ArticleBMC genomics2016
RGAugury: a pipeline for genome-wide prediction of resistance gene analogs (RGAs) in plants.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers.
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143 citing papers in PubMed.
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- Synthetic Raphanobrassica Genome Reveals Functional and Evolutionary Insights Into Clubroot Resistance Genes on Chromosome R5.Plant biotechnology journal · 2026Article
- Genome analysis of Channel millet reveals a wild dodecaploid shaped by environmental variability.Nature communications · 2026Article
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- Haplotype-resolved telomere-to-telomere assembly and haplotype-aware annotation pipeline enable high-quality reannotation of threeHorticulture research · 2026Article
- Transcriptome-metabolome integration reveals MYB/WRKY-RGA network and phenylalanine metabolism in potato late blight resistance.BMC plant biology · 2026Article
- Accessing crop genetic diversity via pangenomics.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- The Genome of the Rice Variety Mowanggu Provides Insight Into Resistance to Magnaporthe oryzae.Molecular plant pathology · 2026Article
- Accelerating Resistance Breeding: Emerging Methods to Identify and Validate Plant Immunity Genes.Plants (Basel, Switzerland) · 2026Review
- Haplotype-resolved chromosome-level genome assembly of an autohexaploid oil camellia tree Camellia osmantha.Scientific data · 2026Article
- The pangenome enhances the understanding of the genetic diversity of papaya.Horticulture research · 2026Article
- Unveiling the early defense response dynamics in grapevines against Plasmopara viticola by single-cell transcriptomics.Genome biology · 2026Article
- Graph pan-genome illuminates evolutionary trajectories and agronomic trait architecture in allotetraploid cotton.Nature genetics · 2026Article
- Improved reannotations of Aegilops umbellulata (PI 554389) genome and transcriptomics data provide candidates for leaf rust resistance for wheat improvement.Scientific reports · 2025Article
- CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences.BMC biotechnology · 2025Article
- Chromosome level assembly of five Brassica rapa and oleracea accessions expand the resistance genes reservoir.Scientific data · 2025Article
- TVIR 2.0: an enhanced database of the vegetables information resources.Horticulture research · 2025Article
83 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
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Abstract
backgroundResistance gene analogs (RGAs), such as NBS-encoding proteins, receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), are potential R-genes that contain specific conserved domains and motifs. Thus, RGAs can be predicted based on their conserved structural features using bioinformatics tools. Computer programs have been developed for the identification of individual domains and motifs from the protein sequences of RGAs but none offer a systematic assessment of the different types of RGAs. A user-friendly and efficient pipeline is needed for large-scale genome-wide RGA predictions of the growing number of sequenced plant genomes.
resultsAn integrative pipeline, named RGAugury, was developed to automate RGA prediction. The pipeline first identifies RGA-related protein domains and motifs, namely nucleotide binding site (NB-ARC), leucine rich repeat (LRR), transmembrane (TM), serine/threonine and tyrosine kinase (STTK), lysin motif (LysM), coiled-coil (CC) and Toll/Interleukin-1 receptor (TIR). RGA candidates are identified and classified into four major families based on the presence of combinations of these RGA domains and motifs: NBS-encoding, TM-CC, and membrane associated RLP and RLK. All time-consuming analyses of the pipeline are paralleled to improve performance. The pipeline was evaluated using the well-annotated Arabidopsis genome. A total of 98.5, 85.2, and 100 % of the reported NBS-encoding genes, membrane associated RLPs and RLKs were validated, respectively. The pipeline was also successfully applied to predict RGAs for 50 sequenced plant genomes. A user-friendly web interface was implemented to ease command line operations, facilitate visualization and simplify result management for multiple datasets.
conclusionsRGAugury is an efficiently integrative bioinformatics tool for large scale genome-wide identification of RGAs. It is freely available at Bitbucket: https://bitbucket.org/yaanlpc/rgaugury .
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