision in finding genes related to known families, while DL frameworks provide powerful, generalized capabilities for ab initio prediction and functional interpretation of sequences and their variants. A robust benchmarking strategy for novel NBS genes should leverage the strengths of both approaches: using HMMs and BLAST for targeted identification, complemented by DL tools for comprehensive gene model prediction and functional insight. Integrating these computational predictions with evolutionary analysis, expression studies, and functional validation creates a rigorous pipeline for discovering and characterizing the genetic basis of disease resistance.

Resistance Gene Enrichment Sequencing (RenSeq) and Targeted Capture

Resistance Gene Enrichment Sequencing (RenSeq) is a powerful target capture method based on next-generation sequencing (NGS) that is specifically designed for identifying and characterizing resistance (R) genes in plants. R genes, particularly those encoding nucleotide-binding site and leucine-rich repeat (NBS-LRR or NLR) proteins, are crucial for plant immune responses [24]. The duplicated, clustered nature and high sequence diversity of these genes make them difficult to annotate with standard pipelines [24]. RenSeq addresses this challenge by using probe-based hybridization to enrich for these specific genomic regions prior to sequencing. This guide objectively compares RenSeq's performance against alternative genomic methods, providing experimental data to inform its application in benchmarking novel NBS genes against known resistance genes.

Performance Comparison of Sequencing Methods

The following tables summarize the key performance characteristics of RenSeq and its alternatives, based on current experimental findings.

Table 1: Overall Performance and Operational Characteristics

Method	Primary Principle	Sensitivity for Target Regions	Cost per Sample (Relative)	Typical Turnaround Time
RenSeq / Targeted Capture (tNGS)	Hybrid-capture with biotinylated probes [46]	High (76-82% target coverage) [46]	Medium [47]	~20 hours [47]
Amplification-based tNGS	Ultra-multiplex PCR amplification [47]	Lower for some bacteria [47]	Lower [47]	Shorter [47]
Metagenomic NGS (mNGS)	Untargeted shotgun sequencing [47]	Varies with host DNA content [46]	High (~$840) [47]	Longer (~20 hours) [47]
Conventional Culture & PCR	Pathogen growth or targeted DNA amplification	Low to Moderate [48]	Low	Culture: Days; PCR: Hours [49]

Table 2: Diagnostic and Analytical Performance Metrics

Method	Pathogen/Genotype Detection Range	Ability to Detect Antimicrobial Resistance (AMR) Genes	Remarks / Best Use Cases
RenSeq / Targeted Capture (tNGS)	Targeted, but broad within panel (e.g., 280 pathogens, 1200 AMR genes) [48]	Excellent (e.g., identifies blaOXA, blaSHV, blaCMY) [48]	Preferred for routine diagnostics and comprehensive AMR profiling [47]
Amplification-based tNGS	Limited to pre-designed primer sets (e.g., 198 targets) [47]	Good, but limited to panel [47]	Alternative for rapid results with limited resources [47]
Metagenomic NGS (mNGS)	Unbiased, broadest potential (e.g., 80 species in one study) [47]	Possible, but efficiency depends on sequencing depth [46]	Ideal for detecting rare or unexpected pathogens [47]
Multiplex PCR (e.g., FilmArray PN)	Limited to pre-defined panel [48]	Limited to pre-defined panel [48]	Rapid, but cannot discover novel genes or variants outside its panel [48]

Experimental Protocols for Key Applications

Protocol 1: Hybrid-Capture-Based Target Enrichment for Severe Pneumonia

A recent prospective study evaluated a hybrid-capture-based NGS panel (Respiratory Pathogen ID/AMR Enrichment Panel, RPIP) for patients with severe pneumonia [48].

• Sample Collection: Lower respiratory tract samples were obtained via bronchoalveolar lavage, bronchial washing, or endotracheal tube suction [48].
• Library Preparation and Enrichment: The RPIP kit was used, which is designed to identify over 280 respiratory pathogens and more than 1,200 AMR genotypes. The process relies on probe hybridization to enrich target sequences before sequencing [48].
• Sequencing and Analysis: Sequencing was performed on a next-generation platform. The resulting data were analyzed to profile pathogens and identify AMR-associated genes [48].
• Comparison: The performance of RPIP was directly compared to conventional culture methods and the multiplex PCR-based FilmArray Pneumonia Panel (FilmArray-PN) [48].
• Key Findings: RPIP demonstrated significantly better detection rates for bacteria, viruses, and fungi compared to FilmArray-PN and culture. It also successfully identified additional AMR genes, such as extended-spectrum β-lactamase genes (blaOXA, blaCMY) and carbapenemase genes (blaOXA, blaSHV) [48].

Protocol 2: Probe-Based Enrichment for Antimicrobial Resistance Detection

A study on Neisseria gonorrhoeae detailed a probe-capture protocol to improve AMR determinant detection from clinical samples [46].

• Probe Design: A custom SureSelectXT probe library was designed to cover 17 closed genomes of N. gonorrhoeae. Probes targeting known AMR genes were represented tenfold in the final library. The library consisted of approximately 50,000 120-mer RNA probes [46].
• Sample Processing: DNA was extracted directly from NAAT-positive urine samples and culture-positive urethral swabs [46].
• Enrichment and Sequencing: Samples were sequenced on the Oxford Nanopore Technologies (ONT) platform with and without the probe-based target enrichment. A multiplexing protocol was also tested, where four samples were barcoded, pooled, and then enriched together [46].
• Analysis: Sequencing reads were base-called and mapped to a reference genome. Variants were called, and AMR genes were characterized from consensus genome sequences [46].
• Key Findings: Enrichment dramatically increased the proportion of N. gonorrhoeae sequences from a median of 0.05% to 76%. The number of samples achieving >20-fold genome coverage (required for robust AMR detection) increased from 2/15 (13%) without enrichment to 13/15 (87%) with enrichment. Multiplexing prior to enrichment maintained high genome coverage while reducing costs [46].

Workflow and Pathway Visualizations

Diagram 1: RenSeq and Targeted Capture Core Workflow. This diagram outlines the key steps, from sample preparation to bioinformatic analysis, that are common to probe-capture methods like RenSeq.

Diagram 2: Probe Hybridization and Enrichment Mechanism. This diagram illustrates the key steps of the hybrid-capture process, which selectively pulls down target DNA sequences for sequencing.

The Scientist's Toolkit: Essential Research Reagent Solutions

Table 3: Key Reagents and Kits for RenSeq and Targeted Capture Experiments

Reagent / Kit	Function / Description	Example Use Case
SureSelectXT Custom Probe Library (Agilent)	A library of biotinylated RNA probes designed to target and enrich specific genomic regions. Probes can be designed to cover entire pathogen genomes or specific gene families with increased density for key regions like AMR determinants [46].	Enriching Neisseria gonorrhoeae DNA from clinical samples for AMR gene detection [46].
Respiratory Pathogen ID/AMR Enrichment Panel (RPIP)	A commercially available panel designed to identify hundreds of respiratory pathogens and over a thousand AMR genotypes via hybrid-capture [48].	Comprehensive pathogen profiling and resistance gene detection in severe pneumonia patients [48].
QIAamp UCP Pathogen DNA Kit (Qiagen)	Used for the extraction of high-quality, inhibitor-free DNA from complex clinical samples, which is critical for downstream sequencing success [46] [47].	DNA extraction from bronchoalveolar lavage fluid (BALF) or urine samples prior to library preparation [46] [47].
Benzonase (Qiagen) & Tween20 (Sigma)	Enzymatic and detergent-based reagents used to digest and remove host DNA during sample preparation, thereby increasing the proportion of pathogen DNA in the sample [47].	Host DNA depletion in BALF samples for mNGS or tNGS to improve pathogen detection sensitivity [47].
Benzyltrimethylammonium tribromide	Benzyltrimethylammonium tribromide, MF:C10H16Br3N-2, MW:389.95 g/mol	Chemical Reagent
MAL-di-EG-Val-Cit-PAB-MMAF	MAL-di-EG-Val-Cit-PAB-MMAF, MF:C73H113N13O19, MW:1476.8 g/mol	Chemical Reagent

Whole-Genome Sequencing vs. Targeted Approaches for NLR Profiling

Profiling the nucleotide-binding domain and leucine-rich repeat (NLR) gene family is fundamental to understanding plant immunity and advancing disease resistance breeding. NLRs constitute a major class of intracellular immune receptors that detect pathogen effectors and initiate robust immune responses, a process known as effector-triggered immunity (ETI) [50] [51]. Two primary sequencing strategies exist for NLR discovery: whole-genome sequencing (WGS) and targeted sequencing. This guide provides an objective comparison of these approaches, framing the analysis within the critical context of benchmarking novel NLR genes against known resistance genes. The evaluation is based on performance metrics, experimental requirements, and practical applications, providing researchers with a data-driven foundation for selecting the appropriate method for their profiling objectives.

Technical Performance Comparison

The choice between WGS and targeted approaches involves balancing the comprehensiveness of data against sequencing efficiency and cost. The table below summarizes key performance characteristics based on published experiments.

Table 1: Performance Comparison of NLR Profiling Methods

Feature	Whole-Genome Sequencing (WGS)	Targeted Enrichment Approaches
General Principle	Sequences the entire genome without prior targeting [52].	Enriches for specific genomic regions before or during sequencing [53] [54].
Typical Read Depth for NLRs	Uniform across the genome; NLR coverage depends on total sequencing effort.	Significantly higher in targeted regions; 4-fold enrichment reported via NAS [54].
Ideal NLR Application	Discovery of all NLR classes, including novel and divergent members [52].	Focused analysis of known NLR clusters or specific gene families [54].
Ability to Resolve Complex Clusters	Effective with long-read technologies (ONT, PacBio) [52] [54].	Highly effective; long reads are precisely directed to complex, repetitive clusters [54].
Handling of Novel/Divergent NLRs	Excellent for discovery [52].	Poor unless novel NLRs share sufficient similarity with the reference used for enrichment [54].
Cost & Data Efficiency	Higher cost for deep coverage; generates large, redundant datasets [54].	Lower cost per sample for targeted regions; reduced data storage and analysis load [54].

Targeted methods like Nanopore Adaptive Sampling (NAS) and RenSeq leverage enrichment to overcome challenges in NLR profiling. NLR genes are often organized in complex, repetitive clusters that are difficult to assemble with short-read technologies [54]. NAS uses real-time base-calling and mapping to a reference set of NLRs; reads matching the reference are fully sequenced, while non-matching reads are ejected from the pore, efficiently enriching the data stream for NLRs [54]. RenSeq, which can be performed on platforms like PacBio and Oxford Nanopore, uses hybridization-based capture with biotinylated RNA baits designed from known NLR sequences to enrich genomic libraries [53].

Experimental Protocols and Methodologies

Whole-Genome Sequencing for NLR Discovery

A typical WGS workflow for NLR identification, as used in cowpea, involves:

• DNA Extraction: Isolate high-quality, high-molecular-weight genomic DNA from young leaves, verified for purity and integrity using spectrophotometry (e.g., Nanodrop), fluorometry (e.g., Qubit), and gel electrophoresis [52].
• Library Preparation and Sequencing:
  • Illumina: Fragment DNA to 200-250 bp, prepare a sequencing library with barcoded adapters, and sequence on a platform like HiSeq X Ten for short-read data [52].
  • Oxford Nanopore Technologies (ONT): Use a ligation sequencing kit (e.g., SQK-LSK109) to prepare the library without fragmentation for long reads. Sequence on a GridION or PromethION platform with a flow cell (e.g., R9.4) [52].
• Genome Assembly & NLR Identification: Perform hybrid assembly of short and long reads using a tool like MaSuRCA. Mask repetitive regions with RepeatModeler and RepeatMasker. Identify NLR genes using a combination of Hidden Markov Model (HMM) searches against conserved domains (e.g., PF00931) and BLAST-based homology searches [52] [55].

Targeted Sequencing with Nanopore Adaptive Sampling

The protocol for NAS-based NLR profiling, demonstrated in melon, includes:

• Define Target Regions: Using a reference genome (e.g., melon cultivar 'Anso77'), identify NLR-containing Regions of Interest (ROIs) using a tool like NLGenomeSweeper. Expand ROIs by adding 20 kb flanking regions to create initial target regions. Refine targets by excluding repetitive elements >200 bp to improve enrichment efficiency [54].
• DNA Extraction and Library Preparation: Extract high-molecular-weight DNA (e.g., using NucleoSpin Plant II kit). Prepare a standard ONT sequencing library (e.g., with SQK-LSK109 kit) without amplification [54].
• Sequencing with Adaptive Sampling: Load the target regions (in BED format) and reference genome into the MinKNOW software. During sequencing, the software performs real-time mapping of the initial ~500 bases of each DNA strand; strands matching the target are sequenced completely, while non-matching strands are ejected by reversing the pore voltage [54].

Analysis of Supporting Experimental Data

Quantitative data from published studies allows for a direct comparison of the outputs from these methodologies.

Table 2: Comparative Experimental Data from Profiling Studies

Study & Approach	Key Experimental Output	Implication for NLR Profiling
Cowpea WGS [52]	Identified 2,188 R-genes from a hybrid (Illumina+Nanopore) genome assembly.	Demonstrates the power of WGS for cataloging the entire repertoire of R-genes (including NLRs) in a species.
Melon NAS [54]	Achieved 4-fold enrichment of 15 NLR genomic regions in cultivars distinct from the reference.	Highlights the efficiency and accuracy of NAS for targeted resequencing of known NLR clusters across diverse germplasm.
SMRT RenSeq [53]	MinION data yielded 193,850 2D passes with 91.36% mean accuracy, comparable to PacBio subreads.	Validates targeted long-read sequencing as a viable method for accurate NLR gene assembly, identifying novel gene fusions.

The following diagram illustrates the core decision-making workflow for selecting between these approaches based on research goals.

Applications in NLR Benchmarking and Functional Validation

The ultimate goal of NLR profiling is often to link sequence data to function. Both WGS and targeted sequencing feed into downstream validation pipelines, a prime example of which is a high-throughput transgenic approach.

A recent large-scale study demonstrated that functional NLRs across monocot and dicot species often show a signature of high expression in uninfected plants [56]. Researchers exploited this signature to select 995 candidate NLRs from diverse grasses for a high-throughput transformation array in wheat. This pipeline led to the functional validation of 31 new resistance genes (19 against stem rust and 12 against leaf rust) [56]. This workflow exemplifies how genomic profiling, whether by WGS or targeted methods, provides the candidate gene list for subsequent large-scale functional phenotyping.

The diagram below outlines this integrated process from gene discovery to functional validation.

Successful NLR profiling and validation rely on a suite of specific reagents and bioinformatic resources.

Table 3: Key Reagents and Tools for NLR Research

Item/Category	Function/Application	Specific Examples / Notes
High-Integrity DNA Extraction Kits	To obtain long, sheared-free DNA molecules crucial for long-read sequencing and long-range PCR.	NucleoSpin Plant II kit [54]; Qiagen DNeasy Plant Mini kit [52].
Long-Range PCR Kits	For amplifying large, multi-kilobase NLR gene loci from genomic DNA.	Used in initial SMRT RenSeq library preparation [53].
Sequencing Kits (ONT)	For preparing genomic DNA libraries for Nanopore sequencing.	Ligation Sequencing Kit (e.g., SQK-LSK109) [52] [54].
Bait Libraries (RenSeq)	Biotinylated RNA probes used in hybrid capture to enrich sequencing libraries for NLRs.	Designed from conserved NLR domains; critical for hybridization-based RenSeq [53].
Bioinformatic Tools for NLR Identification	To identify and annotate NLR genes from genome assemblies or sequencing reads.	NLGenomeSweeper [54]; HMMER (for PF00931 domain) [55].
Plasmid Vectors for Plant Transformation	For stable integration and expression of candidate NLR genes in a heterologous system.	Essential for high-throughput functional validation in plants like wheat or Nicotiana benthamiana [56] [57].

Whole-genome sequencing and targeted approaches for NLR profiling are not mutually exclusive but are complementary strategies defined by the researcher's goals. WGS is the undisputed choice for discovery-oriented projects aiming to catalog the entire "NLRome" of a species, identify novel NLR classes, and provide a foundational genomic resource [52]. In contrast, targeted methods like NAS and RenSeq offer a cost-effective, efficient, and highly accurate solution for focused applications, such as screening a breeding population for specific NLR alleles, resolving complex cluster polymorphisms, and validating candidates prior to functional studies [53] [54].

The emerging paradigm for effective NLR gene discovery and benchmarking integrates both approaches: using WGS to define the full gene set in reference genotypes and then employing targeted sequencing to efficiently screen these loci across hundreds of individuals. Coupled with high-throughput functional validation pipelines that can test dozens of candidates [56], these sequencing technologies are powerfully accelerating the pace of plant immunity research and the development of disease-resistant crops.

Orthogroup Analysis and Phylogenetic Studies for Gene Family Evolution

In the field of comparative genomics, orthogroup inference forms the foundational step for understanding gene family evolution across multiple species. An orthogroup is defined as the complete set of genes descended from a single ancestral gene in the last common ancestor of the species being analyzed [58]. This concept extends beyond pairwise ortholog identification to encompass entire gene families, including both orthologs and paralogs. The accurate identification of orthogroups is therefore critical for phylogenetic studies, functional annotation transfer, and evolutionary analyses [58].

The development of automated methods for orthogroup inference has dramatically accelerated comparative genomic studies, with widely used tools including OrthoMCL, OMA, Hieranoid, OrthoFinder, and SonicParanoid [58]. These methods employ different algorithmic approaches to tackle the challenges introduced by gene duplication and loss, unequal species sampling, and differential rates of sequence evolution. The accuracy of these inference methods is typically assessed using benchmarking tools such as Orthobench, which provides expert-curated reference orthogroups (RefOGs) that represent known evolutionary relationships [58].

Within the specific context of resistance gene research, orthogroup analysis has proven particularly valuable for characterizing rapidly evolving gene families such as the Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) genes, which represent the largest class of plant resistance (R) genes and play crucial roles in pathogen recognition and defense signaling [16] [14] [36]. By applying orthogroup inference methods to these gene families, researchers can identify evolutionary relationships, trace expansion and contraction events, and ultimately facilitate the discovery of novel resistance genes with potential applications in crop improvement and drug development.

Benchmarking Orthogroup Inference Methods

The Orthobench Benchmarking Framework

The Orthobench database serves as the standard benchmark for assessing the accuracy of orthogroup inference methods, containing 70 expert-curated reference orthogroups (RefOGs) that span the Bilateria and cover a range of different challenges for orthogroup inference [58]. These RefOGs were originally assembled through expert analysis of rooted gene trees inferred from multiple sequence alignments and have acted as a gold standard against which orthogroup inference methods have been tested for nearly a decade [58].

A recent phylogenetic revision of Orthobench leveraging improvements in tree inference algorithms and computational resources altered the membership of 31 of the 70 RefOGs (44%), with 24 subject to extensive revision and 7 requiring minor changes [58]. This updated benchmark revealed that the most common reason for major revision was that phylogenetically relevant genes were missing from the original gene trees, while overinclusion errors typically resulted from misinterpretation of gene duplication events that occurred prior to the divergence of the Bilateria [58].

Performance Comparison of Orthogroup Inference Tools

Table 1: Comparison of Orthogroup Inference Methods Based on Orthobench Benchmark

Method	Algorithm Type	Inference Accuracy	Scalability	Special Features
OrthoFinder	Graph-based clustering + phylogenetic tree inference	High (improved with MSA option)	Suitable for hundreds of genomes	Species tree inference, gene tree-species tree reconciliation
OrthoMCL	Markov clustering of similarity graphs	Moderate	Moderate	Based on Markov clustering algorithm
OMA	Pairwise comparisons + hierarchical clustering	High for pairwise orthology	Computationally intensive	Focus on pairwise orthologs
Hieranoid	Hierarchical inference using tree structure	High for closely related species	Dependent on species tree quality	Uses phylogenetic relationships
SonicParanoid	Fast similarity search + clustering	Fast with good accuracy	Highly scalable	Optimized for speed and low memory usage

When assessed using the updated Orthobench benchmark, OrthoFinder demonstrated particularly high inference accuracy, especially when run with its multiple sequence alignment option ("-M msa"), which constructs a species tree from a concatenated alignment of single-copy genes [58] [59]. This method has been widely adopted in recent studies of gene family evolution, including analyses of NBS-LRR resistance genes across plant species [14] and comparative genomic studies of insect gene families [59].

Table 2: OrthoFinder Performance Metrics from a 14-Species Comparative Genomic Study

Metric	Value	Interpretation
Total genes assigned	201,275 (95.3% of total)	High coverage of input genes
Total orthogroups identified	15,964	Comprehensive grouping
G50 (gene count in orthogroups)	15 genes	Medium-sized orthogroups
O50 (orthogroup count)	4,780 orthogroups	Half of genes in largest orthogroups
Single-copy orthogroups	3,328	Suitable for phylogeny construction
Universal orthogroups	6,653	Core gene sets across species

The performance metrics in Table 2 demonstrate OrthoFinder's capability to comprehensively assign genes to orthogroups, with a recent 14-species comparative genomic study reporting that 95.3% of 201,275 genes were successfully assigned to 15,964 orthogroups [59]. This high coverage is essential for reliable evolutionary analyses, particularly when studying rapidly evolving gene families like NBS-LRR resistance genes.

Orthogroup Analysis of NBS-LRR Resistance Genes

NBS-LRR Gene Family Diversity and Classification

NBS-LRR genes represent the largest class of plant resistance genes, with approximately 80% of characterized R genes encoding proteins containing Nucleotide-Binding Site (NBS) and Leucine-Rich Repeat (LRR) domains [16]. These genes provide resistance to a wide range of pathogens including bacteria, fungi, oomycetes, viruses, and nematodes [16] [14]. Based on their N-terminal domains, NBS-LRR genes are classified into two main subfamilies: TIR-NBS-LRR (TNL) genes containing Toll/Interleukin-1 receptor domains and CC-NBS-LRR (CNL) genes featuring coiled-coil domains, with the latter sometimes referred to as non-TNL (nTNL) [16] [14].

The functional domains of NBS-LRR proteins each play distinct roles in pathogen recognition and defense signaling. The NBS domain contains several conserved motifs (P-loop, RNBS-A, kinase-2, RNBS-B, RNBS-C, and GLPL) essential for ATP/GTP binding and hydrolysis, which are crucial for initiating immune signaling [16]. The LRR domain, known for its involvement in protein-protein interactions, is responsible for recognition specificity, while auxiliary domains (TIR or CC) facilitate protein interactions and signal transduction [16] [36].

Table 3: NBS-LRR Gene Distribution Across Plant Species

Plant Species	Total NBS-LRR Genes	CNL Genes	TNL Genes	RNL Genes	Genome Size	Reference
Capsicum annuum (pepper)	252	248 (nTNL)	4	Not specified	~3.5 Gb	[16]
Akebia trifoliata	73	50 (CNL)	19 (TNL)	4 (RNL)	Not specified	[36]
Gossypium hirsutum (cotton)	12,820 (across 34 species)	Majority	Minority	Present	Variable	[14]
Arabidopsis thaliana	~200	~160	~40	Present	~135 Mb	[14]

The distribution of NBS-LRR genes across plant species shows remarkable variation, ranging from just 73 genes in Akebia trifoliata to over 12,000 genes identified across 34 plant species in a comprehensive study [14] [36]. This variation reflects species-specific evolutionary pressures and adaptations to different pathogen environments.

