PDF(Pubmed)
Abstract：
BACKGROUND: In the ten years since the initial publication of the RenSeq protocol, the method has proved to be a powerful tool for studying disease resistance in plants and providing target genes for breeding programmes. Since the initial publication of the methodology, it has continued to be developed as new technologies have become available and the increased availability of computing power has made new bioinformatic approaches possible. Most recently, this has included the development of a k-mer based association genetics approach, the use of PacBio HiFi data, and graphical genotyping with diagnostic RenSeq. However, there is not yet a unified workflow available and researchers must instead configure approaches from various sources themselves. This makes reproducibility and version control a challenge and limits the ability to perform these analyses to those with bioinformatics expertise.
RESULTS: Here we present HISS, consisting of three workflows which take a user from raw RenSeq reads to the identification of candidates for disease resistance genes. These workflows conduct the assembly of enriched HiFi reads from an accession with the resistance phenotype of interest. A panel of accessions both possessing and lacking the resistance are then used in an association genetics approach (AgRenSeq) to identify contigs positively associated with the resistance phenotype. Candidate genes are then identified on these contigs and assessed for their presence or absence in the panel with a graphical genotyping approach that uses dRenSeq. These workflows are implemented via Snakemake, a python-based workflow manager. Software dependencies are either shipped with the release or handled with conda. All code is freely available and is distributed under the GNU GPL-3.0 license.
CONCLUSIONS: HISS provides a user-friendly, portable, and easily customised approach for identifying novel disease resistance genes in plants. It is easily installed with all dependencies handled internally or shipped with the release and represents a significant improvement in the ease of use of these bioinformatics analyses.
RESULTS: Here we present HISS, consisting of three workflows which take a user from raw RenSeq reads to the identification of candidates for disease resistance genes. These workflows conduct the assembly of enriched HiFi reads from an accession with the resistance phenotype of interest. A panel of accessions both possessing and lacking the resistance are then used in an association genetics approach (AgRenSeq) to identify contigs positively associated with the resistance phenotype. Candidate genes are then identified on these contigs and assessed for their presence or absence in the panel with a graphical genotyping approach that uses dRenSeq. These workflows are implemented via Snakemake, a python-based workflow manager. Software dependencies are either shipped with the release or handled with conda. All code is freely available and is distributed under the GNU GPL-3.0 license.
CONCLUSIONS: HISS provides a user-friendly, portable, and easily customised approach for identifying novel disease resistance genes in plants. It is easily installed with all dependencies handled internally or shipped with the release and represents a significant improvement in the ease of use of these bioinformatics analyses.
摘要：
背景：自RenSeq协议首次发布以来的十年中，该方法已被证明是研究植物抗病性并为育种计划提供目标基因的有力工具。自从该方法首次发布以来，随着新技术的出现，它继续得到发展，计算能力的增加使新的生物信息学方法成为可能。最近,这包括开发基于k-mer的关联遗传学方法，使用PacBioHiFi数据，和图形基因分型与诊断RenSeq。然而,目前还没有一个统一的工作流程，研究人员必须从各种来源自己配置方法。这使得再现性和版本控制成为挑战，并限制了对具有生物信息学专业知识的人进行这些分析的能力。
结果：这里我们介绍HISS，由三个工作流程组成，这些工作流程将用户从原始的RenSeq读取到识别疾病抗性基因的候选基因。这些工作流程进行来自具有感兴趣的抗性表型的登录名的富集的HiFi读段的组装。然后在关联遗传学方法（AgRenSeq）中使用具有和缺乏抗性的一组种质来鉴定与抗性表型正相关的重叠群。然后在这些重叠群上鉴定候选基因，并使用使用dRenSeq的图形基因分型方法评估其在面板中的存在或不存在。这些工作流是通过Snakemake实现的，基于python的工作流管理器。软件依赖项要么随版本一起提供，要么与conda一起处理。所有代码都是免费提供的，并在GNUGPL-3.0许可证下分发。
结论：HISS提供了一个用户友好的，便携式，和易于定制的方法来识别植物中的新的抗病基因。它可以很容易地安装，所有的依赖关系都在内部处理或随版本一起发布，并且在这些生物信息学分析的易用性方面有了显著的改进。
结果：这里我们介绍HISS，由三个工作流程组成，这些工作流程将用户从原始的RenSeq读取到识别疾病抗性基因的候选基因。这些工作流程进行来自具有感兴趣的抗性表型的登录名的富集的HiFi读段的组装。然后在关联遗传学方法（AgRenSeq）中使用具有和缺乏抗性的一组种质来鉴定与抗性表型正相关的重叠群。然后在这些重叠群上鉴定候选基因，并使用使用dRenSeq的图形基因分型方法评估其在面板中的存在或不存在。这些工作流是通过Snakemake实现的，基于python的工作流管理器。软件依赖项要么随版本一起提供，要么与conda一起处理。所有代码都是免费提供的，并在GNUGPL-3.0许可证下分发。
结论：HISS提供了一个用户友好的，便携式，和易于定制的方法来识别植物中的新的抗病基因。它可以很容易地安装，所有的依赖关系都在内部处理或随版本一起发布，并且在这些生物信息学分析的易用性方面有了显著的改进。
