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KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis

Nucleic acids research, 2021-07, Vol.49 (W1), p.W317-W325 [Peer Reviewed Journal]

The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. 2021 ;ISSN: 0305-1048 ;EISSN: 1362-4962 ;DOI: 10.1093/nar/gkab447 ;PMID: 34086934

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  • Title:
    KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis
  • Author: Bu, Dechao ; Luo, Haitao ; Huo, Peipei ; Wang, Zhihao ; Zhang, Shan ; He, Zihao ; Wu, Yang ; Zhao, Lianhe ; Liu, Jingjia ; Guo, Jincheng ; Fang, Shuangsang ; Cao, Wanchen ; Yi, Lan ; Zhao, Yi ; Kong, Lei
  • Subjects: Web Server Issue
  • Is Part Of: Nucleic acids research, 2021-07, Vol.49 (W1), p.W317-W325
  • Description: Abstract Gene set enrichment (GSE) analysis plays an essential role in extracting biological insight from genome-scale experiments. ORA (overrepresentation analysis), FCS (functional class scoring), and PT (pathway topology) approaches are three generations of GSE methods along the timeline of development. Previous versions of KOBAS provided services based on just the ORA method. Here we presented version 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published earlier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and intelligently prioritizes the relevant biological pathways. In addition, KOBAS has expanded the downstream exploratory visualization for selecting and understanding the enriched results. The tool constructs a novel view of cirFunMap, which presents different enriched terms and their correlations in a landscape. Finally, based on the previous version's framework, KOBAS increased the number of supported species from 1327 to 5944. For an easier local run, it also provides a prebuilt Docker image that requires no installation, as a supplementary to the source code version. KOBAS can be freely accessed at http://kobas.cbi.pku.edu.cn, and a mirror site is available at http://bioinfo.org/kobas. Graphical Abstract Graphical Abstract Framework of KOBAS.
  • Publisher: Oxford University Press
  • Language: English
  • Identifier: ISSN: 0305-1048
    EISSN: 1362-4962
    DOI: 10.1093/nar/gkab447
    PMID: 34086934
  • Source: Oxford Journals Open Access Collection
    GFMER Free Medical Journals
    PubMed Central
    DOAJ Directory of Open Access Journals
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