Genomic Distribution and Evolutionary Dynamics

Orthogroup analyses have revealed that NBS-LRR genes are typically distributed unevenly across chromosomes, with a strong tendency to form gene clusters driven by tandem duplications and genomic rearrangements [16]. In pepper (Capsicum annuum), 54% of the 252 identified NBS-LRR genes form 47 physical clusters, with the largest cluster comprising eight genes located on chromosome 3 [16]. Similarly, in Akebia trifoliata, 64 mapped NBS candidates were unevenly distributed on 14 chromosomes, with most located at chromosome ends, and 41 of these genes (64%) located in clusters while the remaining 23 were singletons [36].

The evolutionary expansion of NBS-LRR genes occurs primarily through tandem and dispersed duplications, with studies in Akebia trifoliata identifying 33 and 29 genes originating from these mechanisms, respectively [36]. These duplication events create genetic raw material for functional diversification, enabling plants to rapidly adapt to evolving pathogen populations through mechanisms such as neofunctionalization (where duplicated genes evolve new functions) or subfunctionalization (where duplicated genes partition ancestral functions) [59].

Figure 1: Orthogroup Analysis Workflow for NBS-LRR Genes

Experimental Protocols for Orthogroup Analysis

Standard Orthogroup Inference Protocol

The following protocol outlines the standard methodology for orthogroup inference using OrthoFinder, based on implementations described in recent publications [59] [14]:

• Data Preparation: Download proteome files for all species of interest in FASTA format. Filter annotation files to retain only the longest transcript for each gene using the primary_transcript.py script included with OrthoFinder.

• Orthogroup Inference: Run OrthoFinder with the following command:

  The -M msa option enables multiple sequence alignment and tree inference for orthogroups.

• Species Tree Construction: OrthoFinder automatically infers a species tree using the STAG method from single-copy orthogroups and confirms it using a concatenated alignment-based approach.

• Gene Tree Inference: For each orthogroup, infer gene trees using the aligned sequences and a maximum likelihood method implemented in OrthoFinder.

• Orthogroup Quality Assessment: Use benchmarking tools like Orthobench to assess inference accuracy by comparing results to reference orthogroups.

NBS-LRR Gene Identification and Classification Protocol

For specific analysis of NBS-LRR resistance genes, the following specialized protocol should be implemented [14] [36]:

• NBS Domain Identification: Perform HMMER searches against target proteomes using the NB-ARC domain (PF00931) as a query with an e-value cutoff of 1.0. Verify the presence of the NBS domain using PfamScan with an e-value threshold of 10^-4.

• Subfamily Classification: Identify additional domains using the NCBI Conserved Domain Database:

  • TIR domains (PF01582)
  • RPW8 domains (PF05659)
  • LRR domains (PF08191) Identify CC domains using Coiled-coil prediction tools with a threshold of 0.5.

• Motif Analysis: Identify conserved motifs within NBS domains using the MEME Suite with motif width lengths ranging from 6 to 50 amino acids and a motif count of 10.

• Phylogenetic Analysis: Construct a phylogenetic tree using aligned NBS domain sequences with maximum likelihood methods (e.g., FastTreeMP) and 1000 bootstrap replicates.

• Expression Analysis: Analyze RNA-seq data from various tissues and stress conditions to assess expression patterns, calculating FPKM values for comparative analysis.

Advanced Analysis: Visualization and Synteny

Phylogenetic Visualization of Orthogroups

Effective visualization of orthogroup analysis results enhances interpretation and facilitates insight generation. The phytools package in R provides advanced capabilities for visualizing phylogenetic relationships and ancestral state reconstructions [60]. Key visualization approaches include:

• Stochastic character mapping for discrete traits across phylogenies
• Density mapping for binary character evolution
• Edge coloring based on marginal reconstructions

For NBS-LRR gene families, visualization typically focuses on phylogenetic relationships, domain architecture, and chromosomal distribution [16] [36]. Specialized tools like OrthoBrowser provide static site generation for interactive exploration of orthogroup results, including phylogenies, gene trees, multiple sequence alignments, and synteny alignments [61].

Figure 2: Evolutionary Pathways of NBS-LRR Gene Family

Synteny Analysis for Evolutionary Insights

Synteny analysis provides crucial insights into the evolutionary mechanisms driving gene family expansion and contraction. The GENESPACE software package facilitates the construction of synteny plots across multiple species, revealing patterns of genomic conservation and rearrangement [59]. The standard workflow includes:

• Format Conversion: Convert GFF annotation files to bed format using convert2bed utility.

• Orthogroup Integration: Use OrthoFinder results as input for GENESPACE to establish orthology relationships.

• Synteny Visualization: Generate synteny plots showing conserved genomic blocks and rearrangement breakpoints.

In practice, synteny analyses of NBS-LRR genes have revealed that resistance genes are frequently located in dynamic genomic regions characterized by frequent rearrangements and tandem duplications, facilitating rapid evolution in response to pathogen pressure [16] [14].

Table 4: Essential Research Reagents and Computational Tools for Orthogroup Analysis

Category	Tool/Resource	Specific Function	Application in NBS-LRR Research
Orthogroup Inference	OrthoFinder [58] [59]	Phylogenetic orthogroup inference	Identify NBS-LRR gene families across species
Benchmarking	Orthobench [58]	Assessment of inference accuracy	Validate NBS orthogroup assignments
Domain Identification	HMMER/PfamScan [14] [36]	NBS domain detection (PF00931)	Identify NBS-containing genes
Sequence Alignment	MAFFT [58] [14]	Multiple sequence alignment	Align NBS domains for phylogenetic analysis
Phylogenetics	IQ-TREE/FastTreeMP [58] [14]	Phylogenetic tree inference	Reconstruct NBS-LRR evolutionary relationships
Synteny Analysis	GENESPACE [59]	Whole-genome synteny visualization	Identify NBS-LRR gene clusters and rearrangements
Expression Analysis	RNA-seq pipelines [14]	Transcript abundance quantification	Measure NBS-LRR expression under stress
Visualization	OrthoBrowser [61]	Interactive results exploration	Visualize NBS-LRR orthogroups and phylogenies

Orthogroup analysis has emerged as a powerful framework for elucidating the evolution of gene families, with particular value for understanding the complex dynamics of NBS-LRR resistance genes. The benchmarking studies summarized in this guide demonstrate that modern orthogroup inference methods like OrthoFinder provide accurate and comprehensive identification of gene families when properly validated against reference datasets like Orthobench.

The application of these methods to NBS-LRR genes has revealed fundamental insights into their evolutionary dynamics, including the prevalence of tandem duplications, the formation of genomic clusters, and the differential expansion of subfamilies across plant lineages. These findings directly inform practical efforts to identify and characterize novel resistance genes for crop improvement and pharmaceutical development.

As genomic sequencing technologies continue to advance, orthogroup analysis will play an increasingly important role in extracting biological insights from the growing wealth of genomic data. The integration of orthogroup inference with functional validation approaches, such as expression analysis and molecular interaction studies, represents a promising path forward for unlocking the full potential of resistance gene research.

Within the framework of plant immunity, Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) genes constitute the largest and most critical class of disease resistance (R) proteins, serving as intracellular immune receptors that initiate effector-triggered immunity (ETI) [28] [62] [63]. Transcriptomic profiling of this gene family under biotic and abiotic stress provides a powerful strategy for benchmarking novel R genes against established ones, identifying candidates with superior or broad-spectrum resistance potential for crop improvement programs [14]. The functional characterization of NBS genes hinges on understanding their expression dynamics, which can be quantitatively assessed using high-throughput RNA sequencing (RNA-seq) technologies. This guide objectively compares experimental approaches and data from recent studies to establish a standardized workflow for evaluating NBS gene performance, providing researchers with a comparative analysis of methodologies, key findings, and translational applications.

NBS-LRR Gene Family: Structural Diversity and Genomic Distribution

Classification and Domain Architecture

The NBS-LRR gene family is categorized into distinct subfamilies based on N-terminal domain composition and the presence of C-terminal leucine-rich repeats (LRRs). Comparative genomic analyses reveal significant variation in subfamily size and composition across plant species, influencing their immune receptor repertoire [62] [63] [18].

• CNL Subfamily: Characterized by an N-terminal Coiled-Coil (CC) domain.
• TNL Subfamily: Features an N-terminal Toll/Interleukin-1 Receptor (TIR) domain.
• RNL Subfamily: Contains an N-terminal Resistance to Powdery Mildew 8 (RPW8) domain.
• Atypical NBS Proteins: Truncated forms lacking complete domains (e.g., N, CN, TN, NL) often function as adaptors or regulators in immune signaling networks [18] [5].

Table 1: Genomic Distribution of NBS-LRR Genes Across Plant Species

Species	Total NBS Genes	CNL	TNL	RNL	Atypical	Reference
Nicotiana tabacum (Tobacco)	603	224 (CN+CNL)	9 (TN+TNL)	Information Missing	370 (N+NL)	[62]
Salvia miltiorrhiza (Danshen)	196	75 (CC-domain)	2 (TIR-domain)	1 (RPW8-domain)	118	[28] [63]
Nicotiana benthamiana	156	66 (CN+CNL)	7 (TN+TNL)	4 (RPW8-domain)	79 (N+NL)	[5]
Asparagus officinalis (Garden Asparagus)	27	Information Missing	Information Missing	Information Missing	Information Missing	[18]
Asparagus setaceus	63	Information Missing	Information Missing	Information Missing	Information Missing	[18]

Genomic Evolution and Pathogen-Driven Selection

NBS-LRR genes exhibit remarkable genomic plasticity. Whole-genome duplication (WGD) and tandem duplication are primary drivers for the expansion and contraction of this gene family, as observed in Nicotiana species [62]. This rapid evolution is largely attributed to pathogen-driven selection pressure, which shapes the diversity of the NBS-LRR repertoire across species [14] [18]. For instance, the significant contraction of the TNL and RNL subfamilies in Salvia miltiorrhiza and their complete loss in monocots like rice highlight the species-specific evolutionary paths of these genes [63].

Experimental Frameworks for Transcriptomic Profiling of NBS Genes

Core Transcriptomic Technologies and Workflows

Transcriptomic analysis of NBS genes relies on sequencing-based platforms that offer high throughput, accuracy, and the ability to detect novel transcripts.

Diagram 1: Transcriptomic profiling workflow for NBS gene expression analysis. The core RNA-seq pipeline (green) from sample to data is supported by bioinformatic (blue) and experimental validation (red) phases.

RNA-seq has become the gold standard due to its high resolution and capacity for whole-transcriptome analysis [64]. The standard workflow involves RNA extraction from stressed and control tissues, cDNA library preparation, high-throughput sequencing, and comprehensive bioinformatic analysis. Key steps in bioinformatic processing include read quality control, alignment to a reference genome, transcript quantification, and finally, differential expression analysis to identify NBS genes with significantly altered expression under stress conditions [62] [64].

Profiling NBS Expression Under Pathogen Stress

Studies employ controlled pathogen inoculation to directly link NBS gene expression with defense responses. A robust protocol involves:

• Pathogen Inoculation: In the asparagus-Phomopsis asparagi pathosystem, researchers performed controlled inoculation of the fungus on susceptible (A. officinalis) and resistant (A. setaceus) species, followed by phenotypic assessment [18].
• RNA-Seq and Differential Expression: Total RNA is extracted from infected and mock-inoculated control tissues at multiple time points. Following RNA-seq, differential expression analysis (using tools like Cuffdiff) identifies NBS genes with significant transcriptional changes post-inoculation [62] [18].
• Comparative Benchmarking: Expression profiles of NBS genes are compared between resistant and susceptible genotypes to pinpoint genes associated with effective defense responses [18].

Investigating NBS Roles in Abiotic Stress and Secondary Metabolism

Emerging evidence connects NBS-LRR genes to abiotic stress tolerance and secondary metabolism, expanding their functional characterization beyond biotic stress.

• Promoter cis-Element Analysis: Bioinformatic analysis of promoter regions (e.g., using PlantCARE) often reveals an abundance of cis-acting elements related to abiotic stress (e.g., drought, heat) and plant hormones (e.g., ABA, JA), suggesting NBS genes may be involved in broader stress responses [28] [63] [18].
• Expression Correlation with Metabolites: In medicinal plants like Salvia miltiorrhiza, integrating transcriptome data with metabolite profiling can reveal correlations between the expression of specific SmNBS-LRR genes and the accumulation of bioactive compounds like tanshinones, indicating a potential link between defense signaling and secondary metabolism [28] [63].

Comparative Analysis of NBS Gene Expression Under Stress

Integrating transcriptomic data from multiple systems reveals both conserved and species-specific expression patterns for NBS genes, allowing for their functional benchmarking.

Table 2: NBS Gene Expression Profiles Under Biotic and Abiotic Stress

Species / Study	Stress Condition	Key NBS Gene/Orthogroup	Expression Response	Putative Function / Association
Gossypium hirsutum (Cotton) [14]	Cotton Leaf Curl Disease (CLCuD)	Orthogroup 2 (OG2)	Upregulated in resistant/tolerant lines	Virus resistance (validated by VIGS)
Asparagus officinalis vs. A. setaceus [18]	Phomopsis asparagi infection	Preserved NLR orthologs	Unchanged or downregulated in susceptible A. officinalis	Loss of responsive expression linked to susceptibility
Salvia miltiorrhiza [28] [63]	Hormonal and Abiotic Stress	Specific SmNBS-LRRs	Modulated by plant hormones	Promoter contains related cis-elements; linked to secondary metabolism
Nicotiana tabacum [62]	Black shank and Bacterial wilt	Multiple NBS genes	Differential expression	Key disease resistance genes identified

Functional Validation: From Transcriptional Profile to Physiological Role

Key Experimental Validation Techniques

Identifying differentially expressed NBS genes is a critical first step, but establishing their functional role requires direct experimental validation.

• Virus-Induced Gene Silencing (VIGS): This powerful technique was used to silence a candidate NBS gene (GaNBS from OG2) in resistant cotton. The resulting increase in viral titer confirmed the gene's essential role in resistance to Cotton Leaf Curl Disease [14].
• Heterologous Expression: The function of an NBS gene can be tested by expressing it in a heterologous system. For example, expressing a maize NBS-LRR gene in Arabidopsis improved its resistance to Pseudomonas syringae [62].
• Protein-Ligand and Protein-Protein Interaction Studies: In silico analyses, such as molecular docking, can predict strong interactions between putative NBS proteins and pathogen effectors (e.g., with cotton leaf curl virus core proteins) or signaling molecules like ADP/ATP, providing mechanistic insights [14].

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Reagents for Transcriptomic and Functional Analysis of NBS Genes

Reagent / Solution	Function / Application	Example Use Case
TRIzol/CTAB Buffer	High-quality total RNA extraction from plant tissues.	RNA extraction for transcriptome sequencing in Euterpe edulis and cotton [14] [65].
Illumina TruSeq Stranded RNA Library Prep Kit	Preparation of sequencing-ready cDNA libraries.	Standardized library construction for RNA-seq [65].
RNase-Free DNase I	Removal of genomic DNA contamination from RNA samples.	Essential step in RNA cleanup prior to library prep or RT-qPCR [65].
VIGS Vectors (e.g., TRV-based)	Functional characterization through post-transcriptional gene silencing.	Silencing of GaNBS in cotton to validate virus resistance function [14].
Phytohormones (e.g., JA, SA, ABA)	Treatment solutions to simulate biotic/abiotic stress signaling.	Used to probe the responsiveness of NBS gene promoters to specific defense hormones [28] [63].
2-(Azido-PEG3-amido)-1,3-bis(NHS Ester)	2-(Azido-PEG3-amido)-1,3-bis(NHS Ester), MF:C26H38N6O14, MW:658.6 g/mol	Chemical Reagent
7b-Hydroxy Cholesterol-d7	7b-Hydroxy Cholesterol-d7, MF:C27H46O2, MW:409.7 g/mol	Chemical Reagent

Integrated Signaling Pathways in NBS-Mediated Immunity

The signaling pathways activated by NBS-LRR genes involve specific recognition, nucleotide-dependent conformational changes, and downstream signaling cascades.

Diagram 2: NBS-LRR mediated immunity signaling pathway. Pathogen effector recognition triggers nucleotide exchange and activation, leading to downstream signaling and defense responses.

The NBS-LRR protein activation mechanism is conserved. In the resting state, the NBS domain is bound to ADP. Upon pathogen effector recognition, often mediated by the LRR domain, a conformational change occurs, promoting the exchange of ADP for ATP. This ATP-bound state activates the protein, enabling it to initiate downstream signaling [63] [5]. For TNLs, this frequently involves the lipase-like proteins EDS1 and PAD4, which form a complex with helper RNLs (e.g., ADR1) to amplify the immune signal, ultimately leading to the hypersensitive response and systemic acquired resistance [63].

Transcriptomic profiling solidifies the NBS-LRR gene family's role as a cornerstone of plant immunity while revealing its complex regulation and broader functional repertoire. Benchmarking studies consistently show that successful resistance is often correlated with the rapid and strong induction of specific NBS genes, a trait that can be lost during domestication [18] or leveraged through breeding. The future of NBS gene research lies in integrating multi-omics data—transcriptomics, proteomics, and metabolomics—to build comprehensive models of immune signaling networks. Furthermore, the application of gene editing technologies to manipulate elite NBS alleles and the exploration of their connections to secondary metabolism in medicinal plants [28] represent promising frontiers for engineering durable stress resilience in crops.

Integrating Multi-Omics Data for a Holistic Resistance Gene View

Plant disease resistance is a complex trait governed by intricate molecular networks, with Nucleotide-Binding Site Leucine-Rich Repeat (NBS-LRR) genes serving as critical components of the plant immune system. The comprehensive benchmarking of novel NBS genes against established resistance genes requires a multi-faceted approach that integrates diverse biological data layers. Recent advances in multi-omics technologies have enabled researchers to move beyond single-dimensional analyses toward a more holistic understanding of resistance gene function, evolution, and interaction. This paradigm shift allows for the systematic characterization of resistance genes across genomic, transcriptomic, epigenomic, and metabolomic dimensions, providing unprecedented insights into plant defense mechanisms. The integration of these complementary data types through sophisticated computational frameworks represents a transformative approach in plant immunity research, facilitating the identification of key regulatory networks and functional mechanisms that underlie effective pathogen defense [5] [62] [66].

NBS Gene Family Diversity and Comparative Analysis

The NBS-LRR gene family exhibits remarkable diversity across plant species, both in terms of gene count and structural composition. Recent genome-wide studies have systematically characterized these resistance genes in Nicotiana species, revealing significant variation in gene distribution and domain architecture.

Table 1: Comparative Analysis of NBS-LRR Gene Distribution in Nicotiana Species

Species	Genome Type	Total NBS Genes	TNL-Type	CNL-Type	NL-Type	TN-Type	CN-Type	N-Type
N. benthamiana	Diploid	156	5	25	23	2	41	60
N. tabacum	Allotetraploid	603	64	74	-	9	150	306
N. sylvestris	Diploid	344	37	48	-	5	82	172
N. tomentosiformis	Diploid	279	33	47	-	7	65	127

The structural composition of NBS-LRR proteins directly influences their functional mechanisms in pathogen recognition and defense signaling. Typical NBS-LRR proteins containing three complete domains (TNL, CNL, NL) primarily function in direct pathogen detection, while irregular types lacking complete domains often serve as adaptors or regulators in defense signaling pathways [5]. Subcellular localization predictions indicate distinct functional compartments, with 121 NBS-LRRs located in cytoplasm, 33 in plasma membrane, and 12 in nucleus, reflecting their specialized roles in pathogen recognition and signal transduction [5].

Table 2: NBS-LRR Gene Classification by Domain Architecture and Function

Classification	Domain Composition	Representative Genes	Primary Function	Recognition Mechanism
TNL-Type	TIR-NBS-LRR	N gene (TMV resistance)	Pathogen detection	Direct effector recognition
CNL-Type	CC-NBS-LRR	R genes (multiple pathogens)	Pathogen detection	Guardee protein monitoring
NL-Type	NBS-LRR	RPW8-NL variants	Signal transduction	Defense activation
TN-Type	TIR-NBS	Regulatory adaptors	Signal modulation	Complex formation
CN-Type	CC-NBS	Regulatory adaptors	Signal modulation	Complex formation
N-Type	NBS	Regulatory components	Signal regulation	Pathway coordination

Multi-Omics Integration Frameworks and Methodologies

Genomic and Phylogenetic Profiling

The foundation of resistance gene benchmarking begins with comprehensive genomic identification and evolutionary analysis. Current methodologies employ Hidden Markov Model (HMM) searches with PF00931 (NB-ARC domain) profiles against whole-genome sequences, followed by rigorous domain validation through multiple databases [5] [62]. The experimental workflow typically involves:

• Gene Identification: HMMER v3.1b2 with E-value cutoff < 1×10⁻²⁰ and manual verification via Pfam, SMART, and CDD databases [62]
• Domain Architecture Confirmation: NCBI Conserved Domain Database analysis for CC, TIR, and LRR domains with E-values < 0.01 [5]
• Phylogenetic Reconstruction: Multiple sequence alignment using MUSCLE v3.8.31 or Clustal W, followed by maximum-likelihood tree construction in MEGA11 with 1000 bootstrap replicates [5] [62]
• Motif Analysis: MEME suite implementation with motif count set to 10, width 6-50 amino acids for conserved motif discovery [5]
• Gene Structure Analysis: TBtools for exon-intron organization visualization from GFF3 annotation files [5]

This integrated genomic approach enables researchers to classify NBS-LRR genes into distinct phylogenetic clades and identify lineage-specific evolutionary patterns, providing crucial insights into the expansion and diversification of resistance gene families [62].

Transcriptomic and Metabolomic Integration

Advanced multi-omics frameworks leverage dynamic transcriptomic and metabolomic profiling to unravel the complex regulatory networks governing resistance gene expression and function. The integration of these complementary data types enables the construction of comprehensive metabolic regulatory networks that capture system-level responses to pathogen challenge [67]. Key methodological considerations include:

Experimental Design Parameters:

• Temporal sampling across multiple developmental stages and stress conditions
• Field-based cultivation under natural environmental conditions to preserve ecological relevance
• Paired sampling for transcriptome (RNA-Seq) and metabolome (LC-MS/GC-MS) profiling
• Inclusion of diverse ecological niches to enhance network robustness [67]

Computational Integration Pipeline:

• Data Preprocessing: Quality control, normalization, and batch effect correction
• Differential Analysis: Identification of significantly altered genes and metabolites
• Correlation Mapping: Pairwise association between gene expression and metabolite abundance
• Network Construction: Multi-algorithm integration (WGCNA, MixOmics, MOFA) to reconstruct regulatory relationships
• Hub Identification: Centrality analysis to pinpoint key regulatory genes and metabolites [67]

This integrated approach has successfully identified critical transcriptional hubs, including NtMYB28 that promotes hydroxycinnamic acids synthesis by modifying Nt4CL2 and NtPAL2 expression, and NtERF167 that amplifies lipid synthesis via NtLACS2 activation [67]. These regulatory nodes represent promising targets for metabolic engineering of enhanced disease resistance.

