BioinformaticsBioinformaticsPeer-reviewed journal article
KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold
Takuya Aramaki, Romain Blanc‐MathieuiD, Hisashi EndoiD, Koichi Ohkubo, Minoru I. KanehisaiD, Susumu GotoiD, Hiroyuki OgataiD
Abstract
SummaryKofamKOALA 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.\n\nAvailabilityKofamKOALA, KofamScan, and KOfam are freely available from https://www.genome.jp/tools/kofamkoala/\n\nContactogata@kuicr.kyoto-u.ac.jp
- assay: protein sequence
- research focus: gene regulatory network inference
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
- Citations
- 2,054
- FWCI
- 42.04
- Field percentile
- top 1%
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