SVPLEX：一种用于队列水平结构变异检测的Nextflow流程
SVPLEX: A Nextflow Pipeline for Cohort-level Structural Variant Calling
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中文总结 AI 辅助
SVPLEX是一款用于队列水平结构变异检测的Nextflow流程，集成六种检测工具生成合并共识调用集，支持多平台运行，可用于罕见病变异分析，已开源发布。
中文摘要 AI 辅助
SVPLEX是一种用于从短读长全基因组测序数据中进行队列水平结构变异（SV）检测的Nextflow流程。该流程集成了六种不同优势与劣势的结构变异检测工具，整合了SV的不同层级证据，并生成分析队列的合并共识调用集。调用集过滤通过利用多个单独检测工具的共识，以及确保缺失和重复调用得到测序深度可观测变化的支持来实现。输出的合并队列SV调用集可用于评估队列特异性变异、去除技术假象，并作为罕见病变异优先排序工作流程的输入。SVPLEX具有用户友好、可重复、可扩展的特点，可灵活在本地工作站、高性能计算（HPC）集群或云基础设施上执行。所需输入为目标队列的比对文件，输出为单个合并队列结构变异VCF文件。SVPLEX可在GitHub（bahlolab/SVPLEX）获取，采用MIT开源许可证。
英文摘要
SVPLEX is a Nextflow pipeline for cohort-level structural variant detection from short-read whole-genome sequencing data. The pipeline implements six different structural variant callers with different strengths and weaknesses, integrating different levels of evidence for SVs, and generates a merged consensus callset across the analysis cohort. Callset filtering is achieved by leveraging consensus among multiple individual callers and by ensuring that deletion and duplication calls are supported by observable changes in read depth. The output merged cohort SV callset can then be used to assess cohort-specific variation, remove technical artefacts, and serve as input for rare disease variant prioritisation workflows. SVPLEX is user-friendly, reproducible, scalable, and can be executed flexibly on either a local workstation, a high-performance compute (HPC) cluster, or deployed on cloud infrastructure. The required inputs are alignment files for the cohort of interest, and the output is a single merged cohort structural variant VCF. SVPLEX is available on GitHub (bahlolab/SVPLEX) and is licensed under the MIT open-source licence.
