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

By Takuya Aramaki, Romain Blanc-Mathieu, Hisashi Endo, Koichi Ohkubo, Minoru Kanehisa, Susumu Goto, Hiroyuki Ogata. bioRxiv, 2019

1,777 citations recorded by the source corpus.

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

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fbbf97b7c1eb1b3f84d406b1267767e0da77b8be

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Yoko Sato
2. Minoru Kanehisa
3. Karthik Anantharaman
4. Anton I. Korobeynikov
5. Alla L. Lapidus
6. Robert D. Finn

In Molecular Biology

1. Yoko Sato
2. Minoru Kanehisa
3. Simon C. Potter
4. Robert D. Finn
5. Karthik Anantharaman
6. Kristopher Kieft
7. Zhichao Zhou
8. Alla L. Lapidus

Citations in the corpus

• KEGG Mapper for inferring cellular functions from protein sequences (2019, methodology): “In contrast to these sequence similarity based tools, KofamKOALA searches the HMM profile database named KOfam and uses its bit scores with a precomputed adaptive score threshold for each KO group.(8) The nonredundant pangenome datasets are updated weekly, while the KOfam database is updated every 1”
• VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences (2020, methodology): “VIBRANT uses HMM profiles from three different databases: Kyoto Encyclopedia of Genes and Genomes (KEGG) KoFam (March 2019 release) [54, 55], Pfam (v32) [40], and Virus Orthologous Groups (VOG) (release 94, vogdb.org).”
• MGnify: the microbiome analysis resource in 2020 (2019, methodology): “…functional analyses are also provided in the form of KEGG orthologue (KO) (23) annotations, which are calculated using HMMER (40) and the KOfam library (using a slightly modified form of the profile hidden Markov model (HMM) database of KEGG ortho-logues (41)) on the predicted protein sequences.”