Machine Learning and Predictive Modeling

The integration of multi-omics data with machine learning (ML) approaches represents a cutting-edge frontier in resistance gene research. ML models excel at capturing non-linear relationships and complex interactions prevalent in high-dimensional biological data, enabling more accurate prediction of resistance mechanisms and breeding values [66]. Key implementation strategies include:

Data Processing and Feature Engineering:

• Genomic data encoding as numeric allele counts (0,1,2) for SNP markers
• Transcriptomic data quantification as raw counts or FPKM/TPM values
• Metabolomic data normalization and scaling
• Feature selection to reduce dimensionality and mitigate overfitting [66]

Model Selection and Training:

• Random Forests: Handle heterogeneous data types and identify feature importance
• Support Vector Machines: Effective for high-dimensional classification problems
• Neural Networks: Capture complex non-linear relationships across omics layers
• Ensemble Methods: Combine multiple models to improve prediction accuracy [66]

Validation Framework:

• Cross-validation strategies to assess model performance
• Independent validation cohorts to test generalizability
• Experimental confirmation of predicted resistance mechanisms [66]

This approach has demonstrated particular utility in predicting polygenic resistance traits, where traditional genome-wide association studies often fail to capture the complex interactions between multiple genes and environmental factors [66].

Experimental Protocols for Key Analyses

Genome-Wide NBS-LRR Identification and Characterization

A standardized protocol for comprehensive identification and characterization of NBS-LRR genes has been established through recent studies in Nicotiana species [5] [62]:

Step 1: Sequence Retrieval and Quality Assessment

• Download genome assembly and annotated protein sequences from designated repositories
• Assess genome quality using contiguity metrics (N50, scaffold number) and BUSCO completeness scores
• For tobacco genomes, access data from Zenodo (accession numbers 8256256, 8256252, 8256254) [62]

Step 2: HMM-Based Gene Identification

• Perform HMMER v3.1b2 search with PF00931 (NB-ARC domain) model
• Apply E-value cutoff < 1×10⁻²⁰ for initial identification
• Extract protein sequences of candidate genes using TBtools [5]

Step 3: Domain Validation and Classification

• Confirm domain architecture using Pfam (TIR: PF01582; LRR: PF00560, PF07723, PF07725, PF12779, PF13306, PF13516, PF13855, PF14580, PF03382, PF01030, PF05725)
• Validate coiled-coil domains via NCBI Conserved Domain Database
• Classify genes into subfamilies based on domain composition [62]

Step 4: Evolutionary and Structural Analysis

• Perform multiple sequence alignment using MUSCLE v3.8.31 or Clustal W
• Construct phylogenetic trees using maximum likelihood method in MEGA11 with 1000 bootstrap replicates
• Identify conserved motifs using MEME suite with default parameters [5]

Multi-Omics Network Construction and Hub Gene Validation

The integration of transcriptomic and metabolomic data enables the construction of comprehensive regulatory networks underlying disease resistance [67]:

Step 1: Experimental Design and Sample Collection

• Conduct field trials across multiple ecological regions with distinct environmental conditions
• Implement synchronized topping strategy to standardize developmental stages
• Collect leaf samples at multiple timepoints following treatment or pathogen challenge
• Preserve samples immediately in liquid nitrogen for parallel transcriptome and metabolome analysis [67]

Step 2: Omics Data Generation

• Transcriptomics: RNA extraction, library preparation, and Illumina sequencing (≥20 million reads per sample)
• Metabolomics: Metabolite extraction followed by LC-MS and GC-MS analysis in both positive and negative ionization modes
• Data Processing: Quality control, normalization, and batch effect correction using platform-specific protocols [67]

Step 3: Network Construction and Integration

• Calculate pairwise correlations between gene expression and metabolite abundance
• Implement multi-algorithm integration (WGCNA, MixOmics) to reconstruct regulatory networks
• Identify hub genes through centrality measures (degree, betweenness, eigenvector centrality)
• Validate network robustness through permutation testing and cross-validation [67]

Step 4: Functional Validation of Candidate Genes

• Implement virus-induced gene silencing (VIGS) or CRISPR-Cas9 mediated mutagenesis
• Conduct pathogen challenge assays to quantify resistance phenotypes
• Measure downstream metabolite production to confirm predicted regulatory relationships
• Perform transgenic complementation to verify gene function [67]

Visualization of Multi-Omics Integration Pipeline

Table 3: Essential Research Reagents and Computational Tools for Multi-Omics Resistance Gene Research

Category	Resource/Tool	Specific Application	Key Features
Genomic Databases	Pfam Database	NBS domain identification (PF00931)	Curated HMM profiles for domain prediction
	NCBI CDD	Coiled-coil domain validation	Conserved domain analysis with e-value statistics
	Ensembl Plants	Genome browsing and annotation	Comparative genomics across plant species
Bioinformatics Tools	HMMER v3.1b2	Domain-based gene identification	Hidden Markov Model search with statistical rigor
	MEME Suite	Conserved motif discovery	Pattern discovery in protein sequences
	TBtools	Genomic data visualization	User-friendly interface for multiple analyses
Multi-Omics Integration	MOVICS Package	Multi-omics clustering	Integrative subtype identification using 10 algorithms
	AnnDictionary	LLM-assisted cell type annotation	Large language model integration for single-cell data
	MixOmics	Multi-omics data integration	R package for multivariate analysis
Experimental Validation	VIGS Vectors	Functional gene validation	Virus-induced gene silencing for rapid testing
	CRISPR-Cas9 Systems	Gene editing and functional analysis	Precise genome modification for mechanism studies
	Tempus xT/RS Assays	Targeted DNA/RNA sequencing	Clinical-grade multi-omics profiling

The integration of multi-omics data represents a paradigm shift in resistance gene research, enabling a comprehensive understanding of NBS-LRR gene function within the broader context of plant immune networks. By combining genomic identification, transcriptomic profiling, metabolomic analysis, and advanced computational integration, researchers can now benchmark novel resistance genes against established references with unprecedented precision. The frameworks and methodologies outlined here provide a roadmap for systematic resistance gene characterization, from initial discovery to functional validation and breeding application. As multi-omics technologies continue to evolve, particularly in single-cell resolution and spatial transcriptomics, our ability to decipher the complex interactions within plant immune systems will dramatically improve. The integration of machine learning approaches will further enhance predictive capabilities, accelerating the development of durable disease resistance in crop plants through targeted genetic improvement strategies.

Overcoming Challenges in NBS Gene Benchmarking and Analysis

Addressing Annotation Challenges in Complex, Clustered Genomic Regions

Complex, clustered genomic regions present significant challenges for accurate gene annotation, particularly for large and diverse gene families like nucleotide-binding site (NBS) genes that encode crucial plant disease resistance proteins. These genomic areas are characterized by high sequence similarity between paralogs, structural complexity, and frequent tandem duplications that complicate accurate gene prediction, annotation, and functional characterization [14]. The NBS gene family exemplifies these challenges, with members distributed unevenly across chromosomes, often concentrated at chromosome ends in cluster arrangements [15] [68]. More than 22% of NBS genes in blueberry appear together on the same scaffold with at least one other NBS gene, while the remainder are organized as singletons [68]. Similar clustering patterns are observed in Akebia trifoliata, where 64 mapped NBS candidates were unevenly distributed on 14 chromosomes, with 41 located in clusters and 23 as singletons [15].

Current genomic annotation pipelines frequently struggle with these regions due to several inherent difficulties. The presence of nearly identical paralogs can lead to misassembly, where sequences from different genes are incorrectly merged or separated. Domain architecture variations within gene families introduce additional complexity, as evidenced by the identification of 168 different classes of NBS-domain-containing genes across 34 plant species [14]. The limitations of automated annotation are particularly pronounced in non-model organisms and species with incomplete genomic resources, where the lack of comprehensive transcriptomic data and experimental validation hinders accurate gene model prediction [69]. These challenges necessitate specialized approaches for accurate annotation and functional interpretation of genes within complex genomic regions.

Comparative Analysis of NBS Gene Annotation Across Species

Diversity in NBS Gene Family Size and Organization

The NBS gene family exhibits remarkable diversity in size and organization across plant species, reflecting different evolutionary paths and adaptation strategies. Table 1 summarizes the quantitative variation in NBS genes across recently studied species, highlighting differences in gene counts, architectural classes, and clustering patterns.

Table 1: Comparative Analysis of NBS Genes Across Plant Species

Species	Total NBS Genes	Key Subfamilies	Clustered Genes	Tandem Duplications	Study Year
Akebia trifoliata	73	50 CNL, 19 TNL, 4 RNL	41/64 (64.1%)	33 genes	2021 [15]
Salvia miltiorrhiza	196	61 CNL, 1 RNL, 2 TIR	Not specified	Not specified	2025 [11]
Blueberry	106	11 TNL, 86 nTNL	>22%	18 gene families	2018 [68]
Nicotiana tabacum	603	45.5% NBS-only, 23.3% CC-NBS	Not specified	Significant WGD contribution	2025 [7]
34 Plant Species	12,820	168 domain architecture classes	603 orthogroups	Tandem duplications in core OGs	2024 [14]

This comparative analysis reveals several important patterns. First, the number of NBS genes varies dramatically, from 73 in Akebia trifoliata to 12,820 across 34 species [14] [15]. Second, the distribution of NBS genes across the CNL, TNL, and RNL subfamilies differs substantially between species, with some like Salvia miltiorrhiza showing a notable reduction in TNL and RNL members [11]. Third, clustering appears to be a common organizational principle, though the extent varies between species. These differences highlight the need for annotation approaches that can accommodate species-specific characteristics while enabling cross-species comparisons.

Technical Challenges in Annotating Complex NBS Regions

Annotation of complex NBS regions faces multiple technical hurdles that affect accuracy and completeness:

• Domain Architecture Complexity: The identification of 168 domain architecture classes encompassing both classical and species-specific structural patterns demonstrates the extensive diversity that annotation pipelines must capture [14]. These include not only classical patterns (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) but also unusual configurations (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) that may be missed by standard annotation tools.

• Sequence Similarity and Paralogy: High sequence similarity between paralogous genes in clusters complicates both assembly and annotation. In Nicotiana species, whole-genome duplication significantly contributed to NBS gene family expansion, creating additional paralogy challenges [7].

• Incomplete Genomic Resources: For non-model organisms like Dalbergia sissoo, the lack of genomic sequences necessitates alternative approaches such as transcriptome probing to identify resistance gene analogs [69].

• Variant Interpretation: As noted in biomedical genomics, variant interpretation remains challenging, with current tools dramatically underserving the majority of human disease [70]. Similar limitations apply to plant genomics, where the functional impact of sequence variants in NBS genes is difficult to predict.

These technical challenges require specialized methodologies and integrated approaches for accurate annotation, as discussed in the following section.

Experimental Protocols for Annotation Validation

Integrated Genome-Wide Identification Pipeline

Comprehensive identification of NBS genes requires an integrated approach combining multiple bioinformatic tools and experimental validation. The following workflow, implemented in recent studies, provides a robust framework for annotation of complex resistance gene regions:

Diagram: NBS Gene Identification and Annotation Workflow

This workflow begins with comprehensive data collection from genome databases (NCBI, Phytozome, Plaza) [14]. The initial identification employs Hidden Markov Model (HMM) searches using the PF00931 (NB-ARC domain) model from the Pfam database with stringent e-value thresholds (1.1e-50) [14] [7]. Domain architecture is then validated using multiple resources including NCBI Conserved Domain Database (CDD), Pfam, and Coiled-coil prediction tools with a threshold value of 0.5 [15] [7]. Classification into subfamilies (TNL, CNL, RNL) follows established criteria based on N-terminal domains [15]. Cluster analysis examines chromosomal distribution and gene arrangements, while expression validation utilizes RNA-seq data and functional tests like virus-induced gene silencing (VIGS) [14]. Finally, functional analysis investigates protein-ligand interactions and protein-protein interactions to confirm predicted functions [14].

For species lacking comprehensive genomic sequences, such as Dalbergia sissoo, researchers have developed a targeted transcriptome probing approach using degenerate oligonucleotide-primed reverse transcription PCR (DOP-rtPCR) [69]. This method targets conserved regions of NBS-LRR genes to identify resistance gene analogs expressed under disease stress conditions:

• Plant Material Preparation: Sample collection from resistant and susceptible individuals under disease challenge conditions.
• RNA Extraction and cDNA Synthesis: Total RNA isolation using commercial kits, followed by cDNA synthesis with reverse transcriptase.
• Degenerate Primer Design: Design primers targeting conserved NBS domains including kinase P-loop, kinase-2, kinase-3A, and hydrophobic GLPL motifs.
• DOP-rtPCR Amplification: PCR amplification using degenerate primers, product analysis on agarose gels, and excision of differentially expressed amplicons.
• Cloning and Sequencing: TA cloning of amplicons followed by Sanger sequencing.
• In silico Characterization: Bioinformatic analysis of sequences for conserved domains, physicochemical characteristics, subcellular localization, and protein structure prediction [69].

This protocol enables identification of resistance gene analogs even in the absence of complete genomic sequences, making it particularly valuable for non-model organisms and species with limited genomic resources.

Research Reagent Solutions for Annotation Challenges

Table 2: Essential Research Reagents and Tools for Annotation of Complex Genomic Regions

Category	Specific Tools/Databases	Function in Annotation Process	Application Example
Domain Databases	Pfam (PF00931), NCBI CDD, InterPro	Identification of conserved protein domains	NB-ARC domain verification [14] [7]
HMM Tools	HMMER v3.1b2	Hidden Markov Model searches for gene family identification	Initial NBS gene discovery [15] [7]
Genomic Databases	NCBI, Phytozome, Plaza	Source of genome assemblies and annotations	Data collection for 34 plant species [14]
Variant Databases	ClinVar, Franklin, VarSome	Pathogenic variant interpretation and classification	Manual review of flagged variants [71]
Expression Tools	RNA-seq, DOP-rtPCR	Expression validation and transcriptome profiling	Differential expression analysis under stress [14] [69]
Functional Validation	VIGS, Protein-ligand interaction	Experimental validation of gene function	Silencing of GaNBS (OG2) in cotton [14]

These research reagents and tools form the foundation for comprehensive annotation of complex genomic regions. The selection of appropriate tools depends on the specific research context, available genomic resources, and experimental goals. For well-annotated model species, automated pipelines combining HMM searches with domain verification may be sufficient, while non-model organisms may require additional transcriptome probing and manual curation.

The annotation of complex, clustered genomic regions remains challenging due to the inherent complexity of gene families like NBS resistance genes. Current approaches successfully identify substantial diversity in these genes across species, with 12,820 NBS-domain-containing genes discovered across 34 species and classified into 168 distinct architectural classes [14]. However, accurate annotation requires integrated methodologies combining computational prediction, comparative genomics, and experimental validation.

Future directions should focus on developing more sophisticated multi-modal annotation systems that can better handle the complexities of these regions. As noted in variant annotation research, comprehensive approaches must incorporate systems biology, reflecting how biological functions typically arise from networks of interacting variants shaped by background genetic architecture [70]. For plant resistance gene annotation, this means developing frameworks that can integrate genomic, transcriptomic, proteomic, and functional data to create more accurate and comprehensive annotations. Such integrated approaches will be essential for fully leveraging the potential of disease resistance genes in crop improvement and sustainable agriculture.

Managing Structural Variation and Paralog Interference in Genotyping

Accurate genotyping is fundamental to modern genetic research and clinical diagnostics, yet reliable variant calling in genomic regions rich in structural variation (SV) and paralogous sequences remains a significant technical challenge. Structural variation, which encompasses genomic alterations involving 50 base pairs or more, including deletions, duplications, insertions, inversions, and translocations, accounts for more base pair differences between human genomes than single nucleotide polymorphisms combined [72]. The repetitive nature of these regions and the presence of paralogous genes—genes related by duplication within a genome—create interference that complicates sequencing read alignment and variant interpretation. This challenge is particularly acute in studies of disease resistance genes, such as the nucleotide-binding site-leucine rich repeat (NBS-LRR) family, which often reside in dynamic genomic regions characterized by frequent duplication events and complex evolutionary histories [14] [73].

The context of benchmarking novel NBS genes against known resistance genes brings these challenges into sharp focus. NBS-LRR genes represent the largest family of plant resistance genes, playing crucial roles in pathogen recognition and defense activation [15] [74]. Their genomic organization is characterized by tandem duplication events that create clusters of similar sequences, fostering both functional diversification and analytical complications [73] [74]. This article systematically compares experimental and computational approaches for managing structural variation and paralog interference in genotyping, providing researchers with practical guidance for generating reliable data in complex genomic contexts.

Structural Variation Detection: Methodological Comparisons

Technological Platforms and Their Applications

Table 1: Comparison of Structural Variation Detection Methods

Method Category	Specific Technologies	Variant Types Detected	Key Advantages	Key Limitations	Recommended Use Cases
Array-based	Array CGH, SNP microarrays	Copy number variations (CNVs)	Established protocols, cost-effective for large cohorts	Limited resolution (>500 bp), reference-dependent, cannot detect balanced SVs	Large-scale CNV screening, clinical diagnostics [72]
Short-read sequencing	Illumina NovaSeq, NextSeq	SNVs, indels, small CNVs	High accuracy for single nucleotide variants, well-established pipelines	Limited phasing information, poor resolution in repetitive regions	Variant discovery in unique genomic regions [75] [76]
K-mer based approaches	Genome Content Profiling (GCP)	Repeat abundance variation, CNVs	Reference-free, captures variation absent from reference	Computational intensive, novel analysis pipelines	Population-level repeat dynamics, evolutionary studies [77]

The detection of structural variation has evolved significantly with technological advancements. Early approaches relied heavily on microarray technologies, which infer copy number gains or losses through comparative hybridization. Array comparative genomic hybridization (array CGH) and SNP microarrays have been the workhorses of CNV discovery and genotyping, with detection limits typically requiring signals from 3-10 consecutive probes [72]. While these platforms successfully identified numerous CNVs, their resolution limitations and inability to detect balanced structural variations (those without copy number change) like inversions or translocations restricted their utility.

Next-generation sequencing technologies transformed SV detection by enabling base-pair resolution. Short-read sequencing platforms like Illumina's NovaSeq and NextSeq systems facilitate the identification of single nucleotide variants and small insertions/deletions with high accuracy [75]. However, their limited read length (typically 75-150 bp) presents challenges in repetitive regions and for phasing variations. As noted in a large-scale ALS study, even whole-genome sequencing at 25x coverage requires complementary approaches to fully characterize structural variants [76].

Emerging approaches like K-mer based methods circumvent reference bias by analyzing short sequence subsequences without alignment to a reference genome. This reference-free approach successfully identified hypervariable regions contributing to major differences in repeat abundance in Arabidopsis thaliana, demonstrating particular utility for studying repetitive sequence dynamics [77].

Experimental Design Considerations for NBS Gene Studies

For researchers focusing on NBS gene families, specific experimental considerations apply. The highly duplicated nature of these genes necessitates sequencing strategies that overcome paralog interference. As revealed in a comprehensive analysis of 34 plant species, NBS-domain-containing genes exhibit remarkable diversification with both classical and species-specific structural patterns [14]. This diversity stems primarily from tandem and dispersed duplication events, creating analytical challenges during genotyping [15] [14].

Long-read sequencing technologies (PacBio, Oxford Nanopore) effectively resolve complex regions but were notably underrepresented in the surveyed literature. Their increasing accessibility and improving accuracy make them valuable additions to the methodological toolkit for managing structural variation, particularly for generating high-quality assemblies of NBS gene clusters.

Paralog Interference in NBS Gene Families: Patterns and Solutions

Genomic Organization of NBS Genes

The NBS-LRR gene family exhibits distinctive genomic organization patterns that directly contribute to paralog interference challenges. These genes are frequently distributed unevenly across chromosomes, with a predominant presence at chromosome ends and arrangement in clusters [15] [73]. In eggplant, for instance, researchers identified 269 NBS genes with uneven distribution across chromosomes, predominantly clustering on chromosomes 10, 11, and 12 [74]. Similarly, in Akebia trifoliata, 64 mapped NBS candidates showed uneven distribution, with most located in clusters at chromosome ends [15].

This clustered arrangement results from specific evolutionary mechanisms. Tandem and dispersed duplications are the primary forces driving NBS gene expansion, producing multigene families with high sequence similarity that complicates genotyping [15] [14]. In sugarcane, whole genome duplication, gene expansion, and allele loss significantly influence NBS-LRR gene numbers, with whole genome duplication likely being the primary driver [73]. Evolutionary analyses reveal progressive positive selection on NBS-LRR genes, further contributing to their diversification and analytical complexity [73].

Computational Strategies for Managing Paralog Interference

Table 2: Computational Approaches for Paralog Interference in Genotyping

Method Class	Specific Tools/Approaches	Underlying Principle	Effectiveness for NBS Genes	Implementation Considerations
Read alignment-based	BWA-MEM, elPrep	Reference-based alignment with duplicate removal	Limited in complex clusters	Requires optimized parameters for repetitive regions [75]
K-mer based	Genome Content Profiling	Reference-free variant detection	High for repeat abundance	Computationally intensive, novel statistical approaches [77]
Orthology-based	OrthoFinder, OrthoMCL	Gene clustering across species	High for evolutionary studies	Dependent on multiple genome assemblies [14]
Variant calling	HaplotypeCaller, GenotypeGVCFs	Statistical genotype likelihoods	Moderate with careful filtering	Requires stringent quality thresholds [75]

Advanced computational strategies help mitigate paralog interference during genotyping. K-mer based approaches have demonstrated particular utility, with one study developing a method using 12-mer abundances to detect copy number variation with high accuracy (R²=0.98 in simulations) [77]. This reference-free approach enables detection of variation absent from reference genomes, a common issue with rapidly evolving NBS genes.

Orthology-based methods provide another powerful strategy. In a comprehensive analysis of NBS genes across 34 plant species, researchers used OrthoFinder to identify 603 orthogroups, including both core (commonly shared) and unique (species-specific) groups [14]. This evolutionary framework facilitates the identification of orthologous relationships despite paralogous expansion, aiding accurate genotyping in comparative studies.

For variant calling in duplicated regions, stringent filtering is essential. The BabyDetect study implemented strict quality control thresholds for sequencing, coverage, and contamination to ensure reliability in their newborn screening panel [75]. Their approach minimized false positives by focusing on known pathogenic/likely pathogenic variants with strong clinical validity, a strategy that can be adapted for NBS gene studies.

Experimental Protocols for NBS Gene Benchmarking

Genome-Wide Identification and Classification

A standardized protocol for NBS gene identification has emerged across multiple studies, comprising four key steps:

Step 1: Initial Candidate Identification Retrieve protein sequences from genomic databases and perform BLASTP analysis against the NB-ARC domain (PF00931) using HMMER with E-value thresholds of 1.0-10⁻²⁰ [15] [74]. Combine results from HMMsearch and BLAST to ensure comprehensive coverage while removing redundant entries.

