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タイトル: KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold
著者: 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
著者名の別形: 遠藤, 寿
大久保, 宏一
緒方, 博之
発行日: 1-Apr-2020
出版者: Oxford University Press (OUP)
誌名: Bioinformatics
巻: 36
号: 7
開始ページ: 2251
終了ページ: 2252
抄録: 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.
著作権等: © 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(出版社版): 10.1093/bioinformatics/btz859
PubMed ID: 31742321
出現コレクション:学術雑誌掲載論文等

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