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

2021/05/09 by Dechao Bu, Haitao Luo, Peipei Huo +12 · 13 citations
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Gene expression and cancer classification #Machine Learning in Bioinformatics

paper · pdf · doi:10.1093/nar/gkab447

openalex publication_date 2021/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

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.

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