title: HIPPI: highly accurate protein family classification with ensembles of HMMs.
abstract: 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.
  
  
  
  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.
  
  
  
  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 .
authors.affiliation: Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA.
authors.forename: Nam-Phuong
authors.initials: NP
authors.lastname: Nguyen
authors.affiliation: Department of Statistics, University of Illinois at Urbana-Champaign, 725 South Wright Street, Urbana, 61820, IL, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Nute
authors.affiliation: Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, 92093, CA, USA.
authors.forename: Siavash
authors.initials: S
authors.lastname: Mirarab
authors.affiliation: Department of Computer Science, University of Illinois at Urbana-Champaign, 201 North Goodwin Ave, IL, Urbana, 61801, IL, USA. warnow@illinois.edu.|Department of Bioengineering, University of Illinois at Urbana-Champaign, 1270 Digital Computer Laboratory, Urbana, 61801, IL, USA. warnow@illinois.edu.|Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, 1206 West Gregory Drive, Urbana, 61801, IL, USA. warnow@illinois.edu.|National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, 1205 West Clark Street, Urbana, 61801, IL, USA. warnow@illinois.edu.
authors.forename: Tandy
authors.initials: T
authors.lastname: Warnow
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D020407:Internet; D008390:Markov Chains; D011506:Proteins / Q000737:chemistry / Q000145:classification* / Q000378:metabolism; D016415:Sequence Alignment; D014584:User-Computer Interface
chemical_list: D011506:Proteins
keywords: Ensemble of profile hidden Markov models; Pfam; Protein family identification