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HRPv2 automates and enhances full-length homology-based R-gene prediction, specifically for NB-LRR proteins in plants, by replacing manual curation with a reproducible filtering framework and improving homology search steps. This addresses the limitation of domain-based methods that cannot recover missed or misannotated gene loci. The enhanced method demonstrably increases the annotation of full-length NB-LRRs compared to its predecessor, HRP.
Plant R-gene annotation accuracy jumps by recovering missed loci, a critical step for crop improvement.
Motivation: Plant disease resistance genes, particularly those encoding NB-LRR proteins, are important targets for crop improvement. Proteome-based domain or motif searches can only identify NB-LRRs among existing gene models, meaning they cannot recover loci that have been missed or incorrectly predicted by the reference annotation. The full-length, homology-based R-gene prediction (HRP) method circumvents this issue by reconstructing gene models directly on the genome. However, the original implementation of this method requires several separate phases of classification, comparison and filtering operations. Results: HRPv2 is an automated, enhanced version of the original strategy. The labour-intensive, step-by-step curation process used in HRP has been replaced by a reproducible filtering framework. Enhancing two steps of the homology search process has enabled HRPv2 to better account for the specific NB-LRR variability of the genome. Performance validation confirmed that the number of full-length NB-LRRs annotated in both the automatically predicted gene set of the respective genome assembly and the final NB-LRR repertoire has increased in HRPv2 compared to HRP. Availability and implementation: HRPv2 and its associated documentation and reproducible test data are available at https://github.com/AndolfoG/HRPv2. Detailed installation and dependency information is provided in the repository README. A version-pinned Conda package has also been developed and locally validated to provide a reproducible execution environment.