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

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 co...

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Bibliographic Details
Published in:	Bioinformatics Vol. 36; no. 7; pp. 2251 - 2252
Main Authors:	Aramaki, Takuya, Blanc-Mathieu, Romain, Endo, Hisashi, Ohkubo, Koichi, Kanehisa, Minoru, Goto, Susumu, Ogata, Hiroyuki
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 01.04.2020
Subjects:	Applications Notes
ISSN:	1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1367-4803 1367-4811 1460-2059 1367-4811
DOI:	10.1093/bioinformatics/btz859