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vcf2gwas:使用 GEMMA 进行全面 GWAS 分析的 Python API。

vcf2gwas: Python API for comprehensive GWAS analysis using GEMMA.

机构信息

Department of Molecular Biology, Max Planck Institute for Developmental Biology, Tuebingen 72076, Germany.

出版信息

Bioinformatics. 2022 Jan 12;38(3):839-840. doi: 10.1093/bioinformatics/btab710.

Abstract

MOTIVATION

Genome-wide association study (GWAS) requires a researcher to perform a multitude of different actions during analysis. From editing and formatting genotype and phenotype information to running the analysis software to summarizing and visualizing the results. A typical GWAS workflow poses a significant challenge of utilizing the command-line, manual text-editing and requiring knowledge of one or more programming/scripting languages, especially for newcomers.

RESULTS

vcf2gwas is a package that provides a convenient pipeline to perform all of the steps of a traditional GWAS workflow by reducing it to a single command-line input of a Variant Call Format file and a phenotype data file. In addition, all the required software is installed with the package. vcf2gwas also implements several useful features enhancing the reproducibility of GWAS analysis.

AVAILABILITY AND IMPLEMENTATION

The source code of vcf2gwas is available under the GNU General Public License. The package can be easily installed using conda. Installation instructions and a manual including tutorials can be accessed on the package website at https://github.com/frankvogt/vcf2gwas.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

全基因组关联研究(GWAS)要求研究人员在分析过程中执行多种不同的操作。从编辑和格式化基因型和表型信息,到运行分析软件,再到总结和可视化结果。典型的 GWAS 工作流程提出了一个重大挑战,即需要利用命令行、手动文本编辑,并需要掌握一种或多种编程语言的知识,尤其是对于新手来说。

结果

vcf2gwas 是一个软件包,它通过将传统 GWAS 工作流程的所有步骤简化为单个命令行输入变体调用格式文件和表型数据文件,提供了一个方便的流水线来执行所有步骤。此外,该软件包还安装了所有必需的软件。vcf2gwas 还实现了几个有用的功能,增强了 GWAS 分析的可重复性。

可用性和实现

vcf2gwas 的源代码根据 GNU 通用公共许可证发布。可以使用 conda 轻松安装该软件包。安装说明和包含教程的手册可以在软件包网站 https://github.com/frankvogt/vcf2gwas 上获取。

补充信息

补充数据可在《生物信息学》在线获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a638/8756188/d269f3bfb720/btab710f1.jpg

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