RWRtoolkit:在任何物种的多路网络上使用随机行走的多组网络分析。

IF 11.8 2区 生物学 Q1 MULTIDISCIPLINARY SCIENCES
David Kainer, Matthew Lane, Kyle A Sullivan, J Izaak Miller, Mikaela Cashman, Mallory Morgan, Ashley Cliff, Jonathon Romero, Angelica Walker, D Dakota Blair, Hari Chhetri, Yongqin Wang, Mirko Pavicic, Anna Furches, Jaclyn Noshay, Meghan Drake, A J Ireland, Ali Missaoui, Yun Kang, John C Sedbrook, Paramvir Dehal, Shane Canon, Daniel Jacobson
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引用次数: 0

摘要

我们将介绍RWRtoolkit,这是一个为R和命令行用户构建的多路生成、探索和统计包。RWRtoolkit能够有效地探索从自定义实验数据和/或从公开可用的数据集生成的大型和高度复杂的生物网络,并且是物种不可知性的。一系列函数可用于查找生物实体之间的拓扑距离,确定感兴趣集合内的关系,搜索感兴趣集合周围的拓扑上下文,以及统计地评估集合内和集合之间的关系强度。命令行接口是为高性能集群系统上的并行化而设计的,它支持高吞吐量分析,如排列测试。包中的几个工具也可以通过KBase web应用程序在可再现的工作流中使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species.

We introduce RWRtoolkit, a multiplex generation, exploration, and statistical package built for R and command-line users. RWRtoolkit enables the efficient exploration of large and highly complex biological networks generated from custom experimental data and/or from publicly available datasets, and is species agnostic. A range of functions can be used to find topological distances between biological entities, determine relationships within sets of interest, search for topological context around sets of interest, and statistically evaluate the strength of relationships within and between sets. The command-line interface is designed for parallelization on high-performance cluster systems, which enables high-throughput analysis such as permutation testing. Several tools in the package have also been made available for use in reproducible workflows via the KBase web application.

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来源期刊
GigaScience
GigaScience MULTIDISCIPLINARY SCIENCES-
CiteScore
15.50
自引率
1.10%
发文量
119
审稿时长
1 weeks
期刊介绍: GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.
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