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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Vydáno v:Bioinformatics Ročník 36; číslo 7; s. 2251 - 2252
Hlavní autoři: Aramaki, Takuya, Blanc-Mathieu, Romain, Endo, Hisashi, Ohkubo, Koichi, Kanehisa, Minoru, Goto, Susumu, Ogata, Hiroyuki
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 01.04.2020
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ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btz859