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

Author:

Aramaki Takuya1,Blanc-Mathieu Romain1,Endo Hisashi1,Ohkubo Koichi12,Kanehisa Minoru1,Goto Susumu3,Ogata Hiroyuki1

Affiliation:

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

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.

Funder

JSPS/MEXT/KAKENHI

Collaborative Research Program of the Institute for Chemical Research, Kyoto University

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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