Step 2: Domain Architecture Analysis Validate the presence of NBS domains using Pfam and SMART databases with E-value thresholds of 10⁻⁴ [74]. Classify genes into subfamilies (TNL, CNL, RNL) based on N-terminal domains: TIR (PF01582) for TNL, RPW8 (PF05659) for RNL, and coiled-coil domains identified using COILS with threshold 0.9 [15] [73].

Step 3: Genomic Distribution Mapping Extract chromosomal positions from genome annotation files and visualize distributions using tools like TBtools [74]. Identify clustering patterns and correlate with known duplication events.

Step 4: Evolutionary and Expression Analysis Perform phylogenetic analysis using OrthoFinder and multiple sequence alignment with MAFFT [14]. Assess expression patterns under stress conditions using RNA-seq data and qRT-PCR validation [74].

This protocol was successfully applied in eggplant, identifying 269 SmNBS genes (231 CNLs, 36 TNLs, and 2 RNLs) with uneven chromosomal distribution and nine candidates showing differential expression under bacterial wilt stress [74].

Workflow for Managing Structural Variation in Genotyping

The following diagram illustrates a comprehensive workflow for managing structural variation in genotyping experiments, particularly for complex gene families:

Figure 1: Integrated Workflow for Structural Variation Genotyping

Signaling Pathways in NBS-LRR Gene Function

The molecular function of NBS-LRR genes involves conserved signaling pathways that can be targeted in functional validation experiments:

Figure 2: NBS-LRR Signaling Pathways in Plant Immunity

Benchmarking Data: Performance Comparisons Across Methods

Empirical Performance in Disease Association Studies

Table 3: Performance Comparison of Genotyping Methods in Disease Studies

Study Context	Methods Employed	Key Findings	Structural Variants Identified	Clinical/Functional Associations
ALS whole-genome study (n=6,195)	Illumina WGS (25x coverage), Manta, Pindel	Three genes with SV associations after QC	C9orf72 expansion (OR=28.1), VCP inversion (OR=2.33), ERBB4 insertion (OR=2.55)	Younger onset age, specific onset patterns [76]
Sugarcane disease resistance	Comparative genomics, transcriptomics	More differentially expressed NBS-LRR genes from S. spontaneum	Allele-specific expression under leaf scald	125 NBS-LRR genes responding to multiple diseases [73]
Cotton leaf curl disease	RNA-seq, VIGS validation	Expression upregulation of OG2, OG6, OG15 orthogroups	Genetic variation between susceptible and tolerant accessions	GaNBS (OG2) silencing increased virus susceptibility [14]
Eggplant bacterial wilt	Genome-wide identification, qRT-PCR	269 SmNBS genes identified, 9 differentially expressed	EGP05874.1 as resistance candidate	Differential expression in resistant vs. susceptible lines [74]

Large-scale studies provide compelling evidence for the clinical and functional relevance of comprehensive structural variation detection. In a whole-genome sequencing study of amyotrophic lateral sclerosis (ALS) involving 6,580 samples, researchers identified three genes with structural variations significantly associated with disease risk after rigorous quality control [76]. The C9orf72 repeat expansion showed the strongest effect (odds ratio 28.1), but VCP inversions and ERBB4 insertions also contributed significantly to disease risk and phenotypic characteristics. This study demonstrated that individuals with these structural variations experienced younger ages of onset (3-3.5 years earlier) and distinct patterns of disease manifestation [76].

In plant systems, similar approaches revealed the functional significance of NBS gene diversity. In sugarcane, transcriptome data from multiple disease challenges revealed that more differentially expressed NBS-LRR genes derived from Saccharum spontaneum than from Saccharum officinarum in modern cultivars, with the proportion significantly higher than expected [73]. This finding demonstrates the critical contribution of specific lineages to disease resistance in polyploid genomes.

Table 4: Essential Research Reagents and Resources for SV Genotyping

Resource Category	Specific Examples	Function/Application	Key Characteristics
Sequencing Platforms	Illumina NovaSeq/NextSeq, PacBio, Oxford Nanopore	DNA/RNA sequencing	Varying read lengths, error profiles, and throughput options
Bioinformatics Tools	BWA-MEM, OrthoFinder, Manta, Pindel, MEME Suite	Read alignment, orthology analysis, SV detection, motif discovery	Specialized algorithms for specific variant types
Reference Databases	Phytozome, EnsemblPlants, NCBI, Plaza	Genomic context, comparative analysis	Species-specific genomic references and annotations
Experimental Validation	VIGS, qRT-PCR, CRISPR screens	Functional confirmation of genotype-phenotype relationships	Targeted manipulation of candidate genes

Managing structural variation and paralog interference in genotyping requires integrated methodological approaches that combine complementary technologies. No single method currently resolves all challenges, particularly for complex gene families like NBS-LRR genes. Array-based methods offer cost-effective solutions for large cohorts but lack resolution for breakpoint mapping and balanced variant detection. Short-read sequencing provides base-pair resolution for single nucleotide variants and small indels but struggles with repetitive regions and phasing. Emerging approaches like K-mer analysis and long-read sequencing show promise for resolving complex regions but require specialized analytical pipelines and higher resources.

For researchers benchmarking novel NBS genes against known resistance genes, the evidence supports a tiered approach: initial broad characterization using array-based or short-read sequencing technologies followed by targeted deep investigation of candidate regions with long-read technologies and orthogonal validation. The consistent finding of clustered genomic organization and birth-and-death evolution in NBS genes across plant species underscores the necessity of methods that account for rapid sequence divergence and paralogous interference.

The progressive positive selection observed in NBS-LRR genes across species [73] highlights the dynamic nature of these gene families and the corresponding need for genotyping approaches that capture both standing variation and ongoing diversification. By implementing the comparative frameworks and experimental protocols outlined in this review, researchers can advance toward more accurate genotyping in complex genomic regions, accelerating the discovery and functional characterization of novel disease resistance genes across biological systems.

The identification and characterization of Nucleotide-Binding Site-Leucine Rich Repeat (NBS-LRR) genes represent a critical frontier in plant disease resistance research. As the largest class of plant resistance genes, NBS-LRR genes play a pivotal role in effector-triggered immunity, forming an essential component of the plant immune system [14] [73]. The comparative analysis and benchmarking of novel NBS genes against established resistance genes require sophisticated bioinformatics pipelines capable of handling complex genomic data with precision and reproducibility. However, the effectiveness of these pipelines hinges on two fundamental aspects: the strategic selection of biological databases and the meticulous tuning of analytical parameters.

The principle of "garbage in, garbage out" is particularly pertinent in bioinformatics, where the quality of input data directly determines the reliability of scientific conclusions [78]. This challenge is compounded by the complexity of parameter-rich bioinformatics tools, where suboptimal settings can lead to inaccurate gene annotations, missed discoveries, or false positives. A 2022 survey of clinical sequencing labs found that up to 5% of samples had labeling or tracking errors before corrective measures were implemented, highlighting the critical importance of robust data quality control [78].

This guide provides a comprehensive framework for optimizing bioinformatics pipelines specifically for NBS gene research, comparing database options and parameter optimization methodologies through experimental data, and offering practical protocols for researchers engaged in resistance gene benchmarking.

Database Selection for NBS Gene Research

The foundation of any robust NBS gene analysis pipeline rests on the strategic selection of genomic databases. These resources provide the reference data and annotations essential for accurate gene identification, classification, and evolutionary analysis. Based on current literature, the following databases have proven essential for comprehensive NBS gene research.

Table 1: Core Databases for NBS Gene Research

Database Category	Specific Database	Primary Application in NBS Research	Key Strengths
General Genomic Repositories	NCBI	Genome assemblies, raw sequence data	Comprehensive data repository, standardized accessions
	Phytozome	Plant genomics, comparative analysis	Curated plant genomes, evolutionary insights
	Ensembl Plants	Plant-specific genomic annotations	Gene families, comparative genomics
Specialized Plant Resources	Plaza Genome Database	Comparative genomics	Evolutionary studies, ortholog identification
	CottonFGD	Species-specific genomics	Gossypium NBS-LRR analysis [14]
	Cottongen	Species-specific genomics	Cotton genome resources [14]
Disease Resistance Focused	ANNA: Angiosperm NLR Atlas	NLR gene classification	>90,000 NLR genes from 304 angiosperm genomes [14]
	Plant NBS-LRR Gene Database	Custom NBS-LRR resource	Dedicated NBS-LRR analysis platform [73]
Expression Data	IPF Database	RNA-seq data across species	Tissue/stress-specific expression profiles [14]

The integration of data from multiple sources is particularly valuable in NBS gene research. For example, a 2024 study identified 12,820 NBS-domain-containing genes across 34 plant species by integrating data from NCBI, Phytozome, and Plaza databases, revealing significant diversity and several novel domain architecture patterns [14]. Similarly, a 2023 analysis of NBS-LRR genes in sugarcane relied on Saccharum spontaneum and Saccharum officinarum genomes from the Sugarcane Genome database, enabling the discovery that S. spontaneum contributes more disease resistance genes to modern cultivars than expected [73].

Database Selection Impact on Research Outcomes

The choice of databases directly influences the completeness and accuracy of NBS gene identification. Studies consistently demonstrate that multi-database approaches yield more comprehensive results. For instance, research on Dioscorea rotundata identified 167 NBS-LRR genes through integrated domain analysis, with 166 belonging to the CNL subclass and only one to the RNL subclass, with complete absence of TNL genes consistent with other monocots [17]. This finding would be difficult to verify without access to multiple genomic resources for comparative analysis.

The emerging specialty database ANNA (Angiosperm NLR Atlas) exemplifies how curated, taxon-specific resources can enhance research efficiency. With over 90,000 NLR genes from 304 angiosperm genomes, including 18,707 TNL genes, 70,737 CNL genes, and 1,847 RNL genes, this resource provides pre-computed classifications that enable more rapid comparative studies [14]. Such dedicated resources significantly reduce the computational burden of initial gene identification and allow researchers to focus on higher-level comparative analyses.

Parameter Tuning Methodologies

Systematic Approaches to Pipeline Optimization

Parameter tuning in bioinformatics pipelines presents a significant challenge due to the complex interaction effects between parameters and the computational expense of testing combinations. The "doepipeline" methodology, based on Design of Experiments (DoE) principles, provides a systematic framework for addressing this challenge [79]. This approach efficiently navigates parameter spaces through a two-phase process: initial screening using Generalized Subset Designs (GSD) to identify promising regions, followed by iterative optimization using response surface designs to refine numerical parameters.

Table 2: Parameter Tuning Methods Comparison

Method	Key Principles	Best Application Scenarios	Advantages	Limitations
doepipeline (DoE)	Screening + optimization phases; GSD for full space exploration; OLS modeling [79]	Multi-step pipelines with numerous parameters; Resource-intensive workflows	Efficient parameter space exploration; Handles both qualitative and quantitative parameters	Requires well-defined objective function; Computational complexity for very large spaces
Grid Search	Exhaustive search of predefined parameter combinations [80]	Limited parameter sets with small value ranges	Guaranteed to find optimum within search space; Simple implementation	Computationally prohibitive for many parameters; Curse of dimensionality
Tree-based Pipeline Optimization (TPOT)	Genetic programming to evolve optimal pipeline structures [80]	Machine learning pipelines; Feature selection and classification	Automates both model selection and hyperparameter tuning; Discovers novel pipeline combinations	Computationally intensive; Limited interpretability of results
Bayesian Optimization	Probabilistic model of objective function; Focuses on promising regions [80]	Expensive black-box functions with many parameters	Efficient for costly evaluations; Balances exploration and exploitation	Complex implementation; Performance depends on surrogate model

The doepipeline approach has demonstrated effectiveness across multiple bioinformatics applications, including de-novo assembly, scaffolding, k-mer taxonomic classification, and genetic variant calling [79]. In all cases, it identified parameter settings that outperformed default values, highlighting the importance of systematic optimization rather than relying on software defaults or trial-and-error approaches.

Parameter Tuning in NBS Gene Identification

In NBS gene research, parameter selection critically affects identification accuracy. The PfamScan tool with Hidden Markov Models (HMM) is commonly used with specific e-value thresholds (e.g., 1.1e-50) to identify NB-ARC domains [14]. Orthologous group analysis employing tools like OrthoFinder with DIAMOND for sequence similarity and MCL for clustering requires careful parameterization of e-value cutoffs (e.g., 10-5 for intra-species collinearity analysis) [14] [73].

For differential expression analysis of NBS genes under stress conditions, parameters for tools like HISAT2 (alignment) and featureCounts (quantification) must be optimized to ensure accurate measurement of transcript abundance. Studies have successfully employed FPKM normalization and subsequent categorization into tissue-specific, abiotic stress-specific, and biotic stress-specific expression profiles to identify NBS genes responsive to pathogens [14].

Experimental Protocols for Benchmarking NBS Genes

Workflow for Comparative Analysis of NBS Genes

The following workflow diagram illustrates a comprehensive protocol for benchmarking novel NBS genes against known resistance genes:

Detailed Methodological Framework

Genome-Wide Identification of NBS Genes

The initial identification of NBS-domain-containing genes follows a standardized protocol across multiple species. As demonstrated in a 2024 pan-species analysis, researchers should:

• Retrieve genomic data from multiple databases (NCBI, Phytozome, Plaza) for 30+ species representing diverse evolutionary positions [14].
• Identify NBS domains using PfamScan with the Pfam-A.hmm model and strict e-value cutoff (1.1e-50) to ensure only genuine NBS domains are included [14].
• Classify domain architecture using established classification systems that group genes based on similar domain patterns, enabling discovery of both classical (NBS, NBS-LRR, TIR-NBS-LRR) and species-specific structural patterns [14].
• Perform orthologous grouping using OrthoFinder v2.5.1 with DIAMOND for sequence similarity and MCL clustering algorithm, identifying both core orthogroups (shared across species) and unique orthogroups (species-specific) [14].

This approach successfully identified 12,820 NBS genes across 34 species with 168 distinct domain architecture classes, revealing significant diversity and several novel structural patterns [14].

Expression Analysis Under Stress Conditions

To evaluate NBS gene responsiveness to biotic and abiotic stresses:

• Collect RNA-seq data from repositories such as the IPF database, CottonFGD, and NCBI BioProjects, categorizing data into tissue-specific, abiotic stress-specific, and biotic stress-specific expression profiles [14].
• Process RNA-seq data through standardized transcriptomic pipelines to obtain FPKM values, enabling cross-comparison of expression levels [14].
• Identify differentially expressed NBS genes in resistant and susceptible varieties under pathogen challenge, focusing on orthogroups showing consistent upregulation in resistant genotypes.

In cotton leaf curl disease research, this approach revealed specific orthogroups (OG2, OG6, OG15) with putative upregulation in different tissues under various stresses, highlighting their potential role in disease response [14].

Genetic Variation Analysis in Resistant and Susceptible Lines

To identify potentially causal genetic variants in NBS genes:

• Select contrasting genotypes with documented resistance/susceptibility to target pathogens (e.g., Mac7 as tolerant vs. Coker 312 as susceptible for cotton leaf curl disease) [14].
• Identify sequence variants through whole-genome sequencing or targeted resequencing of NBS gene regions.
• Compare variant profiles between resistant and susceptible accessions, focusing on non-synonymous mutations and structural variants that may affect gene function.

Application of this protocol in cotton identified 6,583 unique variants in tolerant Mac7 versus 5,173 in susceptible Coker 312, providing a rich resource for identifying potentially functional polymorphisms [14].

Visualization of NBS Gene Domain Architecture and Evolution

NBS Gene Classification and Evolutionary Relationships

The diversity of NBS gene domain architecture and evolutionary relationships can be visualized through the following diagram:

Key Findings from Comparative NBS Gene Studies

Recent comparative studies of NBS genes across multiple plant species have revealed several important evolutionary patterns:

• Differential expansion: NBS gene families have undergone species-specific expansion, with sugarcane showing substantial increase through whole-genome duplication [73], while yam (Dioscorea rotundata) maintains a moderate repertoire of 167 NBS-LRR genes dominated by the CNL subclass [17].
• Subclass distribution: Monocot species generally lack TNL genes, as evidenced by their absence in D. rotundata [17] and other grass species, while dicots typically contain both TNL and CNL subclasses.
• Cluster organization: A significant proportion of NBS genes are arranged in clusters, with 74% (124 of 167) in D. rotundata located in 25 multigene clusters, primarily driven by tandem duplication events [17].
• Expression patterns: Most NBS genes show low basal expression, with specific upregulation during pathogen challenge, and tissues like tubers and leaves often show relatively higher expression than stems and flowers [17].

Computational Tools and Biological Materials

Table 3: Essential Research Resources for NBS Gene Benchmarking

Category	Resource	Specific Application	Key Features
Bioinformatics Tools	PfamScan	NBS domain identification	HMM-based, strict domain boundary definition
	OrthoFinder	Orthologous group analysis	Determines gene families across species
	MCScanX	Collinearity analysis	Identifies tandem and segmental duplications
	FastQC	Data quality control	Quality metrics for raw sequencing data
	Trimmomatic	Read preprocessing	Adapter removal, quality filtering
	SAMtools	Alignment processing	Variant calling, format conversion
Experimental Validation	VIGS (Virus-Induced Gene Silencing)	Functional validation	Knockdown of candidate NBS genes in resistant plants
	Y2H (Yeast Two-Hybrid)	Protein interaction analysis	Identify NBS protein interactions with pathogen effectors
	Protein-Ligand Docking	Molecular interaction studies	Computational analysis of NBS-ADP/ATP binding [14]
Biological Materials	Resistant/Susceptible Cultivars	Genetic variation studies	Contrasting genotypes for variant identification
	Pathogen Strains	Functional assays	Specific isolates for disease response studies
	RNA-seq Libraries	Expression profiling	Tissue-specific and stress-induced transcriptomes

Optimizing bioinformatics pipelines through strategic database selection and systematic parameter tuning is essential for robust benchmarking of novel NBS genes against known resistance genes. The experimental data presented demonstrates that multi-database approaches incorporating both general genomic repositories and specialized resources yield more comprehensive gene sets, while DoE-based parameter optimization methods consistently outperform default settings across various bioinformatics applications.

The integration of computational predictions with experimental validation through VIGS, protein interaction studies, and genetic variation analysis in resistant/susceptible lines provides a powerful framework for confirming the functional role of candidate NBS genes. As evidenced by recent studies, this integrated approach has successfully identified specific NBS orthogroups responsive to cotton leaf curl disease [14], revealed the disproportionate contribution of Saccharum spontaneum to disease resistance in modern sugarcane cultivars [73], and elucidated the evolutionary mechanisms driving NBS gene expansion across plant species.

The continued refinement of bioinformatics pipelines, coupled with the growing availability of curated plant genomic resources, promises to accelerate the discovery and functional characterization of NBS genes, ultimately enhancing our ability to develop disease-resistant crop varieties through targeted breeding and biotechnology approaches.

Solving Issues with Low Homology and Gene Dropout in Capture Sequencing

In the field of genomics, particularly in the context of benchmarking novel nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes against known resistance genes, researchers face two significant technical challenges: low homology and gene dropout. Low homology complicates the identification and annotation of evolutionarily distant genes using traditional sequence-based methods [81]. Gene dropout, referring to the failure to capture and sequence target regions, reduces the completeness and reliability of sequencing data [82]. This guide objectively compares the performance of various platforms and methodologies designed to overcome these challenges, providing supporting experimental data relevant to researchers, scientists, and drug development professionals working in plant immunity and disease resistance genetics.

Understanding the Core Challenges

The Problem of Low Homology

Low homology presents a substantial barrier in genomic studies aiming to identify novel resistance genes. Traditional sequence alignment methods rely on detectable sequence similarity, which diminishes over evolutionary timescales. While sequence homology is effective for proteins with high sequence similarity (>25%), structural homology often persists even when sequence similarity becomes undetectable [81]. This is particularly relevant for NBS-LRR genes, which constitute the largest group of plant resistance (R) genes and play crucial roles in effector-triggered immunity (ETI) [14]. Over half of all proteins lack detectable sequence homology in standard databases due to distant evolutionary relationships, creating significant annotation gaps in resistance gene studies [81].

The Issue of Gene Dropout in Capture Sequencing

Gene dropout in capture sequencing refers to the failure to adequately sequence specific genomic regions, leading to incomplete data. In whole exome sequencing (WES), which serves as an effective methodology for identifying causative genetic mutations in genomic exon regions, dropout events can result from various factors including capture probe efficiency, hybridization conditions, and sequencing platform performance [82]. The impact of dropouts is particularly pronounced in single-cell RNA sequencing (scRNA-seq), where dropout rates can reach 90% or higher, severely compromising data integrity [83] [84]. While this guide focuses on capture sequencing for NBS-LRR genes, understanding the dropout phenomenon across sequencing modalities provides valuable insights for method development.

Comparative Platform Performance for Minimizing Dropout

Whole Exome Sequencing Platform Comparison

A comprehensive evaluation of four commercially available exome capture platforms on the DNBSEQ-T7 sequencer provides critical performance data for researchers seeking to minimize dropout rates in capture sequencing experiments. The study assessed platforms from BOKE (TargetCap Core Exome Panel v3.0), IDT (xGen Exome Hyb Panel v2), Nanodigmbio (EXome Core Panel), and Twist (Twist Exome 2.0) using standardized library preparation with MGIEasy UDB Universal Library Prep Set and sequencing on DNBSEQ-T7 with PE150 configuration [82].

Table 1: Performance Metrics of Exome Capture Platforms on DNBSEQ-T7

Platform	Target Capture Efficiency	Uniformity of Coverage	Duplicate Rate	GC Bias	Variant Detection Accuracy
BOKE	High	Moderate	Low	Moderate	High
IDT	High	High	Low	Low	High
Nanodigmbio	Moderate	Moderate	Moderate	Moderate	Moderate
Twist	High	High	Low	Low	High

All platforms demonstrated comparable reproducibility and superior technical stability on the DNBSEQ-T7 sequencer. The establishment of a robust workflow for probe hybridization capture compatible with all four commercial exome kits enhanced performance uniformity regardless of probe brand [82]. This standardized protocol, utilizing MGI enrichment reagents (MGIEasy Fast Hybridization and Wash Kit), achieved uniform and outstanding performance across platforms, addressing a key factor in minimizing systematic dropout.

Experimental Protocol for Exome Capture Evaluation

The comparative assessment followed a rigorous experimental design:

• Library Preparation: 72 libraries were prepared from NA12878 reference DNA using MGIEasy UDB Universal Library Prep Set on MGISP-960 Automated System.
• Pre-capture Pooling: Libraries were pooled in 1-plex and 8-plex hybridization arrangements with input masses of 1000ng and 250ng per sample, respectively.
• Hybridization Capture: Four library pools were captured using manufacturer-specific protocols, while four additional pools used standardized MGI enrichment reagents and workflow.
• Post-capture Amplification: 12 PCR cycles using MGIEasy Dual Barcode Exome Capture Accessory Kit.
• Sequencing: All libraries were sequenced on one lane of DNBSEQ-T7 with PE150.
• Bioinformatic Analysis: Processed using MegaBOLT v2.3.0.0 following GATK best practices [82].

Computational Solutions for Low Homology Detection

Remote Homology Detection Tools

For addressing low homology challenges in NBS-LRR gene identification, structural similarity-based approaches outperform traditional sequence-based methods. TM-Vec and DeepBLAST represent advanced deep learning tools that enable remote homology detection without requiring solved protein structures [81] [85].

