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      • Record: found
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      nhmmer: DNA homology search with profile HMMs

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      Author(s): * ,
      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary: Sequence database searches are an essential part of molecular biology, providing information about the function and evolutionary history of proteins, RNA molecules and DNA sequence elements. We present a tool for DNA/DNA sequence comparison that is built on the HMMER framework, which applies probabilistic inference methods based on hidden Markov models to the problem of homology search. This tool, called nhmmer, enables improved detection of remote DNA homologs, and has been used in combination with Dfam and RepeatMasker to improve annotation of transposable elements in the human genome.

          Availability: nhmmer is a part of the new HMMER3.1 release. Source code and documentation can be downloaded from http://hmmer.org. HMMER3.1 is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X.

          Contact: wheelert@ 123456janelia.hhmi.org

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          Most cited references13

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          Identification of common molecular subsequences.

          T.F. Smith, M.S. Waterman (1981)
          Article 0 comments Cited 1698 times – based on 0 reviews     Review now
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            Human-mouse alignments with BLASTZ.

            Scott Schwartz, W. Kent, Arian Smit … (2003)
            The Mouse Genome Analysis Consortium aligned the human and mouse genome sequences for a variety of purposes, using alignment programs that suited the various needs. For investigating issues regarding genome evolution, a particularly sensitive method was needed to permit alignment of a large proportion of the neutrally evolving regions. We selected a program called BLASTZ, an independent implementation of the Gapped BLAST algorithm specifically designed for aligning two long genomic sequences. BLASTZ was subsequently modified, both to attain efficiency adequate for aligning entire mammalian genomes and to increase its sensitivity. This work describes BLASTZ, its modifications, the hardware environment on which we run it, and several empirical studies to validate its results.
            Article 0 comments Cited 467 times – based on 0 reviews     Review now
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              A new generation of homology search tools based on probabilistic inference.

              Sean R. Eddy (2009)
              Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
              Article 0 comments Cited 465 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 1 October 2013
                Publication date (Electronic): 9 July 2013
                Publication date PMC-release: 9 July 2013
                Volume: 29
                Issue: 19
                Pages: 2487-2489
                Affiliations
                HHMI Janelia Farm Research Campus, Ashburn, VA 20147, USA
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Alfonso Valencia

                Article
                Publisher ID: btt403
                DOI: 10.1093/bioinformatics/btt403
                PMC ID: 3777106
                PubMed ID: 23842809
                SO-VID: 344d6809-a622-4d56-9e9c-9531f4d42a12
                Copyright © © The Author(s) 2013. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 17 June 2013
                Date revision received : 17 June 2013
                Date accepted : 5 July 2013
                Page count
                Pages: 3
                Categories
                Subject: Applications Notes
                Subject: Sequence Analysis

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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