Report: KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis
Host: Han Yangshuo
Date: 26 Jul 2021
Gene set enrichment (GSE) analysis plays an essen- tial role in extracting biological insight from genome- scale experiments. ORA (overrepresentation analy- sis), FCS (functional class scoring), and PT (path- way topology) approaches are three generations of GSE methods along the timeline of development. Pre- vious versions of KOBAS provided services based on just the ORA method. Here we presented ver- sion 3.0 of KOBAS, which is named KOBAS-i (short for KOBAS intelligent version). It introduced a novel machine learning-based method we published ear- lier, CGPS, which incorporates seven FCS tools and two PT tools into a single ensemble score and in- telligently prioritizes the relevant biological path- ways. In addition, KOBAS has expanded the down- stream exploratory visualization for selecting and understanding the enriched results. The tool con- structs 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 sup- ported species from 1327 to 5944. For an easier lo- cal 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.