PT  - JOURNAL ARTICLE
AU  - Hirotaka Suetake
AU  - Masaaki Kotera
TI  - KPHMMER: Hidden Markov Model generator for detecting KEGG PATHWAY-specific genes
AID  - 10.1101/636290
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 636290
4099  - http://biorxiv.org/content/early/2019/05/14/636290.short
4100  - http://biorxiv.org/content/early/2019/05/14/636290.full
AB  - Motivation Reinforcement of HMMER search for secondary metabolism-specific Pfam domains should contribute to discover novel biosynthetic machinery of clinically important natural products.Results Here we provide a Python-based command line tool, named as KPHMMER, to extract the Pfam domains that are specific in the user-defined set of pathways in the user-defined set of organisms registered in the KEGG database. KPHMMER outperformed the previous study in detecting secondary metabolism-specific Pfam domain set. Furthermore, it was proven that KPHMMER helps reduce the computational cost compared with the case using the whole Pfam-A HMM file. We believe that KPHMMER is a powerful tool enabling to deal with many other genome-sequenced species for more general purpose.Availability KPHMMER is implemented as a Python package freely available via the package management system “pip” and also at https://github.com/suecharo/KPHMMERContact maskot{at}chemsys.t.u-tokyo.ac.jp