Literature DB >> 12230032

HMM-based databases in InterPro.

Alex Bateman1, Daniel H Haft.   

Abstract

Protein family databases are an important resource for protein annotation and understanding protein evolution and function. In recent years hidden Markov models (HMMs) have become one of the key technologies used for detection of members of these families. This paper reviews the Pfam, TIGRFAMs and SMART databases that use the profile-HMMs provided by the HMMER package.

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Year:  2002        PMID： 12230032     DOI： 10.1093/bib/3.3.236

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN： 1467-5463            Impact factor:   11.622


  11 in total

1.  The TIGRFAMs database of protein families.

Authors:  Daniel H Haft; Jeremy D Selengut; Owen White
Journal:  Nucleic Acids Res       Date:  2003-01-01       Impact factor: 16.971

2.  AtBAG7, an Arabidopsis Bcl-2-associated athanogene, resides in the endoplasmic reticulum and is involved in the unfolded protein response.

Authors:  Brett Williams; Mehdi Kabbage; Robert Britt; Martin B Dickman
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-15       Impact factor: 11.205

3.  Vestigial-like-2b (VITO-1b) and Tead-3a (Tef-5a) expression in zebrafish skeletal muscle, brain and notochord.

Authors:  Christopher J Mann; Daniel P S Osborn; Simon M Hughes
Journal:  Gene Expr Patterns       Date:  2007-08-15       Impact factor: 1.224

Review 4.  Cataloging the relationships between proteins: a review of interaction databases.

Authors:  Carol Rohl; Yancey Price; Tiffany B Fischer; Melissa Paczkowski; Michael F Zettel; Jerry Tsai
Journal:  Mol Biotechnol       Date:  2006-09       Impact factor: 2.860

5.  Analysis of and function predictions for previously conserved hypothetical or putative proteins in Blochmannia floridanus.

Authors:  Peter Gaudermann; Ina Vogl; Evelyn Zientz; Francisco J Silva; Andres Moya; Roy Gross; Thomas Dandekar
Journal:  BMC Microbiol       Date:  2006-01-09       Impact factor: 3.605

Review 6.  T-cell epitope vaccine design by immunoinformatics.

Authors:  Atanas Patronov; Irini Doytchinova
Journal:  Open Biol       Date:  2013-01-08       Impact factor: 6.411

7.  Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.

Authors:  Hashem A Shihab; Julian Gough; David N Cooper; Peter D Stenson; Gary L A Barker; Keith J Edwards; Ian N M Day; Tom R Gaunt
Journal:  Hum Mutat       Date:  2012-11-02       Impact factor: 4.878

8.  Using structural motif descriptors for sequence-based binding site prediction.

Authors:  Andreas Henschel; Christof Winter; Wan Kyu Kim; Michael Schroeder
Journal:  BMC Bioinformatics       Date:  2007-05-22       Impact factor: 3.169

9.  The SeqFEATURE library of 3D functional site models: comparison to existing methods and applications to protein function annotation.

Authors:  Shirley Wu; Mike P Liang; Russ B Altman
Journal:  Genome Biol       Date:  2008-01-16       Impact factor: 13.583

10.  De novo transcriptomic analysis of hydrogen production in the green alga Chlamydomonas moewusii through RNA-Seq.

Authors:  Shihui Yang; Michael T Guarnieri; Sharon Smolinski; Maria Ghirardi; Philip T Pienkos
Journal:  Biotechnol Biofuels       Date:  2013-08-23       Impact factor: 6.040
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