Fastq2vcf: a concise and transparent pipeline for whole-exome sequencing data analyses.
Fastq2vcf: a concise and transparent pipeline for whole-exome sequencing data analyses.
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DOI：
10.1186/s13104-015-1027-x
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发表时间：
2015-03-08
影响因子：
1.8
通讯作者：
Starmer J
中科院分区：
文献类型：
--
作者：
Gao X;Xu J;Starmer J
全外显子组测序（WES）是一种流行的新一代测序技术，被众多具备不同统计和分析专业水平的实验室所使用。集中式数据库，如序列读取档案库和欧洲核苷酸档案库，允许独立实验室对数据进行重新分析以确认结果并获得更多见解。对新数据和共享数据的获取凸显了对软件的需求，这种软件既要降低生成结果所需的统计和分析专业知识要求，又要在实验室之间推广可重复的方法。
我们开发了fastq2vcf，这是一个使用多个识别器自动进行基因组变异识别过程的流程。fastq2vcf通过无缝集成几种领先的测序分析工具，提供了更高的灵活性、效率和可重复性。它不仅输出每个识别器的带注释的变异识别集，还输出不同识别器共享的一致变异识别集。此外，它可以很容易地定制和扩展。
我们的软件工具会自动为分析WES数据所需的各种工具生成可执行的命令行。它还具有高度可配置性，使用户能够完全控制处理过程，使其在单工作站和并行计算环境中都易于提交和跟踪任务。通过使用这个流程，WES分析可以很容易地重现。
Whole-exome sequencing (WES) is a popular next-generation sequencing technology used by numerous laboratories with various levels of statistical and analytical expertise. Centralized databases, such as the Sequence Read Archive and the European Nucleotide Archive, allow data to be reanalyzed by independent labs to confirm results and derive additional insights. Access to new and shared data highlights the necessity for software that both lowers the statistical and analytical expertise required to generate results and promotes reproducible methodology among laboratories. We have developed fastq2vcf, a pipeline that automates the genomic variant calling process using multiple callers. Fastq2vcf offers improved flexibility, efficiency, and reproducibility by seamlessly integrating several leading sequencing analysis tools. It outputs not only the annotated variant call set for each caller, but also the consensus variant call set shared by different callers. Furthermore, it can be customized and extended easily. Our software tool automatically generates executable command lines for a variety of tools required for analyzing WES data. It is also highly configurable and provides users with complete control of the processing procedure, making it easy to submit and track jobs in both single workstation and parallelized computing environments. By using this pipeline, WES analysis can be easily reproduced.