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

Bioinformatics, 2020-04, Vol.36 (7), p.2251-2252 [Peer Reviewed Journal]

The Author(s) 2019. Published by Oxford University Press. 2019 ;The Author(s) 2019. Published by Oxford University Press. ;ISSN: 1367-4803 ;EISSN: 1460-2059 ;EISSN: 1367-4811 ;DOI: 10.1093/bioinformatics/btz859 ;PMID: 31742321

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  • Title:
    KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold
  • Author: Aramaki, Takuya ; Blanc-Mathieu, Romain ; Endo, Hisashi ; Ohkubo, Koichi ; Kanehisa, Minoru ; Goto, Susumu ; Ogata, Hiroyuki
  • Valencia, Alfonso
  • Subjects: Applications Notes
  • Is Part Of: Bioinformatics, 2020-04, Vol.36 (7), p.2251-2252
  • Description: 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.
  • Publisher: England: Oxford University Press
  • Language: English
  • Identifier: ISSN: 1367-4803
    EISSN: 1460-2059
    EISSN: 1367-4811
    DOI: 10.1093/bioinformatics/btz859
    PMID: 31742321
  • Source: Oxford Journals Open Access Collection
    Journals@Ovid Open Access Journal Collection Rolling
    Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
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