Table 2: Performance Comparison of Homology Detection Methods

Method	Input Data	Detection Principle	Accuracy at <25% Sequence Identity	Scalability to Large Databases
BLAST	Sequence	Sequence similarity	Low	Moderate
HMMER	Sequence	Profile HMMs	Moderate	Moderate
TM-align	Structure	Structural alignment	High	Low
TM-Vec	Sequence	Structural similarity prediction	High	High
DeepBLAST	Sequence	Structural alignment prediction	High	Moderate

TM-Vec employs a twin neural network trained to predict TM-scores (a metric of structural similarity) directly from protein sequences, achieving a correlation of r=0.97 with actual TM-scores across 1 million held-out protein pairs. Notably, it maintains low prediction error (median error=0.026) even for sequence pairs with less than 0.1% sequence identity, where traditional methods fail [81]. Once TM-Vec identifies structurally similar proteins, DeepBLAST generates structural alignments using a differentiable Needleman-Wunsch algorithm, outperforming traditional sequence alignment methods for remote homologs [85].

Experimental Validation of Remote Homology Detection

The performance evaluation of TM-Vec followed a rigorous methodology:

• Training Data: Approximately 150 million protein pairs from SWISS-MODEL (277,000 unique chains).
• Validation Sets:
  • Held-out pairs from SWISS-MODEL
  • CATH domains clustered at 40% sequence similarity
  • Held-out CATH folds for extrapolation testing
• Benchmarking: Comparison against TM-align, AlphaFold2, OmegaFold, and ESMFold.
• Specialized Testing: Evaluation on bacteriocin classification accuracy [81].

The model demonstrated robust performance on held-out CATH folds (r=0.781, P<1×10⁻⁵, median error=0.042), indicating capability to extrapolate beyond known fold space—a critical requirement for identifying novel NBS-LRR protein folds in benchmarking studies [81].

Integrating Solutions for NBS-LRR Gene Research

Application to NBS-LRR Gene Identification and Benchmarking

The combination of high-performance capture sequencing and advanced remote homology detection provides a powerful framework for benchmarking novel NBS-LRR genes against known resistance genes. NBS-LRR genes encode proteins characterized by distinct domains: an N-terminal Toll/interleukin-1 receptor (TIR) or coiled-coil (CC) domain, a central NB-ARC domain functioning as a molecular switch, and a C-terminal leucine-rich repeat (LRR) involved in pathogen recognition [39]. These genes have significantly expanded in plants through whole-genome duplication (WGD) and small-scale duplication events, with hundreds of copies present in many species [39] [14].

Comprehensive genome-wide analyses across 23 plant species have revealed that WGD, gene expansion, and allele loss significantly impact NBS-LRR gene numbers, with WGD likely being the primary driver in sugarcane [39]. Transcriptome data from multiple sugarcane diseases showed that more differentially expressed NBS-LRR genes derived from S. spontaneum than from S. officinarum in modern cultivars, indicating greater contribution to disease resistance from the wild relative [39]. Such findings highlight the importance of comprehensive capture and accurate homology detection for identifying functional resistance genes.

Experimental Workflow for NBS-LRR Gene Benchmarking

Diagram: Workflow for NBS-LRR Gene Benchmarking

Research Reagent Solutions

Table 3: Essential Research Reagents and Platforms

Category	Specific Product	Function in NBS-LRR Research
Library Prep	MGIEasy UDB Universal Library Prep Set	High-uniformity library construction for reproducible results
Exome Capture	Twist Exome 2.0 Panel	Comprehensive target enrichment with minimal dropout
Exome Capture	IDT xGen Exome Hyb Panel v2	Alternative high-performance capture platform
Sequencing	DNBSEQ-T7 Platform	High-throughput sequencing with low technical variation
Domain Annotation	InterProScan 5.48-83.0	Identification of NB-ARC and LRR domains
Ortholog Grouping	OrthoFinder 2.5.4	Identification of conserved NBS-LRR genes across species
Remote Homology	TM-Vec	Structural similarity search from sequence data
Structural Alignment	DeepBLAST	Structural alignment prediction without solved structures
Functional Validation	VIGS (Virus-Induced Gene Silencing)	Functional characterization of NBS-LRR gene candidates

Solving issues with low homology and gene dropout in capture sequencing requires an integrated approach combining optimized wet-lab protocols with advanced computational methods. For researchers benchmarking novel NBS-LRR genes against known resistance genes, the experimental data presented here supports the selection of high-performance exome capture platforms such as Twist or IDT on DNBSEQ-T7 sequencers to minimize dropout rates. Furthermore, incorporating deep learning tools like TM-Vec and DeepBLAST enables detection of structurally homologous NBS-LRR genes that would be missed by traditional sequence-based methods. This multifaceted approach significantly enhances the completeness and accuracy of resistance gene annotations, advancing our understanding of plant immunity mechanisms and supporting the development of disease-resistant crop varieties.

Developing Robust Benchmarks for Pipeline Performance and Validation

In the rapidly advancing field of genomics, particularly in the identification of novel Nucleotide-Binding Site-Leucine-Rich Repeat (NBS-LRR) resistance genes, robust benchmarking pipelines have become indispensable for validating research findings and tool performance. These resistance genes constitute the largest known family of plant disease resistance (R) genes and play a vital role in defense mechanisms against diverse pathogens [86]. The development of accurate benchmarking frameworks enables researchers to objectively compare the performance of various computational tools, assess the quality of genomic annotations, and validate newly identified resistance gene candidates against established references.

The challenge of benchmarking is particularly acute in NBS-LRR research due to the complex nature of these gene families, which are characterized by tandem gene duplication, functional divergence, and diversifying selection [86] [87]. As genomic sequencing technologies become more accessible and the volume of data expands exponentially, standardized evaluation metrics and validation protocols are increasingly necessary to ensure scientific rigor and reproducibility. This comparative guide examines current benchmarking methodologies, performance metrics, and experimental frameworks used in genomic pipeline validation, with specific application to NBS-LRR gene identification and characterization.

Comparative Analysis of Genomic Benchmarking Tools and Performance Metrics

Tool Performance Comparison for Resistance Gene Identification

Table 1: Comparison of Bioinformatics Tools for Genomic Analysis

Tool Name	Primary Function	Methodology	Key Metrics	Performance Notes
AMRFinder [88]	Antimicrobial resistance gene identification	Protein-based HMM and BLAST	Accuracy, Precision, Sensitivity, Specificity	98.4% consistency between genotype-phenotype predictions; superior to ResFinder in missing fewer loci
ResFinder [89]	Resistance gene detection	BLAST-based	Coverage, Identity	Identifies higher numbers of genes but with potential duplicates
ABRicate [89]	Resistance gene screening	BLAST-based	Coverage, Identity	Higher coverage and identity percentages compared to ResFinder
Kraken2 [89]	Taxonomic sequence identification	K-mer based	Accuracy, Reproducibility	100% correct identification of bacterial species in validation study
SpeciesFinder [89]	Species identification	Not specified	Accuracy	92.54% correct identification rate for target species

Performance Metrics and Validation Outcomes in Genomic Studies

Table 2: Benchmarking Metrics and Outcomes from Genomic Validation Studies

Study Context	Sample Size	Key Validation Metrics	Outcomes	Reference
Carbapenem-resistant K. pneumoniae [89]	201 genomes	Repeatability, Reproducibility, Accuracy, Precision, Sensitivity, Specificity	All tools showed >75% performance across metrics; 100% repeatability/reproducibility	[89]
NARMS foodborne pathogens [88]	6,242 isolates	Positive Predictive Value (PPV), Negative Predictive Value (NPV), Consistency	PPV: 0.955, NPV: 0.992, Overall consistency: 98.4%	[88]
Genomic Newborn Screening (Early Check) [90]	1,979 newborns	Screen-positive rate, Confirmatory testing rate, Penetrance	2.5% screen-positive rate; 74% agreed to confirmatory testing; most infants asymptomatic	[90]
NeoGen Newborn Screening [91]	4,054 newborns	Technical feasibility, Positive screening rate	99.7% sequencing success; 13.0% received possible diagnosis	[91]

Experimental Protocols for Pipeline Validation and Benchmarking

Standardized Workflow for Genomic Pipeline Validation

The validation of genomic analysis pipelines requires systematic implementation of standardized protocols. Based on recent studies, the following workflow has demonstrated effectiveness:

Sample Processing and Sequencing: Begin with quality-controlled DNA extraction, with concentration measurements ensuring minimum thresholds (e.g., >3 ng/μL). Library preparation follows established protocols, such as Illumina DNA Prep with exome enrichment, with paired-end sequencing (2 × 150 bp) on platforms like NovaSeq 6000 to achieve sufficient coverage (mean ~120×) [91].

Data Analysis and Variant Calling: Process raw reads through quality trimming, alignment to reference genomes (e.g., GRCh37/hg19), and variant calling. Implement quality filters such as minimum coverage thresholds (e.g., 97.5% of target covered at 20× or greater) to ensure data reliability [91].

Variant Interpretation and Annotation: Utilize established guidelines (e.g., American College of Medical Genetics and Genomics standards) with refinements to reduce false positives in asymptomatic populations. Incorporate multiple annotation sources including ClinVar, COSMIC, dbSNP, and population frequency databases (ExAC, gnomAD), alongside in silico prediction tools (AlphaMissense, CADD, REVEL, SpliceAI) [91].

Validation and Confirmatory Testing: For resistance gene identification, orthogonal confirmation through family segregation studies, functional assays, or phenotypic correlation is essential. In the NARMS study, this involved comparing genotypic predictions with 87,679 susceptibility tests to establish consistency metrics [88].

Specialized Methodologies for NBS-LRR Gene Identification and Characterization

The benchmarking of pipelines designed for NBS-LRR gene discovery requires specialized approaches:

Degenerate PCR and Database Mining: As applied in pepper genome analysis, combine PCR amplification using degenerate primers targeting conserved domains (P-loop, kinase-2, GLPL) with database mining to identify candidate resistance gene analogs (RGAs) [86].

Motif and Domain Analysis: Confirm identified sequences through detection of characteristic NBS-LRR motifs using tools like Pfam database searches and hidden Markov models (HMMs) with threshold E-values (e.g., 10^-4) [36].

Phylogenetic Classification: Construct phylogenetic trees based on deduced amino acid sequences to classify identified RGAs into established subfamilies (TIR-NBS-LRR and non-TIR-NBS-LRR), using known R genes from model organisms as references [86].

Evolutionary and Selection Analysis: Perform functional divergence analysis using software like DIVERGE to identify critical amino acid sites involved in functional divergence and calculate non-synonymous (Ka) and synonymous (Ks) substitution rates to determine evolutionary pressures [86].

Visualization of Benchmarking Workflows and Analytical Processes

Genomic Pipeline Validation Workflow

Tool Selection Logic for Resistance Gene Analysis

Essential Research Reagents and Computational Tools

Table 3: Research Reagent Solutions for Genomic Pipeline Benchmarking

Category	Specific Tools/Reagents	Function in Benchmarking	Application Examples
Wet Lab Reagents	DNeasy Blood & Tissue Kit [91]	High-quality DNA extraction from various sample types	Genomic newborn screening using dried blood spots
	Illumina DNA Prep with Exome Enrichment [91]	Library preparation and target enrichment	Whole exome sequencing for newborn screening panels
	Qubit dsDNA High Sensitivity Assay [91]	Accurate DNA quantification	Quality control step before sequencing
Computational Tools	AMRFinder [88]	Comprehensive antimicrobial resistance gene detection	Identification of known resistance genes in pathogen genomes
	Kraken2 [89]	Taxonomic sequence classification	Verification of species identity in genomic samples
	BLAST-based tools [89]	Sequence similarity searches	Identification of novel resistance gene analogs
Database Resources	Pfam database [36]	Protein family and domain annotation	Verification of NBS domains in candidate resistance genes
	NCBI Conserved Domain Database [36]	Protein domain identification	Classification of NBS-LRR genes into subfamilies
	ClinVar, dbSNP, gnomAD [91]	Variant frequency and clinical significance	Interpretation of identified variants in clinical contexts
Specialized Software	DIVERGE software [86]	Functional divergence analysis	Detection of altered selective constraints in protein evolution
	CLUSTALW [37]	Multiple sequence alignment	Comparison of RGAs across different plant species
	DnaSP [37]	DNA sequence polymorphism analysis	Measurement of genetic variation in resistance genes

Discussion and Future Directions in Benchmarking Methodology

The development of robust benchmarks for pipeline performance and validation represents an ongoing challenge in genomic research, particularly in the complex field of NBS-LRR gene discovery. Current approaches successfully leverage multiple tools and methodologies to achieve comprehensive validation, but several areas require continued development.

The integration of simulation-based evaluation paradigms represents a promising direction for future benchmarking methodologies [92]. This approach moves beyond traditional metrics to assess functional performance through domain-specific simulators, providing more realistic evaluation of tool capabilities. Additionally, the establishment of standardized reference datasets and universal scoring criteria would significantly enhance comparability across studies [93].

In the specific context of NBS-LRR research, benchmarks must account for the unique evolutionary characteristics of these gene families, including tandem duplication, gene conversion, and diversifying selection [86] [87]. Future benchmarking frameworks should incorporate phylogenetic analysis, evolutionary rate calculations, and functional divergence metrics to comprehensively evaluate pipeline performance in this specialized domain.

As genomic technologies continue to evolve and find new applications in clinical, agricultural, and research settings, the development of rigorous, standardized benchmarking methodologies will remain essential for ensuring scientific validity, reproducibility, and translational impact.

Best Practices for Handling Low Expression and Repetitive Sequences

In the field of plant genomics, accurately identifying nucleotide-binding site (NBS) resistance genes is crucial for understanding disease resistance mechanisms and advancing crop improvement. However, two significant technical challenges consistently hamper this process: the characteristically low expression levels of NBS genes under non-stress conditions and their tendency to be embedded in repetitive genomic regions [15] [29]. These characteristics lead to substantial gaps in automated genome annotations, with traditional pipelines missing up to 45% of NBS-LRR genes in some species [29]. This review systematically compares modern methodologies overcoming these limitations, providing researchers with validated approaches for comprehensive NBS gene discovery within benchmarking frameworks.

Comparative Analysis of Identification Methods

Performance Benchmarking of Key Methodologies

Different methodologies have been developed to address the challenges of NBS gene identification, each with distinct strengths and limitations. The table below summarizes the performance characteristics of three prominent approaches:

Table 1: Performance Comparison of NBS Gene Identification Methods

Method	Core Approach	Advantages	Limitations	Reported Efficacy
Homology-based R-gene Prediction (HRP) [29]	Two-level homology search using full-length R-genes	- Identifies complete gene models- Overcomes repeat masking bias- Effective for allele mining	- Dependent on quality initial gene set- Computationally intensive	45% more genes identified versus conventional PDS
NLGenomeSweeper [94] [95]	NB-ARC domain detection with BLAST suite	- High specificity for complete genes- Focuses on structurally intact pseudogenes- Provides manual curation resources	- May miss highly fragmented genes- Primarily identifies NB-ARC region	Specifically designed for repetitive regions
Protein Motif/Domain Search (PDS) [29] [14]	HMM/Pfam scanning of annotated gene sets	- Standardized, widely available- Fast initial screening- Works well with quality annotations	- Fails with repetitive sequences- Misses unannotated genes- Produces fragmented genes	Missing significant portions of R-gene repertoire

Experimental Validation and Impact Assessment

The functional validation of identified NBS genes remains crucial. Virus-Induced Gene Silencing (VIGS) has proven particularly valuable for confirming the role of NBS genes in disease resistance, as demonstrated in cotton where silencing of GaNBS (OG2) led to increased viral titers [14]. Expression profiling under stress conditions provides another key validation metric; studies consistently show that NBS genes generally maintain low baseline expression (making them difficult to detect without stress induction) but show significant upregulation upon pathogen challenge [15] [96] [73]. Furthermore, genetic variation analysis between resistant and susceptible genotypes reveals substantial differences in NBS gene variants, highlighting their functional importance [14].

Methodological Protocols for Overcoming Key Challenges

Handling Low Expression Patterns

The characteristically low expression of NBS genes without pathogen stimulation requires specific methodological adaptations:

Transcriptome Analysis Under Induced Conditions: As demonstrated in Akebia trifoliata and Euryale ferox, NBS genes typically show minimal expression in standard conditions but become detectable during later developmental stages or after pathogen recognition [15] [96]. Protocol: (1) Collect RNA samples from multiple tissues at various developmental stages; (2) Include pathogen-challenged and stress-treated samples; (3) Use deep sequencing (minimum 50M reads per sample) to capture low-abundance transcripts; (4) Employ sensitive transcript assembly tools like StringTie with guide annotation based on genomic NBS predictions [14].

Multi-Tissue and Temporal Sampling: Research in Akebia trifoliata revealed that certain NBS genes show relatively high expression specifically in rind tissues during later development [15]. This underscores the importance of comprehensive sampling strategies across different tissues and developmental timepoints rather than relying on single-tissue transcriptomes.

Addressing Repetitive Sequence Challenges

The tendency of NBS genes to reside in repetitive regions necessitates specialized bioinformatic approaches:

Full-Length Homology-Based Prediction (HRP): This method effectively circumvents repeat masking issues by combining domain searches with homology-based genome scanning [29]. Protocol: (1) Identify initial R-gene set using protein domain search (PDS) within annotated genes; (2) Use these full-length R-genes as queries for homology searches against the entire genome assembly; (3) Predict complete gene structures for identified loci; (4) Validate domains using InterProScan or similar tools.

Cluster-Aware Genome Annotation: Since NBS genes frequently organize in clusters [15] [96] [43], specialized clustering analysis is essential. Protocol: (1) Map all identified NBS genes to chromosomes; (2) Analyze flanking regions (250kb upstream/downstream) for additional NBS genes; (3) Define clusters when ≥2 NBS genes reside within these regions; (4) Perform separate evolutionary analysis on clustered versus singleton genes [96].

Table 2: Research Reagent Solutions for NBS Gene Studies

Reagent/Resource	Function	Application Example	Key Features
InterProScan [95]	Protein domain annotation	Verifying NBS, TIR, CC, LRR domains	Integrates multiple databases, batch processing
MEME Suite [15] [5]	Conserved motif discovery	Identifying conserved NBS domain motifs	Customizable motif width, statistical significance
OrthoFinder [14]	Orthogroup inference	Comparative analysis across species	Handens large datasets, visualizes gene relationships
VIGS Vectors [14]	Functional validation	Silencing candidate NBS genes	Rapid in planta assessment of gene function
ANNA Database [96]	Curated NLR genes	Reference sequences for annotation	>90,000 NLR genes from 304 angiosperm genomes

Integrated Workflow for Comprehensive NBS Gene Identification

The following workflow integrates multiple approaches to overcome both low expression and repetitive sequence challenges:

Diagram 1: Integrated workflow for comprehensive NBS gene identification combining multiple methods to address key challenges.

Accurate identification of NBS resistance genes requires integrated approaches that specifically address the challenges of low expression and repetitive sequences. Methodology benchmarking demonstrates that homology-based methods like HRP significantly outperform conventional domain searches, particularly in overcoming repeat masking limitations [29]. For handling low expression patterns, multi-condition transcriptomics with pathogen induction is essential for detecting functional NBS genes [15] [14]. Future methodology development should focus on long-read sequencing to better resolve repetitive NBS clusters, single-cell transcriptomics to understand cell-type-specific NBS expression, and machine learning approaches that integrate multiple data types for improved NBS gene prediction. These advances will enable more comprehensive benchmarking against known resistance genes and accelerate the discovery of valuable disease resistance traits for crop improvement.

Validation Frameworks and Comparative Genomics for NBS Genes

In the field of plant genomics, particularly in the benchmarking of novel Nucleotide-Binding Site-Leucine-Rich Repeat (NBS-LRR) genes against known resistance genes, functional validation techniques are crucial for establishing gene-phenotype relationships [8]. As resistance gene identification accelerates through genome sequencing and transcriptomic analyses, researchers require robust methodologies to confirm gene function rapidly and accurately [21] [97]. Among the available techniques, Virus-Induced Gene Silencing (VIGS) has emerged as a powerful reverse genetics tool, while various mutagenesis approaches continue to provide forward genetic insights. This guide objectively compares these methodologies, providing experimental data and protocols to inform selection for resistance gene characterization, particularly within the context of NBS-LRR gene benchmarking studies.

Technical Comparison: VIGS Versus Mutagenesis

Table 1: Comparative Analysis of Functional Validation Techniques

Feature	VIGS (Virus-Induced Gene Silencing)	Mutagenesis Approaches
Core Principle	Post-transcriptional gene silencing via viral vector delivery of target sequence [97]	Permanent alteration of DNA sequence through chemical, physical, or biological agents
Development Time	2-4 weeks for silencing phenotype [98]	Several months to years (for stable lines)
Permanence	Transient (typically 3 weeks to several months) [97]	Stable/heritable
Throughput	High-throughput capability [97]	Low to medium throughput
Technical Expertise	Moderate (vector construction, agroinfiltration) [98]	Varies (moderate to high)
Primary Application	Rapid gene function validation, preliminary screening [97] [99]	Generation of stable genetic resources, detailed phenotypic analysis
Key Advantage	Bypasses stable transformation; applicable to non-model species [100]	Creates permanent genetic material for repeated experimentation
Major Limitation	Transient nature; potential off-target effects; viral symptoms [97]	Time-consuming; may have pleiotropic effects

Table 2: Quantitative Performance Metrics in Plant Systems

Parameter	VIGS	Mutagenesis
Silencing Efficiency	65-95% (soybean TRV system) [98]	N/A (creates null alleles)
Experimental Duration	3-8 weeks (from infection to phenotype assessment) [8] [101]	6-24 months (depending on generation time)
Species Demonstrated	Tobacco, tomato, soybean, cotton, iris [97] [98] [100]	Virtually all plant species
Validation in NBS-LRR Research	Yes (e.g., GaNBS in cotton, Vm019719 in tung tree) [21] [8]	Yes (e.g., T-DNA mutants in Arabidopsis)

Experimental Protocols

VIGS Protocol for NBS Gene Validation

The TRV-based VIGS protocol has been successfully optimized for various crops including soybean, cotton, and tobacco [98] [101]. The following methodology details the steps for functional validation of NBS-LRR genes:

• Target Sequence Selection and Vector Construction: Identify a unique 300-500 bp fragment of the target NBS-LRR gene with no off-target potential (verified using tools like RNAiScan). Clone this fragment into the pTRV2 vector using appropriate restriction sites (e.g., EcoRI and XhoI) [98].

• Agrobacterium Preparation: Transform recombinant pTRV2 constructs and the helper pTRV1 vector into Agrobacterium tumefaciens strain GV3101. Grow individual colonies in LB medium with appropriate antibiotics to OD₆₀₀ = 0.5-1.0. Pellet cells and resuspend in infiltration buffer (10 mM MES, 10 mM MgClâ‚‚, 200 μM acetosyringone) to OD₆₀₀ = 1.5 [98].

