Institut de Biología Evolutiva, Universitat Pompeu Fabra, Biomedical Research Park (PRBB), Barcelona, Spain.
Bioinformatics. 2011 Jul 1;27(13):1871-2. doi: 10.1093/bioinformatics/btr301. Epub 2011 May 17.
Genome-wide association studies (GWAS) based on single nucleotide polymorphism (SNP) arrays are the most widely used approach to detect loci associated to human traits. Due to the complexity of the methods and software packages available, each with its particular format requiring intricate management workflows, the analysis of GWAS usually confronts scientists with steep learning curves. Indeed, the wide variety of tools makes the parsing and manipulation of data the most time consuming and error prone part of a study. To help resolve these issues, we present GWASpi, a user-friendly, multiplatform, desktop-able application for the management and analysis of GWAS data, with a novel approach on database technologies to leverage the most out of commonly available desktop hardware. GWASpi aims to be a start-to-finish GWAS management application, from raw data to results, containing the most common analysis tools. As a result, GWASpi is easy to use and reduces in up to two orders of magnitude the time needed to perform the fundamental steps of a GWAS.
Freely available on the web at http://www.gwaspi.org. Implemented in Java, Apache-Derby and NetCDF-3, with all major operating systems supported.
基于单核苷酸多态性 (SNP) 阵列的全基因组关联研究 (GWAS) 是检测与人类特征相关的基因座的最广泛应用的方法。由于可用方法和软件包的复杂性，每个方法都有其特定的格式，需要复杂的管理工作流程，因此 GWAS 的分析通常会让科学家面临陡峭的学习曲线。事实上，大量的工具使得数据的解析和操作成为研究中最耗时和最容易出错的部分。为了解决这些问题，我们提出了 GWASpi，这是一个用户友好的、跨平台的、适用于桌面的 GWAS 数据管理和分析应用程序，它采用了一种新颖的数据库技术方法，可以充分利用常见的桌面硬件。GWASpi 旨在成为从原始数据到结果的完整 GWAS 管理应用程序，包含最常见的分析工具。因此，GWASpi 易于使用，并将执行 GWAS 的基本步骤所需的时间减少了两个数量级。
可在 http://www.gwaspi.org 上免费获得。用 Java、Apache-Derby 和 NetCDF-3 实现，支持所有主要操作系统。
