KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold

Abstract

Abstract Summary KofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction. Availability and implementation KofamKOALA, KofamScan and KOfam are freely available from GenomeNet (https://www.genome.jp/tools/kofamkoala/). Supplementary information Supplementary data are available at Bioinformatics online.

Keywords

KEGGAnnotationComputer scienceHidden Markov modelMarkov chainData miningComputational biologyArtificial intelligenceBiologyGeneMachine learningGene ontologyGenetics

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Publication Info

Year: 2019
Type: article
Volume: 36
Issue: 7
Pages: 2251-2252
Citations: 1690
Access: Closed

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APA Style

                            
                                
                                    Takuya Aramaki, 
                                
                                    Romain Blanc‐Mathieu, 
                                
                                    Hisashi Endo
                                
                                et al.
                            
                            (2019). 
                            KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. 
                            Bioinformatics
                            , 36
                            (7)
                            , 2251-2252.
                            https://doi.org/10.1093/bioinformatics/btz859
                        

Identifiers

DOI: 10.1093/bioinformatics/btz859