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Title: KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold
Authors: Aramaki, Takuya
Blanc-Mathieu, Romain
Endo, Hisashi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0003-0016-1624 (unconfirmed)
Ohkubo, Koichi
Kanehisa, Minoru
Goto, Susumu
Ogata, Hiroyuki
Author's alias: 遠藤, 寿
大久保, 宏一
緒方, 博之
Issue Date: 1-Apr-2020
Publisher: Oxford University Press (OUP)
Journal title: Bioinformatics
Volume: 36
Issue: 7
Start page: 2251
End page: 2252
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.
Rights: © The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
URI: http://hdl.handle.net/2433/250987
DOI(Published Version): 10.1093/bioinformatics/btz859
PubMed ID: 31742321
Appears in Collections:Journal Articles

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