Publication | Open Access
KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold
208
Citations
7
References
2019
Year
Unknown Venue
Artificial IntelligenceProfile HmmMachine LearningEngineeringGeneticsBiometricsGenomicsGene RecognitionBioinformatics DatabaseSpeech RecognitionData ScienceData MiningPattern RecognitionAdaptive Score ThresholdHomology SearchProteomicsAbstract Summary KofamkoalaSequence AnalysisOmicsComputer ScienceBioinformaticsFunctional GenomicsWeb ServerProtein BioinformaticsBiologyGene Sequence AnnotationComputational BiologySystems BiologyMedicine
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 KofamKOALA, KofamScan, and KOfam are freely available from https://www.genome.jp/tools/kofamkoala/ Contact ogata@kuicr.kyoto-u.ac.jp
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