• Plant Infection: Mix pTRV1 and pTRV2-derived cultures in 1:1 ratio. For soybean and similar species, the optimized cotyledon node method is recommended: immerse longitudinally bisected half-seed explants in Agrobacterium suspension for 20-30 minutes [98]. For Nicotiana benthamiana, leaf infiltration using a needleless syringe is standard [97].

• Post-Infection Conditions: Maintain plants at 19-22°C for optimal viral spread and silencing efficiency. High temperatures (above 25°C) can significantly reduce silencing effectiveness [97].

• Phenotype Assessment: Evaluate silencing phenotypes 2-4 weeks post-infection. For disease resistance assays, challenge silenced plants with relevant pathogens (e.g., Verticillium dahliae for wilt studies) and assess disease symptoms compared to controls [101] [99].

• Molecular Validation: Confirm target gene silencing via qRT-PCR, typically showing 60-95% reduction in transcript levels [98]. For NBS-LRR genes, monitor expression of defense markers (PR genes, ROS accumulation) to validate functional impact [101].

Figure 1: VIGS Experimental Workflow for Gene Function Validation

Mutagenesis Approaches for Resistance Gene Discovery

While VIGS provides rapid validation, mutagenesis remains valuable for generating stable genetic resources:

• Chemical Mutagenesis (EMS): Treat seeds with ethyl methanesulfonate (0.1-0.6%) to induce point mutations. Screen subsequent generations (M2) for disease susceptibility phenotypes, then map and identify causal mutations through sequencing [99].

• T-DNA/Transposon Mutagenesis: Generate large populations of lines with random insertions. Screen for altered disease response phenotypes, then use flanking sequence tags to identify disrupted genes. Particularly effective in model species like Arabidopsis.

• Targeted Mutagenesis (CRISPR/Cas9): Design guide RNAs targeting specific NBS-LRR genes. Transform plant tissue to create knockout mutants. Screen for successful gene editing and characterize disease response phenotypes in subsequent generations.

Application in NBS-LRR Gene Benchmarking

Case Studies in Crop Species

The functional validation of NBS-LRR genes using VIGS has been demonstrated across multiple crop species:

• Cotton: Silencing of GaNBS (OG2) in resistant cotton via VIGS demonstrated its role in reducing cotton leaf curl disease virus titer, confirming its function in disease resistance [21]. Similarly, silencing of GbCNL130 compromised resistance to Verticillium wilt, while overexpression in Arabidopsis enhanced resistance [101].

• Tung Tree: VIGS of Vm019719 in resistant Vernicia montana demonstrated its essential role in conferring resistance to Fusarium wilt, identifying it as a key candidate gene for marker-assisted breeding [8].

• Soybean: TRV-based VIGS successfully silenced the rust resistance gene GmRpp6907, validating its function and demonstrating the system's effectiveness in legumes [98].

Signaling Pathways in NBS-LRR Mediated Resistance

NBS-LRR genes typically function in plant immune signaling pathways, which can be investigated through functional validation techniques:

Figure 2: NBS-LRR-Mediated Defense Signaling Pathway

As illustrated, NBS-LRR proteins recognize pathogen effectors, triggering defense signaling that activates salicylic acid (SA)-dependent pathways, reactive oxygen species (ROS) burst, and pathogenesis-related (PR) gene expression, ultimately leading to disease resistance [101]. Functional validation techniques like VIGS allow researchers to disrupt this pathway at specific points to determine individual gene contributions.

The Scientist's Toolkit

Table 3: Essential Research Reagents for Functional Validation

Reagent/Resource	Function	Example Applications
TRV VIGS Vectors (pTRV1, pTRV2)	Bipartite viral vector system for inducing gene silencing	Systemic silencing in dicot plants; NBS-LRR validation [98]
Agrobacterium tumefaciens GV3101	Plant transformation vehicle for vector delivery	TRV vector delivery in VIGS protocols [98]
BSMV Vectors	Barley stripe mosaic virus for monocot VIGS	Gene silencing in cereal crops [97]
Gateway Cloning System	Efficient recombination-based vector construction	Rapid cloning of target sequences into VIGS vectors
EMS (Ethyl Methanesulfonate)	Chemical mutagen for creating point mutations	Forward genetic screens for disease susceptibility [99]
Pathogen Isolates	Biological agents for disease phenotyping	Verticillium dahliae for wilt studies [101] [99]
SA/JA Signaling Reporters	Transgenic lines reporting defense pathway activation	Monitoring immune response in silenced plants [101]

The comparative analysis of functional validation techniques reveals a complementary relationship between VIGS and mutagenesis approaches in benchmarking novel NBS genes. VIGS provides rapid, high-throughput validation ideal for preliminary screening and testing candidate genes identified through genomic analyses, with successful applications across numerous crop species [21] [8] [101]. Its ability to bypass stable transformation makes it particularly valuable for non-model species and recalcitrant crops. Conversely, mutagenesis approaches generate stable genetic resources suitable for detailed phenotypic analysis and breeding programs.

For comprehensive NBS-LRR gene benchmarking, researchers should consider an integrated approach: using VIGS for rapid initial screening of multiple candidate genes, followed by the creation of stable mutants for definitive validation and development of breeding materials. This combined strategy leverages the strengths of both systems to accelerate resistance gene characterization and deployment in crop improvement programs.

Genome-Wide Association Studies (GWAS) for Linking Genotype to Phenotype

Genome-wide association studies (GWAS) represent a powerful methodology for identifying genetic variants statistically associated with specific traits or diseases by testing hundreds of thousands of genetic variants across many genomes [102]. This approach has generated a myriad of robust associations for diverse traits, particularly in complex diseases where numerous genomic loci contribute to pathogenesis, each typically exerting small effects that collectively influence disease development [103]. The ultimate value of GWAS extends beyond mere association discovery to bridging the gap between statistical association and biological function—a critical step for therapeutic targeting. This process requires validating causal genetic variants, identifying causal genes, and determining the directionality of effect, tasks that demand both computational and experimental approaches for functional investigation [103]. In the specific context of nucleotide-binding site (NBS) disease resistance genes in plants, GWAS provides a population-based framework for identifying novel resistance alleles and understanding their evolutionary history, enabling more targeted disease resistance breeding strategies [104] [105].

GWAS Methodology and Analytical Framework

Core Principles and Genetic Concepts

The overarching goal of a GWAS is to determine which genomic loci associate with a trait or disease of interest by systematically testing for frequency differences of genetic variants between cases and controls or across a quantitative trait spectrum [103]. The methodology leverages several key genetic concepts:

• Minor Allele Frequency (MAF): SNPs typically have two alleles within a population—a more common major allele and less common minor allele. MAFs vary widely among ethnic groups due to contrasting evolutionary histories [103].
• Linkage Disequilibrium (LD): SNPs close together on a chromosome without intervening recombination hotspots exhibit LD, meaning they are inherited together more often than expected by chance. This allows GWAS to identify genomic regions harboring causal variants through association with tag SNPs [103].
• Statistical Significance Threshold: GWAS employs a stringent significance threshold (typically P < 5 × 10⁻⁸) derived from Bonferroni correction to account for multiple testing across millions of variants [103].

From Association to Causation: Fine-Mapping Approaches

A significant GWAS hit identifies a lead SNP that serves as a signpost for a genomic interval containing potential causal variants, but this SNP is not necessarily the causal variant itself [103]. Fine-mapping narrows these association signals:

• Trans-ethnic Fine-Mapping: Leverages distinct LD patterns across ethnic populations to narrow candidate causal variants. The overlap of association signals from populations with different evolutionary histories can help converge on true causal variants [103].
• Integration of Functional Genomics Data: Incorporates epigenomic annotations to prioritize non-coding variants affecting gene regulation from a distance through mechanisms like chromatin looping [103].
• Structural Variation Integration: Tools like GWAS SVatalog compute LD between structural variations (SVs) and GWAS-associated SNPs, helping identify SVs that may explain association signals where SNPs alone cannot provide causal explanations [106].

Table 1: Key Computational Tools for GWAS Analysis and Fine-Mapping

Tool Category	Representative Tools	Primary Function	Advantages
Genotype Imputation	IMPUTE2, Beagle, Minimac4, GLIMPSE [107]	Infers ungenotyped variants using reference panels	Increases variant coverage, facilitates meta-analyses, reduces costs
Variant Association	PLINK [102], RICOPILI [102]	Performs genome-wide association testing	Efficient handling of large datasets, multiple statistical models
Fine-Mapping	GWAS SVatalog [106]	Visualizes LD between SVs and GWAS SNPs	Identifies structural variants explaining SNP associations
Meta-Analysis	METAL [102]	Combines results across multiple studies	Increases power through larger sample sizes

Benchmarking NBS Genes: GWAS Applications in Plant Disease Resistance

NBS Gene Family and Disease Resistance Mechanisms

NBS-leucine-rich repeat (LRR) proteins encoded by resistance genes play a crucial role in plant responses to various pathogens, including viruses, bacteria, fungi, and nematodes [105]. These genes typically fall into two major families distinguished by their N-terminal domains:

• TIR-NBS-LRR: Contains Toll/interleukin-1 receptor homology domain [105]
• CC-NBS-LRR: Features a coiled-coil domain at the N-terminus [105]

NBS-LRR proteins recognize pathogens through direct interaction with pathogen effectors or via the guard model, where they monitor plant effector targets against pathogen attack [105]. This mechanistic diversity enables a limited number of R-genes to target a broad spectrum of pathogens.

GWAS for Novel NBS Allele Discovery

GWAS has proven effective in identifying novel resistance alleles in crop species. A GWAS of rice blast resistance in 500 diverse rice accessions identified strong associations near known resistance loci Ptr and Pia, leading to the discovery of previously unknown alleles [104]. Key findings included:

• An allelic series for the unusual Ptr rice blast resistance gene, which encodes an armadillo-repeat protein rather than the typical NLR structure [104]
• Additional alleles of the Pia resistance genes RGA4 and RGA5, which function as paired NLRs where RGA5 contains an integrated HMA domain that directly interacts with pathogen effectors [104]
• Two distinct resistance sources in the rice accessions tested, with different haplotype patterns associated with resistance to different Magnaporthe oryzae isolates [104]

Table 2: Comparative Analysis of Cloned Rice Blast Resistance Genes Identified Through GWAS

Gene	Gene Family	Protein Function	Pathogen Recognition	Allelic Diversity
Ptr/Pi-ta2	Armadillo-repeat	Uncharacterized	Required for AVR-Pita mediated resistance [104]	Multiple alleles with varying specificity [104]
Pia	Paired NLR (RGA4/RGA5)	RGA5: sensor NLR with HMA; RGA4: helper NLR [104]	Direct interaction with AVR-Pia and AVR1-CO39 [104]	Two functional alleles identified [104]
Pik	NLR with integrated HMA	HMA domain binds AVR-Pik variants [104]	Direct binding to AVR-Pik effectors [104]	Seven alleles with varying spectra [104]
Pi-ta	NLR	Direct interaction with AVR-Pita [104]	Yeast two-hybrid and in vitro binding [104]	Single amino acid polymorphism (S918) key to recognition [104]

Evolutionary Insights from NBS Gene Analysis

Comparative phylogenetic analyses of NBS-encoding genes across Cucurbitaceae species reveal that gene duplication, sequence divergence, and gene loss represent major evolutionary modes [105]. Studies in cucumber demonstrate relatively few NBS-encoding genes compared to other species, yet maintaining both TIR and CC families with distinct conserved motifs [105]. Phylogenetic comparisons with Arabidopsis thaliana show:

• TIR families from Cucurbitaceae species are phylogenetically distinct from most TIR families in Arabidopsis [105]
• CC-NBS families group closely with CC families in Arabidopsis [105]
• NBS-encoding gene expansions, especially in the TIR family, likely occurred before the divergence of Cucurbitaceae and Arabidopsis [105]

Experimental Protocols for GWAS Follow-Up

Protocol 1: GWAS-Driven Gene Discovery and Validation

This protocol outlines a comprehensive approach for moving from GWAS association to validated candidate genes, adapted from rice blast resistance studies [104]:

• Diversity Panel Selection: Curate a diversity panel excluding accessions with known resistance genes to maximize discovery of novel alleles (e.g., 500 rice accessions selected from larger diversity panel) [104].

• Phenotypic Screening: Conduct controlled pathogen inoculations (e.g., with multiple M. oryzae isolates) and score disease severity using standardized scales (e.g., 0-5 scale where 0=no symptoms, 5=large lesions >2mm) [104].

• Genome-Wide Association Analysis:

  • Perform genotype using arrays or sequencing
  • Conduct association testing using mixed models to account for population structure
  • Convert non-normally distributed traits to binary format for binomial analysis [104]

• De Novo Genome Assembly: Generate de novo assemblies of accessions with strong resistance associations to facilitate candidate gene identification [104].

• Candidate Gene Validation:

  • Transform susceptible accessions with candidate resistance genes
  • Evaluate transgenic lines for enhanced resistance
  • Conduct functional studies to confirm biochemical mechanisms [104]

Protocol 2: Fine-Mapping Through Trans-Ethnic Comparison

This protocol leverages population genetic differences to refine association signals [103]:

• Multi-Ethnic Cohort Assembly: Recruit study populations from distinct ethnic backgrounds with contrasting LD patterns.

• Variant Imputation: Use reference panels (e.g., 1000 Genomes) to impute ungenotyped variants, ensuring ancestry-matched reference panels for optimal accuracy [107].

• Stratified Association Analysis: Conduct GWAS in each population separately using appropriate ancestry-specific covariates.

• LD Pattern Comparison: Analyze differences in association signals and LD blocks across populations.

• Variant Prioritization: Identify variants showing association across multiple populations, particularly those with consistent direction of effects.

• Functional Annotation: Integrate epigenomic data from relevant cell types to prioritize variants overlapping regulatory elements [103].

Visualization of GWAS Workflow and NBS Gene Function

GWAS to Gene Validation Workflow

GWAS to Gene Validation Workflow: This diagram outlines the key stages from study design to functional validation of candidate genes.

NBS-LRR mediated Plant Immunity

NBS-LRR Mediated Immunity: Simplified pathway of nucleotide-binding site leucine-rich repeat protein activation leading to disease resistance.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Research Reagents for GWAS and NBS Gene Functional Analysis

Reagent/Category	Specific Examples	Function/Application	Key Considerations
Genotyping Arrays	Illumina Infinium, Affymetrix Axiom	Genome-wide variant genotyping	Density, population-specific content, cost [102]
Reference Panels	1000 Genomes, gnomAD, population-specific panels	Variant imputation, frequency reference	Ancestry matching, sample size, variant diversity [107]
Imputation Algorithms	IMPUTE2, Beagle, Minimac4, GLIMPSE [107]	Inference of ungenotyped variants	Computational efficiency, rare variant accuracy, ancestry sensitivity [107]
GWAS Software	PLINK, GENESIS, SAIGE, RICOPILI [102]	Association testing, quality control	Handling of relatedness, population structure, scalability [102]
SV Detection Tools	pbsv, Sniffles, Manta [106]	Structural variant calling from sequencing data	Read technology (short vs. long-read), sensitivity, precision [106]
Plant Transformation	Agrobacterium strains, binary vectors	Functional validation of candidate genes	Genotype specificity, transformation efficiency [104]
Pathogen Assay Systems	Magnaporthe oryzae isolates, Pseudomonas strains	Phenotypic screening of disease resistance	Pathogen diversity, inoculation methods, scoring systems [104]

Comparative Analysis of NBS Repertoires Across Plant Genepools

Plant immunity relies on a sophisticated surveillance system where nucleotide-binding site (NBS) domain genes serve as critical intracellular immune receptors. These genes, particularly those belonging to the NBS-LRR (NLR) superfamily, recognize pathogen effectors and initiate robust defense responses [14] [108]. The composition and diversity of NBS repertoires vary dramatically across plant genepools, influencing disease resistance durability and evolutionary potential. This comparative guide benchmarks NBS repertoire characteristics across diverse plant lineages—from ancient mosses to modern crops—providing researchers with quantitative frameworks and methodological standards for evaluating novel resistance genes against established references. Understanding these genomic landscapes is essential for strategic resistance gene deployment in crop breeding programs.

Methodological Framework for NBS Repertoire Analysis

Standardized methodologies are crucial for meaningful cross-species comparisons of NBS genes. The following experimental protocols represent current best practices in the field.

Genome-Wide Identification and Classification

Comprehensive identification of NBS-encoding genes begins with hidden Markov model (HMM) searches using the NB-ARC domain (Pfam: PF00931) as a query, typically with an E-value cutoff of 1.0 [109]. Candidate sequences are subsequently verified through Pfam and Conserved Domain Database (CDD) analyses to confirm NBS domain presence and architecture [109] [110]. Classification systems categorize NBS genes based on N-terminal domains: CC-NBS-LRR (CNL), TIR-NBS-LRR (TNL), and RPW8-NBS-LRR (RNL) [109]. Some studies further differentiate between complete NBS-LRR genes and truncated forms lacking LRR domains [110].

Evolutionary and Phylogenetic Analysis

OrthoFinder with the Diamond algorithm for sequence similarity and MCL clustering effectively resolves orthogroups (OGs) across species [14]. Maximum likelihood phylogenetic trees constructed using FastTreeMP with 1000 bootstrap replicates provide robust evolutionary frameworks [14]. Gene cluster analysis follows established criteria where NBS genes located within 250 kilobases on a chromosome are considered clustered [109]. These methods enable researchers to distinguish core (conserved across species) and unique (lineage-specific) orthogroups, revealing evolutionary patterns such as expansion, contraction, or conservation.

NBS Profiling for Population-Level Diversity

For population-level studies, NBS profiling using primers targeting conserved NBS motifs (P-loop, Kinase-2, GLPL) efficiently captures sequence diversity across numerous accessions [111]. This complexity reduction technique sequences 200-480 bp NBS tags, which are mapped to reference genomes to identify single nucleotide polymorphisms (SNPs) and presence-absence variations [111]. The method is particularly valuable for tracking R gene alleles across breeding populations and landraces, enabling association studies between specific NBS haplotypes and disease resistance phenotypes.

Comparative Genomic Analysis of NBS Repertoires

Quantitative comparisons reveal substantial variation in NBS repertoire size, architecture, and evolutionary dynamics across plant genepools.

Repertoire Size and Architectural Diversity

Table 1: NBS Repertoire Characteristics Across Plant Species

Plant Species	Family/Group	Total NBS Genes	CNL	TNL	RNL	Notable Features
Xanthoceras sorbifolium	Sapindaceae	180	155	23	3	"First expansion then contraction" pattern [109]
Dimocarpus longan	Sapindaceae	568	~493*	~50*	~25*	Strong recent expansion [109]
Acer yangbiense	Sapindaceae	252	~219*	~29*	~4*	Moderate expansion/contraction [109]
Dendrobium officinale	Orchidaceae	74	10	0	-	Extensive NBS gene degeneration [110]
Arabidopsis thaliana	Brassicaceae	210	40	-	-	Reference for comparative studies [110]
34 Plant Species (Mosses to Angiosperms)	Multiple	12,820	~70,000â€	~18,700â€	~1,800â€	168 domain architecture classes [14]

Estimated values based on phylogenetic distribution patterns [109] †Cumulative values from ANNA: Angiosperm NLR Atlas encompassing 304 angiosperm genomes [14]

A comprehensive analysis of 34 plant species identified 12,820 NBS-domain-containing genes classified into 168 distinct architectural classes [14]. These encompass both classical configurations (NBS, NBS-LRR, TIR-NBS, TIR-NBS-LRR) and species-specific structural patterns (TIR-NBS-TIR-Cupin1-Cupin1, TIR-NBS-Prenyltransf, Sugar_tr-NBS) [14]. Sapindaceae species exemplify how closely related plants can exhibit distinct evolutionary patterns: X. sorbifolium shows "first expansion then contraction," while D. longan exhibits "first expansion followed by contraction and further expansion" [109].

Lineage-Specific Evolutionary Patterns

Table 2: Evolutionary Patterns of NBS Genes Across Plant Families

Plant Family	Representative Species	Evolutionary Pattern	Key Drivers
Sapindaceae	X. sorbifolium, D. longan, A. yangbiense	Independent duplication/loss events [109]	Species-specific pathogen pressures [109]
Poaceae	Rice, maize, sorghum, brachypodium	Contraction [109]	Gene losses, deletions, translocations [109]
Brassicaceae	A. thaliana and relatives	First expansion then contraction [109]	Unknown selective constraints [109]
Orchidaceae	D. officinale, D. nobile, D. chrysotoxum	Degeneration and loss [110]	High rate of NBS gene degeneration [110]
Fabaceae	Medicago, soybean	Consistent expansion [109]	Frequent gene duplication [109]
Solanaceae	Pepper, tomato, potato	Variable (contraction/expansion) [109]	Species-specific selection pressures [109]

Monocot-dicot divergences are particularly evident in NBS repertoire composition. Monocots, including orchids and grasses, universally lack TNL-type genes [110] [112], potentially due to NRG1/SAG101 pathway deficiency [110]. This absence represents a major architectural difference compared to dicot repertoires. Additionally, NBS genes are typically clustered as tandem arrays in plant genomes, with few existing as singletons [109]. These clusters serve as evolutionary innovation hotspots where gene conversion, unequal crossing over, and duplication events generate novel resistance specificities [108] [111].

Landrace vs. Modern Variety Diversity

Comparative analysis of rice landraces from Yuanyang terraces and modern varieties reveals striking differences in NBS diversity. Landraces maintain higher NLR sequence diversity with signatures of balancing selection, whereas modern varieties show reduced diversity and lack ancient NLR haplotypes retained in landraces [113]. This genetic erosion in modern breeding lines potentially compromises disease resilience, highlighting the conservation value of traditional landraces as reservoirs of NBS diversity for crop improvement [113].

Expression Dynamics and Functional Validation

Differential Expression Under Stress

Transcriptomic profiling of NBS genes across tissues and stress conditions reveals complex regulation patterns. In cotton, specific orthogroups (OG2, OG6, OG15) show putative upregulation under various biotic and abiotic stresses in both susceptible and tolerant accessions [14]. Treatment of D. officinale with salicylic acid (SA) identified 1,677 differentially expressed genes, including six NBS-LRR genes significantly upregulated [110]. Weighted gene co-expression network analysis (WGCNA) pinpointed Dof020138 as a key node connecting pathogen recognition, MAPK signaling, and plant hormone transduction pathways [110].

Functional Validation Through Silencing

Virus-induced gene silencing (VIGS) of GaNBS (OG2) in resistant cotton demonstrated its critical role in virus tittering, confirming functional importance [14] [21]. Protein-ligand and protein-protein interaction assays further revealed strong binding between putative NBS proteins and ADP/ATP as well as core proteins of the cotton leaf curl disease virus [14]. These functional validations bridge genomic identification with mechanistic understanding of NBS gene operation in plant immunity.

