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基于图的基因本体论挖掘:使用 GOxploreR 分析生物学意义

Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance.

机构信息

Predictive Society and Data Analytics Lab, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.

Computational Systems Biology, Tampere University, Tampere, Korkeakoulunkatu 10, 33720, Tampere, Finland.

出版信息

Sci Rep. 2020 Oct 7;10(1):16672. doi: 10.1038/s41598-020-73326-3.

Abstract

Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the R package GOxploreR. The main features of GOxploreR are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of GOxploreR is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, GOxploreR provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our R package GOxploreR is freely available from CRAN.

摘要

基因本体论(GO)是一个著名的知识库,常用于为生物、医学和临床问题中的基因或基因集分析提供生物学解释。不幸的是,由于 GO 术语数量庞大、它们的层次和连接组织作为有向无环图(DAG)以及缺乏允许显式利用这种结构信息的工具,这些结果的解释具有挑战性。出于这个原因,我们开发了 R 包 GOxploreR。GOxploreR 的主要特点是:(I)轻松直接访问 GO 的结构特征;(II)基于结构的 GO 术语排序;(III)映射到包括可视化功能的简化 GO-DAG;(IV)GO 术语的优先级排序。GOxploreR 的基本思想是利用 GO 的图论视角,如其 DAG 结构和包含的层次级别,累积语义信息。这意味着所有这些功能都增强了对 GO 结构信息的利用,并补充了现有的分析工具。总的来说,GOxploreR 为补充任何类型的分析提供了探索性和验证性工具,这些分析会产生 GO 术语列表,例如来自差异表达基因或基因集、GWAS 或生物标志物的分析。我们的 R 包 GOxploreR 可以从 CRAN 免费获得。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43db/7542435/6c8b7002dafa/41598_2020_73326_Fig1_HTML.jpg

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