发表机构
University of Naples ‘Federico II’; BOKU University（那不勒斯费德里科二世大学; 博库斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
HRPv2是HRP的自动化增强版，通过可复现过滤框架和优化同源搜索步骤，在基因组上直接重建基因模型，提高全长NB-LRR抗病基因预测的准确性和数量。
AI 中文摘要
动机：植物抗病基因，尤其是编码NB-LRR蛋白的基因，是作物改良的重要靶标。基于蛋白质组的结构域或基序搜索只能识别现有基因模型中的NB-LRR，这意味着它们无法恢复参考注释中遗漏或错误预测的基因座。全长同源R基因预测（HRP）方法通过在基因组上直接重建基因模型来规避此问题。然而，该方法的原始实现需要多个独立的分类、比较和过滤操作阶段。结果：HRPv2是原始策略的自动化增强版本。HRP中劳动密集、逐步进行的整理过程已被可复现的过滤框架取代。增强同源搜索过程中的两个步骤，使HRPv2能更好地考虑基因组特定的NB-LRR变异性。性能验证确认，与HRP相比，HRPv2在各自基因组组装的自动预测基因集和最终NB-LRR库中注释的全长NB-LRR数量均有所增加。可用性和实现：HRPv2及其相关文档和可复现的测试数据可在该https URL获取。详细的安装和依赖信息在仓库README中提供。还开发了版本固定的Conda包并进行了本地验证，以提供可复现的执行环境。
英文摘要
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 The labour-intensive, step-by-step curation process used in HRP has been replaced by a reproducible filtering framework.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.
CommentsSupplementary data available at https://doi.org/10.6084/m9.figshare.34085769