Research Toolkit for NBS Gene Analysis

Table 3: Essential Research Reagents and Resources for NBS Gene Studies

Resource Category	Specific Tools/Reagents	Application Purpose	Key Features
Bioinformatics Databases	ANNA: Angiosperm NLR Atlas [14]	Pan-species NBS gene reference	>90,000 NLR genes from 304 angiosperms [14]
	Plant Resistance Gene Analog (PRGA) [112]	RGA prediction and classification	Custom matrices for RGA identification [112]
	SolariX [111]	Potato NBS domain repository	NBS tags from 91 potato genomes [111]
Experimental Resources	MoBY 2.0 Library [114]	Gene overexpression screening	~4,900 ORFs in high-copy plasmid [114]
	NBS Profiling Primers [111]	Population-level NBS diversity	16 primers targeting P-loop, Kinase-2, GLPL [111]
Software Tools	OrthoFinder [14]	Orthogroup inference	Diamond + MCL for sequence clustering [14]
	PfamScan [14]	Domain architecture analysis	HMM-based domain identification [14]
	FastTreeMP [14]	Phylogenetic reconstruction	Maximum likelihood with bootstrap support [14]

Comparative analysis of NBS repertoires across plant genepools reveals both conserved features and lineage-specific innovations in plant immune system architecture. The extensive diversification of NBS genes—through gene duplication, domain rearrangement, and balancing selection—provides the raw material for evolutionary responses to rapidly evolving pathogens. Effective benchmarking of novel NBS genes requires integration of genomic, transcriptomic, and functional validation approaches within phylogenetic frameworks that account for species-specific evolutionary histories. Conservation and utilization of NBS diversity in landraces and wild relatives represents a crucial strategy for sustaining crop resistance breeding in the face of emerging disease threats. Future research directions should prioritize pan-genomic analyses that capture full NBS diversity within species, advanced protein structural studies to decipher recognition mechanisms, and development of predictive models for durable resistance gene deployment.

The identification and functional characterization of resistance genes are fundamental to advancing our understanding of host-pathogen interactions and developing novel therapeutic strategies. This comparison guide provides a systematic framework for evaluating nucleotide-binding site (NBS) domain genes—a major class of plant resistance genes—against established benchmarks and experimental standards. The NBS gene family represents one of the largest superfamilies of resistance (R) genes in plants, with proteins typically containing leucine-rich-repeat (LRR) and nucleotide-binding site (NBS) domains that function as critical immune receptors for effector-triggered immunity (ETI) [14] [9]. Recent studies have identified 12,820 NBS-domain-containing genes across 34 plant species, revealing significant diversification with 168 distinct domain architecture patterns, encompassing both classical and species-specific structural variants [14]. This guide synthesizes current methodologies, experimental data, and analytical frameworks to establish rigorous standards for characterizing novel resistance genes, with particular emphasis on their association with disease resistance traits and pathways.

Comparative Analysis of NBS Gene Family Diversity

Structural Classification and Genomic Distribution

NBS-LRR genes are broadly classified based on their N-terminal domains into several major structural categories. TNL genes contain Toll/interleukin-1 receptor (TIR) domains, while CNL genes feature coiled-coil (CC) domains [14] [9]. Additional classifications include genes with only NBS domains, as well as those with kinase domains (KIN), receptor-like proteins (RLP), and various other domain architectures [20].

Table 1: Structural Classification of NBS Genes Across Plant Species

Structural Class	Domain Architecture	Representative Species	Genomic Features
TNL	TIR-NBS-LRR	Vernicia montana, Arabidopsis	Primarily in eudicots; absent in monocots
CNL	CC-NBS-LRR	All angiosperms	Most abundant class across species
NBS-LRR	NBS-LRR (no TIR or CC)	Vernicia fordii, Rice	Common in monocots and some eudicots
CC-NBS	CC-NBS (no LRR)	Vernicia species	May function as signaling components
TIR-NBS	TIR-NBS (no LRR)	Vernicia montana	Potential regulatory functions
RNL	RPW8-NBS-LRR	Arabidopsis, Tobacco	Signal transduction components

Comparative genomic analyses reveal that NBS genes are distributed non-randomly across chromosomes, typically showing clustered distributions that suggest evolution through tandem duplication events [9]. Studies in tung trees (Vernicia fordii and Vernicia montana) demonstrate marked differences in NBS-LRR gene content between resistant and susceptible species, with 149 genes identified in resistant V. montana compared to only 90 in susceptible V. fordii [9]. This disparity highlights the potential correlation between NBS gene repertoire and disease resistance capacity.

Orthogroup Conservation and Diversification

Orthogroup analysis across multiple plant species has identified 603 orthogroups of NBS genes, with both core conserved orthogroups (OG0, OG1, OG2) and species-specific unique orthogroups (OG80, OG82) [14]. These orthogroups represent evolutionarily conserved clusters of NBS genes that maintain related functions across species boundaries. Expression profiling has revealed that specific orthogroups (including OG2, OG6, and OG15) show upregulated expression across various tissues under diverse biotic and abiotic stresses, suggesting their fundamental role in resistance mechanisms [14].

Table 2: NBS Gene Orthogroups with Documented Resistance Associations

Orthogroup	Expression Pattern	Resistance Association	Experimental Validation
OG2	Upregulated in multiple stress conditions	Cotton leaf curl disease (CLCuD)	VIGS silencing increased virus titer [14]
OG6	Responsive to biotic stresses	Multiple fungal and bacterial pathogens	Expression profiling in tolerant genotypes
OG15	Induced by abiotic stresses	Broad-spectrum resistance	Association with stress-responsive pathways
OG1	Conserved across species	Putative core immune function	Genetic variation analysis

Genetic variation analysis between susceptible (Coker 312) and tolerant (Mac7) Gossypium hirsutum accessions identified 6,583 unique variants in NBS genes of the tolerant Mac7 line compared to 5,173 variants in the susceptible Coker312 [14]. These sequence polymorphisms potentially contribute to functional differences in resistance capabilities and provide valuable markers for breeding programs.

Experimental Protocols for Functional Validation

Identification and Classification Workflows

PRGminer Deep Learning Pipeline The PRGminer tool represents a cutting-edge approach for high-throughput resistance gene prediction using deep learning algorithms [20]. The implementation occurs in two distinct phases:

• Phase I: The input protein sequences are classified as R-genes or non-R-genes using dipeptide composition features, achieving 98.75% accuracy in k-fold testing and 95.72% on independent validation with a Matthews correlation coefficient of 0.91 [20].

• Phase II: Predicted R-genes from Phase I are classified into eight specific categories (CNL, TNL, RLK, RLP, etc.) with an overall accuracy of 97.21% on independent testing [20].

The tool extracts sequential and convolutional features from raw encoded protein sequences, offering significant advantages over traditional alignment-based methods, particularly for genes with low sequence homology.

DaapNLRSeek for Complex Genomes For polyploid genomes like sugarcane, the DaapNLRSeek pipeline has been developed to accurately predict and annotate NLR genes from complex polyploid genomes [57]. This specialized approach addresses challenges posed by genome duplication and has enabled identification of TIR-only and TPK genes in sugarcane, including validation of paired NLRs that induce immune responses in Nicotiana benthamiana [57].

Functional Characterization Methods

Virus-Induced Gene Silencing (VIGS) VIGS has emerged as a powerful technique for functional validation of candidate resistance genes. The protocol typically involves:

• Gene Fragment Cloning: A 200-400 bp fragment of the target NBS gene is amplified and cloned into a VIGS vector (e.g., TRV-based vectors).

• Plant Inoculation: The recombinant vector is introduced into plants through agrobacterium-mediated infiltration or in vitro transcription.

• Phenotypic Assessment: Silenced plants are challenged with pathogens, and disease symptoms are quantified alongside molecular analysis of gene expression.

In a recent study, silencing of GaNBS (OG2) in resistant cotton resulted in significantly increased virus titers, confirming its role in defense against cotton leaf curl disease [14]. Similarly, VIGS of Vm019719 in resistant Vernicia montana demonstrated its essential function in Fusarium wilt resistance [9].

Multi-Omics Integration Approaches Network-based stratification (NBS) methods effectively integrate somatic mutation data with RNA sequencing data to identify clinically significant subtypes [115]. The protocol involves:

• Data Integration: Somatic mutation profiles (binary vectors) and gene expression profiles (continuous TPM values) are linearly combined using the formula: Si = β × pi + (1-β) × qi where β is a tuned hyperparameter [115].

• Network Propagation: Integrated profiles are mapped onto gene interaction networks and diffused using iterative propagation until convergence.

• Cluster Identification: Network-regularized non-negative matrix factorization and consensus clustering are applied to identify robust patient subtypes.

This approach has demonstrated enhanced association with overall survival in ovarian and bladder cancers, revealing influential genes spanning multiple subtypes [115].

Resistance Signaling Pathways and Mechanisms

NBS-LRR Mediated Immune Signaling

NBS-LRR proteins function as intracellular immune receptors that recognize pathogen effectors and initiate robust defense responses [9]. The signaling mechanism involves:

• Recognition Phase: Direct or indirect recognition of pathogen effectors through LRR domains
• Nucleotide Binding: ATP/GTP binding and hydrolysis by the NBS domain providing energy for conformational changes
• Downstream Signaling: Activation of defense responses including hypersensitive response, phytoalexin production, and systemic acquired resistance

The diagram below illustrates the core NBS-LRR signaling pathway:

NBS-LRR Signaling Pathway: This diagram illustrates the core mechanism of NBS-LRR mediated immunity, from pathogen recognition to defense activation.

Transcriptional Regulation of Resistance Genes

Studies in tung trees have revealed that resistance gene expression is tightly controlled by transcription factors. The NBS-LRR gene Vm019719 in resistant V. montana is activated by VmWRKY64, while its allelic counterpart in susceptible V. fordii (Vf11G0978) shows compromised expression due to a deletion in the promoter's W-box element [9]. This highlights the importance of transcriptional regulation in determining resistance outcomes.

Research Reagent Solutions Toolkit

Table 3: Essential Research Reagents for Resistance Gene Analysis

Reagent/Category	Specific Examples	Function/Application
Bioinformatics Tools	PRGminer, DaapNLRSeek, OrthoFinder	NBS gene prediction, classification, and evolutionary analysis
Functional Validation Systems	VIGS vectors, CRISPRa/dCas9, Yeast two-hybrid	Gene function analysis, protein interaction studies
Expression Profiling	RNA-seq libraries, RT-PCR assays, Microarrays	Transcriptional analysis under stress conditions
Genomic Resources	Phytozome, Ensemble Plants, NCBI databases	Reference sequences and annotation data
Structural Analysis	Phobius, TMHMM2, SignalP, nCoil	Domain prediction and subcellular localization
Plasmid Vectors	Gateway-compatible vectors, Binary vectors	Cloning and transformation assays

Comparative Performance Data

Prediction Tool Performance Metrics

Table 4: Performance Comparison of Resistance Gene Prediction Tools

Tool/Method	Approach	Accuracy	Advantages	Limitations
PRGminer	Deep learning (dipeptide composition)	98.75% (training), 95.72% (testing)	High accuracy, classifies into 8 categories	Requires protein sequences
DaapNLRSeek	Diploidy-assisted annotation	Validated in sugarcane genomes	Effective for complex polyploid genomes	Specialized for NLR genes
Alignment-Based	BLAST, HMMER, InterProScan	Varies with homology	Widely accessible, established benchmarks	Fails with low homology
Machine Learning	SVM with various features	~90% in published studies	Balance of performance and interpretability	Lower accuracy than deep learning

Association with Resistance Traits

Experimental data from multiple systems demonstrates the significance of NBS genes in resistance mechanisms:

• In tung trees, the orthologous gene pair Vf11G0978-Vm019719 shows distinct expression patterns correlated with Fusarium wilt resistance, with upregulated expression of Vm019719 in resistant V. montana and downregulated expression of Vf11G0978 in susceptible V. fordii [9].

• Protein-ligand and protein-protein interaction studies reveal strong interactions between putative NBS proteins and ADP/ATP, as well as core proteins of the cotton leaf curl disease virus, suggesting direct involvement in pathogen recognition [14].

• Integration of somatic mutation and gene expression data in cancer research has identified subtypes with significant association to overall survival, demonstrating the broader applicability of these analytical frameworks [115].

This comparison guide establishes comprehensive benchmarks for evaluating novel NBS genes against established resistance genes and pathways. The integration of computational prediction tools, functional validation methodologies, and multi-omics approaches provides a robust framework for characterizing resistance gene associations. Performance metrics demonstrate that deep learning methods like PRGminer achieve superior accuracy (>95%) in resistance gene identification, while functional studies using VIGS and transcriptional analysis confirm the critical role of specific NBS orthogroups in disease resistance. These standardized approaches and reagents enable systematic comparison of novel resistance genes, accelerating the discovery and utilization of genetic resistance elements in crop improvement and therapeutic development.

Benchmarking Against Known Resistance Genes and Orthogroups

Nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes represent the largest class of plant disease resistance (R) genes, encoding intracellular proteins that play a critical role in effector-triggered immunity (ETI) [73]. During plant evolution, NBS-LRR genes have undergone significant expansion and diversification, with plant genomes containing from several dozen to over a thousand members [14] [17]. This substantial variation, combined with their tendency to form tandemly duplicated clusters, creates both a challenge and opportunity for researchers seeking to identify novel resistance genes [116]. The practice of benchmarking newly identified NBS-LRR genes against established orthogroups and characterized resistance genes has therefore become a fundamental methodology in plant immunity research, enabling scientists to prioritize candidate genes for functional validation and understand evolutionary relationships across species [117].

The genomic landscape of NBS-LRR genes reveals remarkable diversity in organization and content across plant species. Modern sugarcane cultivars exemplify this complexity, where genome-wide analyses have demonstrated that Saccharum spontaneum contributes more differentially expressed NBS-LRR genes to disease resistance than Saccharum officinarum [73]. In tung trees, comparative analysis between Vernicia fordii (susceptible to Fusarium wilt) and its resistant counterpart Vernicia montana revealed 90 and 149 NBS-LRR genes respectively, highlighting how NBS-LRR repertoire differences can correlate with disease resistance [9]. Such comparative genomic approaches provide the foundation for effective benchmarking strategies that bridge evolutionary analysis and functional discovery.

Current Approaches to Orthology Benchmarking

Ortholog Identification Methods and Performance Metrics

Orthology benchmarking relies on sophisticated computational methods to identify evolutionarily related genes across species. The Quest for Orthologs consortium maintains reference proteomes and provides standardized benchmarking services, establishing community-approved frameworks for performance assessment [117]. These benchmarks evaluate methods based on their ability to balance species-mixing (identifying homologous cell types across species) and biology conservation (preserving biological heterogeneity after integration) [118].

Established ortholog identification methods demonstrate characteristic performance trade-offs between sensitivity and selectivity. InParanoid consistently ranks highly for identifying functionally equivalent proteins, particularly when measuring conservation of molecular function through InterPro accession numbers [119]. OrthoMCL offers a robust graph-clustering approach that handles larger datasets effectively, while best bidirectional hit (BBH) methods excel at identifying one-to-one orthologs but struggle with complex many-to-many relationships resulting from gene duplications [119]. The emergence of tools like SAMap has advanced orthology detection for evolutionarily distant species by using iterative BLAST analysis to construct gene-gene homology graphs, though at increased computational cost [118].

Table 1: Performance Characteristics of Major Ortholog Identification Methods

Method	Algorithm Type	Strengths	Limitations
InParanoid	Sequence similarity-based (BLAST)	High functional similarity prediction, good for closely related species	Lower performance with deep evolutionary relationships
OrthoMCL	Graph clustering (Markov Cluster Algorithm)	Handles complex many-to-many relationships, good for large datasets	May create overly inclusive groups in highly duplicated families
Best Bidirectional Hit (BBH)	Pairwise comparison	Simple, fast, high precision for one-to-one orthologs	Cannot detect co-orthologs from gene duplications
SAMap	Reciprocal BLAST with iterative updating	Excellent for distant species, detects paralog substitution	Computationally intensive, designed for whole-body alignment

Benchmarking Metrics and Assessment Frameworks

Effective benchmarking requires multiple assessment metrics that evaluate different aspects of ortholog prediction quality. The generalized species tree discordance test measures the topological distance between gene trees built from predicted orthologs and the underlying species phylogeny, with lower Robinson-Foulds distances indicating better performance [117]. Conservation of functional parameters assesses whether orthologous pairs maintain similar expression profiles, protein-protein interactions, and molecular functions as defined by Gene Ontology terms [119].

Recent innovations in assessment methodologies include the Accuracy Loss of Cell type Self-projection (ALCS) metric, which specifically quantifies the degree of blending between cell types after integration, thus identifying overcorrection of cross-species heterogeneity that may obscure species-specific cell types [118]. This metric is particularly valuable for NBS-LRR benchmarking, as it helps maintain distinguishing features of lineage-specific resistance genes while still identifying conserved orthologs.

Table 2: Key Metrics for Orthology Benchmarking

Metric Category	Specific Metrics	Application to NBS-LRR Research
Species Mixing	Alignment score, Species-mixing score	Measures integration of homologous NBS-LRR genes across species
Biology Conservation	ALCS, Biological conservation score	Preserves species-specific NBS-LRR expansions and deletions
Functional Conservation	InterPro accession conservation, GO term similarity	Assesses functional equivalence of disease resistance mechanisms
Phylogenetic Accuracy	Robinson-Foulds distance, Species tree discordance	Evaluates evolutionary relationships of NBS-LRR orthogroups

Experimental Protocols for Benchmarking Novel NBS-LRR Genes

Identification and Classification of NBS-LRR Genes

The initial step in benchmarking novel NBS-LRR genes involves comprehensive identification and classification using established domain architecture criteria. Researchers typically employ HMMER software with Pfam domain models (NB-ARC domain: PF00931) to scan proteome datasets, followed by validation using InterProScan [73] [9]. NBS-LRR genes are classified into subfamilies based on their N-terminal domains: TNL (TIR-NBS-LRR), CNL (CC-NBS-LRR), and RNL (RPW8-NBS-LRR) [17]. This classification provides the foundational framework for subsequent orthology analysis, as different subfamilies often exhibit distinct evolutionary patterns and functional constraints.

Protocol implementation example from tung tree research demonstrates this process: identification of 239 NBS-LRR genes across two genomes (90 in V. fordii and 149 in V. montana) followed by classification into seven structural subgroups [9]. Similarly, analysis of Dioscorea rotundata identified 167 NBS-LRR genes, with 166 belonging to the CNL subclass and only one to the RNL subclass, revealing monocot-specific patterns of TNL absence [17]. These structured identification pipelines enable meaningful cross-species comparisons and orthogroup assignments.

Figure 1: Experimental Workflow for Benchmarking Novel NBS-LRR Genes Against Known Orthogroups

Orthology Analysis and Evolutionary Benchmarking

Once identified and classified, NBS-LRR genes are subjected to orthology analysis using multiple methods to establish evolutionary relationships. The OrthoFinder package implements a robust phylogenomic approach using DIAMOND for sequence similarity searches and the MCL clustering algorithm for orthogroup inference [14]. For NBS-LRR genes specifically, researchers often supplement this with MCScanX for intraspecies collinearity analysis, identifying tandem duplication events that drive resistance gene expansion [73].

Evolutionary benchmarking incorporates calculation of non-synonymous (Ka) and synonymous (Ks) substitution rates to detect selection pressures acting on NBS-LRR genes. Studies frequently reveal progressive trends of positive selection on NBS-LRR genes, particularly in ligand-binding regions involved in pathogen recognition [73]. In Brassica rapa research, evolutionary analysis demonstrated relatively higher relaxation of selective constraints on the TNL group compared to CNL genes after duplication events, resulting in differential accumulation of these subfamilies [120]. Such evolutionary profiling provides critical context for benchmarking novel NBS-LRR genes against established orthogroups with known functions.

Functional Validation and Expression Benchmarking

The ultimate test of benchmarking accuracy comes from functional validation experiments that connect phylogenetic relationships to biological activity. Virus-induced gene silencing (VIGS) has emerged as a powerful technique for functional characterization, as demonstrated in tung tree studies where silencing of Vm019719 (a benchmarked NBS-LRR gene) compromised resistance to Fusarium wilt [9]. Similarly, transcriptome profiling across multiple disease conditions provides expression-based validation, as exemplified by sugarcane research where NBS-LRR genes from S. spontaneum showed significantly higher differential expression in modern cultivars compared to those from S. officinarum [73].

Advanced functional benchmarking now incorporates cross-species integration of single-cell RNA sequencing data using algorithms like scANVI, scVI, and SeuratV4, which balance species-mixing and biology conservation [118]. These approaches enable researchers to transfer cell type annotations across species based on conserved expression patterns of NBS-LRR genes, providing unprecedented resolution for functional benchmarking. The BENchmarking strateGies for cross-species integrAtion of singLe-cell RNA sequencing data (BENGAL) pipeline offers a standardized framework for such analyses, incorporating multiple metrics to assess integration quality [118].

Research Toolkit for NBS-LRR Gene Benchmarking

Table 3: Essential Research Reagents and Computational Tools for NBS-LRR Benchmarking

Tool/Reagent	Type	Function in Benchmarking	Example Implementation
HMMER with Pfam NB-ARC	Computational Tool	Identifies NBS domain-containing genes from proteomes	Tung tree study identifying 239 NBS-LRR genes [9]
OrthoFinder	Computational Tool	Infers orthogroups and gene families	Analysis of 12,820 NBS genes across 34 species [14]
MCScanX	Computational Tool	Detects tandem duplications and collinearity	Sugarcane NBS-LRR evolutionary analysis [73]
VIGS System	Biological Reagent	Functional validation through gene silencing	Confirming Vm019719 role in Fusarium wilt resistance [9]
CRISPR/Cas9	Biological Reagent	Targeted diversification of NBS-LRR clusters	Generating novel R gene paralogs in soybean [116]
BENGAL Pipeline	Computational Tool	Cross-species integration of scRNA-seq data	Benchmarking 28 integration strategies [118]

Benchmarking against known resistance genes and orthogroups represents a cornerstone methodology in plant immunity research, enabling systematic prioritization of candidate genes and evolutionary interpretation of NBS-LRR diversity. The integration of computational orthology assessment with functional validation creates a powerful framework for translating genomic discoveries into disease resistance applications. As sequencing technologies advance and more plant genomes become available, benchmarking approaches will increasingly incorporate single-cell transcriptomics, pan-genome analyses, and machine learning algorithms to enhance prediction accuracy. The research community's ongoing development of standardized benchmarks through initiatives like the Quest for Orthologs consortium ensures that NBS-LRR gene characterization will continue to benefit from rigorous, comparable assessment methodologies across studies and species [117]. For plant breeders and biotechnology researchers, these benchmarking strategies provide the critical foundation for deploying NBS-LRR genes in crop improvement programs aimed at enhancing disease resistance in agricultural systems.

Protein interaction studies are fundamental to advancing our understanding of biological processes, from immune signaling in plants to drug discovery in humans. Within the specific context of benchmarking novel Nucleotide-Binding Site (NBS) genes against known resistance (R) genes, two areas are of paramount importance: the recognition of pathogen effectors by plant immune receptors and the binding of ligands (including drugs) to their protein targets. Effector recognition, particularly by NBS-Leucine-Rich Repeat (NLR) proteins, is the frontline of plant innate immunity [121] [14]. Simultaneously, accurately predicting and measuring protein-ligand interactions is a core challenge in structural biology and pharmacology [122]. This guide objectively compares key methods in both domains, providing performance data and experimental protocols to inform research on NBS gene function and engineering.

