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KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold.
Aramaki, Takuya; Blanc-Mathieu, Romain; Endo, Hisashi; Ohkubo, Koichi; Kanehisa, Minoru; Goto, Susumu; Ogata, Hiroyuki.
Afiliação
  • Aramaki T; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011.
  • Blanc-Mathieu R; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011.
  • Endo H; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011.
  • Ohkubo K; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011.
  • Kanehisa M; Hewlett-Packard Japan Ltd., Koto-ku, Tokyo 136-8711.
  • Goto S; Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011.
  • Ogata H; Database Center for Life Science, Research Organization of Information and Systems, Kashiwa, Chiba 277-0871, Japan.
Bioinformatics ; 36(7): 2251-2252, 2020 04 01.
Article em En | MEDLINE | ID: mdl-31742321
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Computadores Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Computadores Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article