HIPPI: Highly accurate protein family classification with ensembles of HMMs

Nam phuong Nguyen, Michael Nute, Siavash Mirarab, Tandy Warnow

Research output: Contribution to journalArticle

Abstract

Background: Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. Results: We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. Conclusion: HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp.

Original languageEnglish (US)
Article number765
JournalBMC genomics
Volume17
DOIs
StatePublished - Nov 11 2016

Keywords

  • Ensemble of profile hidden Markov models
  • Pfam
  • Protein family identification

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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  • Datasets

    HIPPI Dataset

    Nguyen, N. (Creator), Nute, M. (Creator), Mirarab, S. (Creator) & Warnow, T. (Creator), University of Illinois at Urbana-Champaign, Aug 16 2016

    Dataset

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