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

Takuya Aramaki; Romain Blanc-Mathieu; Hisashi Endo; Koichi Ohkubo; Minoru Kanehisa; Susumu Goto; Hiroyuki Ogata

doi:10.1093/bioinformatics/btz859

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

Bioinformatics. 2020 Apr 1;36(7):2251-2252. doi: 10.1093/bioinformatics/btz859.

Authors

Takuya Aramaki¹, Romain Blanc-Mathieu¹, Hisashi Endo¹, Koichi Ohkubo^{1

2}, Minoru Kanehisa¹, Susumu Goto³, Hiroyuki Ogata¹

Affiliations

¹ Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011.
² Hewlett-Packard Japan Ltd., Koto-ku, Tokyo 136-8711.
³ Database Center for Life Science, Research Organization of Information and Systems, Kashiwa, Chiba 277-0871, Japan.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Amino Acid Sequence
Computers*
Databases, Factual