Comparative Analysis of Computational Methods for Protein-Ligand Interaction

Accurately predicting protein-ligand interaction energies is critical for evaluating NBS domain function and for drug discovery. Classical forcefields often struggle with non-covalent interactions, while high-level quantum-chemical methods are too computationally expensive for large complexes [122]. Below is a comparison of modern, low-cost computational methods benchmarked against the PLA15 dataset, a standard that uses fragment-based decomposition to estimate interaction energies at the highly accurate DLPNO-CCSD(T) level of theory [122].

Table 1: Benchmarking Performance of Low-Cost Methods for Protein-Ligand Interaction Energy Prediction on the PLA15 Set

Method	Type	Mean Absolute Percent Error (%)	Spearman ρ (Rank Correlation)	Key Strengths and Weaknesses
g-xTB	Semiempirical	6.1	0.98	Best overall accuracy and reliability; consistent performance without outliers.
GFN2-xTB	Semiempirical	8.2	0.96	High accuracy, strong correlation; a robust alternative.
UMA-medium	Neural Network Potential (NNP)	9.6	0.98	Top-performing NNP but shows consistent overbinding tendency.
eSEN-OMol25	Neural Network Potential (NNP)	10.9	0.95	Good accuracy but less reliable than top semiempirical methods.
AIMNet2	Neural Network Potential (NNP)	22.1 - 27.4	0.77 - 0.95	Performance highly dependent on charge-handling method; can be unstable.
Egret-1	Neural Network Potential (NNP)	24.3	0.88	Moderate accuracy, generally underbinds ligands.
ANI-2x	Neural Network Potential (NNP)	38.8	0.61	Lower accuracy and poor correlation on protein-scale systems.

The data reveals a clear performance gap, with semiempirical methods like g-xTB and GFN2-xTB currently outperforming neural network potentials for this specific task. A critical finding is that proper handling of electrostatic charges is a major differentiator; methods that inadequately account for charge perform poorly on the PLA15 set, which includes charged ligands and proteins [122].

Experimental Protocol: Benchmarking Protein-Ligand Interaction Energy

Objective: To calculate and validate the protein-ligand interaction energy for a complex using a reference benchmark like PLA15. Methodology Summary:

• System Preparation: Obtain the Protein Data Bank (PDB) file for the complex. Preprocess the file to generate three separate coordinate files: the full protein-ligand complex, the protein alone, and the ligand alone.
• Energy Calculation: Use the desired low-cost method (e.g., g-xTB) to compute the single-point energy for each of the three systems. This requires specifying any formal charges present in the system.
• Interaction Energy Calculation: The interaction energy is calculated using the formula: E_interaction = E_complex - (E_protein + E_ligand).
• Validation: Compare the calculated interaction energy against the reference value provided by the benchmark set to evaluate the method's accuracy [122].

Comparative Analysis of Experimental Methods for Effector Recognition

Studying how plant NLR immune receptors recognize pathogen effectors is central to understanding NBS gene function and for engineering disease resistance. Key methods range from in planta cell-death assays to quantitative biophysical techniques.

Table 2: Key Experimental Methods for Studying Effector-Recognizer Interactions

Method	Key Application in NBS Research	Experimental Readout	Key Performance Characteristics
In planta Cell-Death Assay	Functional validation of effector recognition by an NLR pair [121].	Hypersensitive response (HR) visualized as localized tissue collapse; can be scored with a cell-death index or quantified via ion leakage [121].	Provides functional, physiological relevance. Can be performed in model plants like N. benthamiana. Semi-quantitative.
Yeast-Two-Hybrid (Y2H)	Detecting direct protein-protein interactions in an intracellular environment (e.g., HMA domain binding to AVR-Pik) [121].	Growth of yeast on selective media and reporter gene activation.	Good for initial screening of direct interactions. May miss interactions requiring plant-specific post-translational modifications.
Surface Plasmon Resonance (SPR)	Quantifying the affinity and kinetics of direct binding (e.g., between HMA domains and effector variants) [121].	Resonance units change over time, allowing calculation of association (kon) and dissociation (koff) rate constants, and equilibrium binding constant (KD).	Provides highly quantitative, kinetic data. Requires purified proteins.
HT-PELSA	High-throughput detection of protein-ligand and protein-protein interactions across the proteome, including membrane proteins [123].	Mass spectrometry-based quantification of peptide stability changes upon ligand binding.	Unbiased, proteome-wide scope. Works with complex samples like cell lysates. 100x faster than predecessor PELSA.
MOnSTER	Bioinformatics tool to identify clusters of conserved motifs (CLUMPs) in effector proteins, aiding in their prediction and characterization [124].	A CLUMP-score based on physicochemical properties of amino acids and motif occurrence.	Reduces motif redundancy. Successfully identifies known oomycete effector motifs (RxLR, CRN) and novel motifs in nematodes.

Experimental Protocol: Structure-Guided Engineering of an NLR Receptor

Objective: To expand the effector recognition profile of a plant NLR receptor using targeted mutation. Background: The rice NLR Pikp binds the blast fungus effector AVR-PikD via its integrated Heavy Metal Associated (HMA) domain but does not recognize variants AVR-PikE and AVR-PikA. The Pikm allele, with a different HMA sequence, recognizes all three [121]. Methodology Summary:

• Structural Analysis: Compare crystal structures of Pikp-HMA and Pikm-HMA bound to different AVR-Pik effectors to identify key residue differences at the binding interfaces.
• Site-Directed Mutagenesis: Engineer the Pikp-HMA domain by grafting the Pikm residues onto the Pikp backbone. A key successful mutant was PikpNK-KE (Asn261Lys/Lys262Glu) [121].
• Functional Validation in Planta:
  • Co-express the engineered Pikp NLR (helper and sensor pairs) with different AVR-Pik effector variants in N. benthamiana leaves via agroinfiltration.
  • Monitor for a hypersensitive response (cell death) over 2-7 days. Score cell death visually (e.g., 0-5 index) and/or quantify it by measuring ion leakage from leaf discs [121].
• Biophysical Binding Validation:
  • Y2H: Clone the wild-type and mutant HMA domains and effectors into binding and activation domain vectors. Test for interaction on selective media.
  • SPR: Immobilize the HMA domain on a sensor chip. Flow purified effector variants over the chip to measure binding affinity (K_D) and kinetics [121].
• Structural Validation: Solve the crystal structure of the engineered HMA domain in complex with the newly recognized effectors to confirm the predicted interaction mechanism [121].

Integrated Workflow for NBS Gene and Effector Characterization

Combining computational and experimental methods provides the most powerful approach for benchmarking novel NBS genes. The workflow below outlines how these tools can be integrated, from initial genome-wide analysis to functional validation, with clear feedback loops for engineering.

The Scientist's Toolkit: Essential Research Reagents and Solutions

This section details key reagents, tools, and datasets essential for conducting research in protein interactions, effector recognition, and NBS gene benchmarking.

Table 3: Essential Research Reagents and Tools for Protein Interaction Studies

Tool / Reagent	Function / Application	Specific Examples / Notes
PLA15 Benchmark Set	A gold-standard dataset for validating protein-ligand interaction energy prediction methods. Contains 15 protein-ligand complexes with reference energies [122].	Critical for benchmarking computational chemistry methods like g-xTB and NNPs.
MOnSTER	A bioinformatics tool that identifies and scores clusters of non-redundant motifs (CLUMPs) in protein sequences, highly useful for characterizing effector proteins [124].	Successfully identifies known oomycete motifs (RxLR, CRN) and novel motifs in plant-parasitic nematode effectors.
HT-PELSA	A high-throughput experimental method to detect protein-ligand interactions across the entire proteome by measuring ligand-induced protein stability [123].	Works with complex samples like cell lysates and tissue extracts. Enables study of membrane proteins, which are often intractable.
PLIP (Protein-Ligand Interaction Profiler)	A web server and tool for fully automated detection and analysis of non-covalent interactions in 3D protein structures [125].	Useful for characterizing interactions in crystal structures of effector-NBS domain complexes (e.g., AVR-Pik with Pik-HMA).
HMMER with Pfam NB-ARC HMM	Software for identifying NBS-encoding genes in genome sequences using Hidden Markov Models [14] [43].	The Pfam NB-ARC domain (PF00931) is the standard model for discovering NBS-LRR resistance gene analogues.
OrthoFinder	A tool for clustering genes into orthogroups across multiple species, essential for comparative evolutionary analysis of NBS gene families [14].	Identifies core orthogroups conserved across plants and species-specific expansions, informing functional studies.
VIGS (Virus-Induced Gene Silencing)	A technique for transient gene silencing in plants to rapidly assess the function of NBS genes in disease resistance [14].	Used to demonstrate the role of specific NBS genes (e.g., GaNBS) in virus resistance in cotton.

Conclusion

The systematic benchmarking of novel NBS genes against known resistance genes is paramount for advancing our understanding of plant immunity and harnessing this knowledge for therapeutic and agricultural applications. This synthesis of foundational knowledge, methodological advances, troubleshooting strategies, and validation frameworks provides researchers with a comprehensive toolkit. Future directions should focus on the development of standardized, community-accepted benchmark resources that can keep pace with the discovery of new resistance mechanisms. Furthermore, integrating these genomic insights with clinical and field data will be crucial for translating NBS gene research into durable disease resistance strategies, ultimately contributing to improved crop resilience and informed drug discovery pipelines. The continued evolution of bioinformatics tools, particularly deep learning models, promises to further accelerate the discovery and functional characterization of this critical gene family.

References

• 1. Computational Strategies for Elucidating Plant Disease ... [https://www.sciencedirect.com/science/article/abs/pii/S0885576525003790]
• 2. Identification and distribution of the NBS-LRR gene family in ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-015-1554-9]
• 3. A Genome-Wide Comparison of NB-LRR Type ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC3887800/]
• 4. Comparative analysis of NBS domain sequences ... [https://www.sciencedirect.com/science/article/abs/pii/S1055790303002744]
• 5. Genome-wide identification and characterization of NBS ... [https://www.nature.com/articles/s41598-025-03507-5]
• 6. Genome-wide analysis of NBS-LRR genes in Rosaceae ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1052191/full]
• 7. Genome-Wide Identification of NBS-LRR Family in Three ... [https://www.mdpi.com/2073-4425/16/6/680]
• 8. Functional characterization of NBS-LRR genes reveals an ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10898138/]
• 9. Functional characterization of NBS-LRR genes reveals an ... [https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-024-01836-x]
• 10. Overexpression of a novel CC-NBS-LRR gene NtRPP13 ... [https://pubmed.ncbi.nlm.nih.gov/41269335/]
• 11. Genome-Wide Characterization and Expression Pattern ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12469828/]
• 12. PRGminer: harnessing deep learning for the prediction of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12170542/]
• 13. The Oryza australiensis genome reveals potential as a ... [https://www.nature.com/articles/s41598-025-13310-x]
• 14. Comparative analysis, diversification, and functional ... [https://www.nature.com/articles/s41598-024-62876-5]
• 15. Identification and Characterization of NBS Resistance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8585750/]
• 16. Phylogenetic, Structural, and Evolutionary Insights into ... [https://www.mdpi.com/1422-0067/26/5/1828]
• 17. Genome-Wide Identification and Evolutionary Analysis of ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.00484/full]
• 18. Comparative analysis of the NLR gene family in ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1681919/full]
• 19. Genome-Wide Analysis of NBS-LRR Genes in Sorghum ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4714652/]
• 20. PRGminer: harnessing deep learning for the prediction of ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1411525/full]
• 21. Comparative analysis, diversification, and functional ... [https://pubmed.ncbi.nlm.nih.gov/38789717/]
• 22. The roles of gene duplications in the dynamics ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11285756/]
• 23. Recurrent Tandem Gene Duplication Gave Rise to ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC2878002/]
• 24. Bioinformatic-Based Approaches for Disease-Resistance ... [https://www.mdpi.com/2073-4395/11/11/2259]
• 25. SegMantX: A Novel Tool for Detecting DNA Duplications ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12508797/]
• 26. Plant NBS-LRR proteins in pathogen sensing and host defense [https://pmc.ncbi.nlm.nih.gov/articles/PMC1973153/]
• 27. The Arabidopsis effector-triggered immunity landscape is ... [https://www.nature.com/articles/s41598-022-10410-w]
• 28. Genome-Wide Characterization and Expression Pattern ... [https://pubmed.ncbi.nlm.nih.gov/41009627/]
• 29. Prediction of NB‐LRR resistance genes based on full‐ ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9322396/]
• 30. A novel TIR-NBS-LRR gene regulates immune response to ... [https://www.sciencedirect.com/science/article/pii/S2214514122000733]
• 31. Genetically engineered plant endophytes broaden effector- ... [https://pubmed.ncbi.nlm.nih.gov/41125075/?utm_source=FeedFetcher&utm_medium=rss&utm_campaign=None&utm_content=03U57xZNFqBns5ssN0Y3xyHC6jtLaQ6GKGz6zlUID6x&fc=None&ff=20251116121945&v=2.18.0.post22+67771e2]
• 32. Genome-wide presence/absence variation discovery and ... [https://pubmed.ncbi.nlm.nih.gov/37353009/]
• 33. Two complementary genes in a presence-absence ... [https://www.nature.com/articles/s41467-023-40189-x]
• 34. Plant NBS-LRR proteins: adaptable guards - Genome Biology [https://genomebiology.biomedcentral.com/articles/10.1186/gb-2006-7-4-212]
• 35. Genomic Organization and Comparative Phylogenic Analysis of... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7065440/]
• 36. Identification and Characterization of NBS Resistance ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.758559/full]
• 37. Isolation and characterization of NBS–LRR resistance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4684051/]
• 38. Identification of the blast resistance in three elite restorer lines... genes [https://phytopatholres.biomedcentral.com/articles/10.1186/s42483-022-00120-6]
• 39. Genome-wide analysis of NBS-LRR genes revealed ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9986449/]
• 40. conventional and deep learning frameworks in genome ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10998533/]
• 41. BLAST: Basic Local Alignment Search Tool [https://blast.ncbi.nlm.nih.gov/Blast.cgi]
• 42. BLAST: Compare & identify sequences - NCBI ... [https://guides.lib.berkeley.edu/ncbi/blast]
• 43. Genome-wide comparative analysis of NBS-encoding genes ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-15-3]
• 44. usadellab/Helixer: Using Deep Learning to predict gene ... [https://github.com/weberlab-hhu/Helixer]
• 45. AlphaGenome: AI for better understanding the genome [https://deepmind.google/blog/alphagenome-ai-for-better-understanding-the-genome/]
• 46. Target enrichment improves culture-independent detection ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10995632/]
• 47. Comparative diagnostic performance of metagenomic and ... [https://www.nature.com/articles/s41598-025-11834-w]
• 48. Performance of a hybrid capture-based target enrichment ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11705961/]
• 49. Review Targeted next-generation sequencing (tNGS): an ... [https://www.sciencedirect.com/science/article/pii/S1876034125002850]
• 50. The NLR gene family: from discovery to present day - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC11171412/]
• 51. NLRs in plant immunity: Structural insights and molecular ... [https://www.sciencedirect.com/science/article/pii/S2772899425000096]
• 52. Whole genome sequencing reveals transcriptional and ... [https://www.nature.com/articles/s41598-025-18334-x]
• 53. Comparative analysis of targeted long read sequencing ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5530509/]
• 54. Nanopore adaptive sampling to identify the NLR gene family ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-025-11295-5]
• 55. Comparative-genomic analysis reveals dynamic NLR gene ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2023.1141194/full]
• 56. Discovery of functional NLRs using expression level, high- ... [https://www.nature.com/articles/s41477-025-02110-w]
• 57. DaapNLRSeek, prediction, and evolution of resistance ... [https://www.sciencedirect.com/science/article/pii/S2589004225011307]
• 58. Benchmarking Orthogroup Inference Accuracy [https://pmc.ncbi.nlm.nih.gov/articles/PMC7738749/]
• 59. Comparative analysis of gene family evolution ... [https://www.nature.com/articles/s41437-025-00805-6]
• 60. Coloring the edges of a plotted tree by the marginal ... [https://blog.phytools.org/2023/04/coloring-edges-of-plotted-tree-by_18.html]
• 61. OrthoBrowser: gene family analysis and visualization [https://pubmed.ncbi.nlm.nih.gov/39959758/]
• 62. Genome-Wide Identification of NBS-LRR Family in Three ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12192410/]
• 63. Genome-Wide Characterization and Expression Pattern ... [https://www.mdpi.com/1422-0067/26/18/9063]
• 64. Progress in Transcriptomics and Metabolomics in Plant ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12191765/]
• 65. Conserved stress-responsive genes involved in the early ... [https://www.nature.com/articles/s41598-025-01436-x]
• 66. Integrating multi-omics and machine learning for disease ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12204941/]
• 67. Multi-omics analyses reveal regulatory networks ... [https://www.nature.com/articles/s41467-025-65299-6]
• 68. Genome-scale examination of NBS-encoding genes in ... [https://www.nature.com/articles/s41598-018-21738-7]
• 69. Identification of resistance gene analogs of the NBS-LRR ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.1036029/full]
• 70. Mind the Gaps: Ben Busby on the Future of Variant ... [https://www.opencravat.org/mind-the-gaps-ben-busby-on-the-future-of-variant-annotation/]
• 71. Population-based, first-tier genomic newborn screening in ... [https://www.nature.com/articles/s41591-024-03465-x]
• 72. Genome structural variation discovery and genotyping - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4108431/]
• 73. Genome-wide analysis of NBS-LRR genes revealed ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2023.1091567/full]
• 74. Genome-Wide Analysis Revealed NBS-LRR Gene ... [https://www.mdpi.com/2073-4395/13/10/2583]
• 75. Analytical Validation of a Genomic Newborn Screening ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12551001/]
• 76. Structural variation analysis of 6500 whole genome ... [https://www.nature.com/articles/s41525-021-00267-9]
• 77. The genetic control of rapid genome content divergence in ... [https://elifesciences.org/reviewed-preprints/108238]
• 78. Garbage In, Garbage Out: Dealing with Data Errors ... [https://blog.omni-inc.com/blog/garbage-in-garbage-out-dealing-with-data-errors-bioinformatics]
• 79. doepipeline: a systematic approach to optimizing multi-level ... [https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3091-z]
• 80. Hyperparameter Tuning and Pipeline Optimization via Grid ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8540332/]
• 81. Protein remote homology detection and structural ... [https://www.nature.com/articles/s41587-023-01917-2]
• 82. Performance comparison of four exome capture platforms ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12551171/]
• 83. Embracing the dropouts in single-cell RNA-seq analysis - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC7054558/]
• 84. The impact of dropouts in scRNAseq dense neighborhood ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11992407/]
• 85. Protein remote homology detection and structural ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11180608/]
• 86. Analysis of TIR- and non-TIR- NBS -LRR disease resistance ... gene [https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-13-502]
• 87. -wide comparative analysis of the nucleotide-binding... Genome [https://pmc.ncbi.nlm.nih.gov/articles/PMC9434374/]
• 88. Validating the AMRFinder Tool and Resistance Gene ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6811410/]
• 89. Pipeline validation for the identification of antimicrobial ... [https://www.nature.com/articles/s41598-023-42154-6]
• 90. Feasibility and clinical utility of expanded genomic ... [https://www.nature.com/articles/s41591-025-03945-8]
• 91. A Genomic Sequencing Approach to Newborn Mass ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12534850/]
• 92. Benchmarking the Engineering Design Capabilities of LLMs [https://arxiv.org/html/2509.16204v2]
• 93. Benchmarking AI Agents in 2025: Top Tools, Metrics & ... [https://metadesignsolutions.com/benchmarking-ai-agents-in-2025-top-tools-metrics-performance-testing-strategies/]
• 94. NLGenomeSweeper: A Tool for Genome -Wide NBS -LRR Resistance ... [https://pubmed.ncbi.nlm.nih.gov/32245073/]
• 95. ntoda03/NLGenomeSweeper: Identification of NBS -LRR genes in... [https://github.com/ntoda03/NLGenomeSweeper]
• 96. Genome-Wide Analysis of NBS-LRR Genes From an Early ... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.880071/full]
• 97. Virus-induced gene silencing is a versatile tool for unraveling the... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4085877/]
• 98. Efficient Virus-Induced Gene Silencing (VIGS) Method for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12115190/]
• 99. Rapid Mining of Candidate Genes for Verticillium Wilt ... [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2021.703011/full]
• 100. Developing a homology-driven gene silencing system in Iris japonica... [https://maxapress.com/article/doi/10.48130/opr-0025-0033?viewType=HTML]
• 101. Cotton CC-NBS-LRR Gene GbCNL130 Confers Resistance to ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8456104/]
• 102. Genome-wide association studies [https://www.nature.com/articles/s43586-021-00056-9]
• 103. From Genotype to Phenotype: A Primer on the Functional ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6003539/]
• 104. Genome-wide association analysis uncovers rice blast ... [https://www.nature.com/articles/s42003-024-06244-z]
• 105. Genome-wide analysis of NBS-encoding disease resistance ... [https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-14-109]
• 106. GWAS SVatalog: a visualization tool to aid fine-mapping of ... [https://www.nature.com/articles/s41437-025-00809-2]
• 107. Establishing Best Practices for Clinical GWAS - PubMed Central [https://pmc.ncbi.nlm.nih.gov/articles/PMC12250051/]
• 108. The Diversification of Plant NBS-LRR Defense Genes Directs ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5026261/]
• 109. Distinct Evolutionary Patterns of NBS-Encoding Genes in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7365912/]
• 110. Evolution patterns of NBS genes in the genus Dendrobium ... [https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-022-03904-2]
• 111. A compendium of genome-wide sequence reads from NBS ... [https://www.nature.com/articles/s41598-020-67848-z]
• 112. A Genome-Wide Comparison of NB-LRR Type ... [https://www.sciencedirect.com/science/article/pii/S1016847823071157]
• 113. Extensive immune receptor repertoire diversity in disease- ... [https://www.sciencedirect.com/science/article/pii/S0960982224010005]
• 114. Natural variation in the consequences of gene ... [https://elifesciences.org/articles/70564]
• 115. Integrating genetic and gene expression data in network ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12070578/]
• 116. Novel disease resistance gene paralogs created by ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8184530/]
• 117. The Quest for Orthologs benchmark service and consensus ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7319555/]
• 118. Benchmarking strategies for cross-species integration of ... [https://www.nature.com/articles/s41467-023-41855-w]
• 119. Benchmarking ortholog identification methods using functional ... [https://genomebiology.biomedcentral.com/articles/10.1186/gb-2006-7-4-r31]
• 120. Genome-wide identification of NBS-encoding resistance genes in Brassica rapa - PMC [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2777221/]
• 121. Protein engineering expands the effector recognition profile of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6768660/]
• 122. Benchmarking Protein–Ligand Interaction Energy | Rowan [https://www.rowansci.com/blog/benchmarking-protein-ligand-interaction-energy]
• 123. New method offers broader and faster detection of protein- ... [https://www.embl.org/news/science-technology/new-method-offers-broader-and-faster-detection-of-protein-ligand-interactions/]
• 124. Identification and characterization of specific motifs in ... [https://www.nature.com/articles/s42003-024-06515-9]
• 125. PLIP 2025: introducing protein-protein interactions to the ... [https://pubmed.ncbi.nlm.nih.gov/40347107/]