KPHMMER: Hidden Markov Model generator for detecting KEGG PATHWAY-specific genes.

Suetake, H.; Kotera, M.

doi:10.1101/636290

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bioRxiv · 10.1101/636290

KPHMMER: Hidden Markov Model generator for detecting KEGG PATHWAY-specific genes.

Suetake, H.·Kotera, M.

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Abstract

MotivationReinforcement of HMMER search for secondary metabolism-specific Pfam domains should contribute to discover novel biosynthetic machinery of clinically important natural products.\n\nResultsHere 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.\n\nAvailabilityKPHMMER is implemented as a Python package freely available via the package management system \"pip\" and also at https://github.com/suecharo/KPHMMER\n\nContactmaskot@chemsys.t.u-tokyo.ac.jp

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Suetake, H., Kotera, M.. 2019-05-14. KPHMMER: Hidden Markov Model generator for detecting KEGG PATHWAY-specific genes.. https://doi.org/10.1101/636290

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