PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets
PAST: The Pathway Association Studies Tool to Infer Biological Meaning from GWAS Datasets
复制标题
DOI：
10.3390/plants9010058
复制
发表时间：
2020-01
期刊：
影响因子：
--
通讯作者：
Adam Thrash;Juliet D. Tang;Mason DeOrnellis;D. Peterson;M. Warburton
中科院分区：
文献类型：
--
作者：
Adam Thrash;Juliet D. Tang;Mason DeOrnellis;D. Peterson;M. Warburton
近年来，一种利用代谢途径分析来解释全基因组关联研究（GWAS）数据的生物信息学方法已经发展起来，并成功地用于寻找解释植物感兴趣的表型性状的重要途径和机制。然而，实现这种方法的许多脚本并不容易使用，必须为每个项目定制，需要用户监督，处理数据需要24小时以上。PAST（路径关联研究工具），这种方法的一个新的实现，已经开发出来，以解决这些问题。PAST已经被实现为R语言的包。提供了两个用户界面; PAST可以通过在R中加载包并调用其方法来运行，或者通过使用R Shiny引导的用户界面来运行。在测试中，PAST通过并行处理数据，在大约半小时到一小时内完成分析，并产生与先前开发的方法相同的结果。PAST有许多用户指定的选项，可进行最大程度的自定义。因此，为了促进一种强大的新途径分析方法，解释GWAS数据，以找到与感兴趣的性状相关的生物学机制，我们开发了一种更容易获得，更有效和用户友好的工具。这些属性使PAST可供有兴趣将代谢途径与GWAS数据集相关联的研究人员使用，以更好地了解影响表型的遗传结构和机制。
In recent years, a bioinformatics method for interpreting genome-wide association study (GWAS) data using metabolic pathway analysis has been developed and successfully used to find significant pathways and mechanisms explaining phenotypic traits of interest in plants. However, the many scripts implementing this method were not straightforward to use, had to be customized for each project, required user supervision, and took more than 24 h to process data. PAST (Pathway Association Study Tool), a new implementation of this method, has been developed to address these concerns. PAST has been implemented as a package for the R language. Two user-interfaces are provided; PAST can be run by loading the package in R and calling its methods, or by using an R Shiny guided user interface. In testing, PAST completed analyses in approximately half an hour to one hour by processing data in parallel and produced the same results as the previously developed method. PAST has many user-specified options for maximum customization. Thus, to promote a powerful new pathway analysis methodology that interprets GWAS data to find biological mechanisms associated with traits of interest, we developed a more accessible, efficient, and user-friendly tool. These attributes make PAST accessible to researchers interested in associating metabolic pathways with GWAS datasets to better understand the genetic architecture and mechanisms affecting phenotypes.