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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Published inBioinformatics 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
LanguageEnglish
Published England Oxford University Press 01.04.2020
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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.
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ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btz859