Literature DB >> 14962937

COACH: profile-profile alignment of protein families using hidden Markov models.

Robert C Edgar1, Kimmen Sjölander.   

Abstract

MOTIVATION: Alignments of two multiple-sequence alignments, or statistical models of such alignments (profiles), have important applications in computational biology. The increased amount of information in a profile versus a single sequence can lead to more accurate alignments and more sensitive homolog detection in database searches. Several profile-profile alignment methods have been proposed and have been shown to improve sensitivity and alignment quality compared with sequence-sequence methods (such as BLAST) and profile-sequence methods (e.g. PSI-BLAST). Here we present a new approach to profile-profile alignment we call Comparison of Alignments by Constructing Hidden Markov Models (HMMs) (COACH). COACH aligns two multiple sequence alignments by constructing a profile HMM from one alignment and aligning the other to that HMM.
RESULTS: We compare the alignment accuracy of COACH with two recently published methods: Yona and Levitt's prof_sim and Sadreyev and Grishin's COMPASS. On two sets of reference alignments selected from the FSSP database, we find that COACH is able, on average, to produce alignments giving the best coverage or the fewest errors, depending on the chosen parameter settings. AVAILABILITY: COACH is freely available from www.drive5.com/lobster

Mesh:

Year:  2004        PMID: 14962937     DOI: 10.1093/bioinformatics/bth091

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  30 in total

1.  MUSCLE: multiple sequence alignment with high accuracy and high throughput.

Authors:  Robert C Edgar
Journal:  Nucleic Acids Res       Date:  2004-03-19       Impact factor: 16.971

Review 2.  The limits of protein sequence comparison?

Authors:  William R Pearson; Michael L Sierk
Journal:  Curr Opin Struct Biol       Date:  2005-06       Impact factor: 6.809

3.  Functional annotation prediction: all for one and one for all.

Authors:  Ori Sasson; Noam Kaplan; Michal Linial
Journal:  Protein Sci       Date:  2006-05-02       Impact factor: 6.725

4.  SCOOP: a simple method for identification of novel protein superfamily relationships.

Authors:  Alex Bateman; Robert D Finn
Journal:  Bioinformatics       Date:  2007-02-03       Impact factor: 6.937

5.  Improving protein fold recognition by random forest.

Authors:  Taeho Jo; Jianlin Cheng
Journal:  BMC Bioinformatics       Date:  2014-10-21       Impact factor: 3.169

6.  Immunomics of the koala (Phascolarctos cinereus).

Authors:  Kendra C Abts; Jamie A Ivy; J Andrew DeWoody
Journal:  Immunogenetics       Date:  2015-03-13       Impact factor: 2.846

7.  Detection of distant evolutionary relationships between protein families using theory of sequence profile-profile comparison.

Authors:  Mindaugas Margelevicius; Ceslovas Venclovas
Journal:  BMC Bioinformatics       Date:  2010-02-17       Impact factor: 3.169

8.  Hidden Markov Models and their Applications in Biological Sequence Analysis.

Authors:  Byung-Jun Yoon
Journal:  Curr Genomics       Date:  2009-09       Impact factor: 2.236

9.  Highly sensitive detection of individual HEAT and ARM repeats with HHpred and COACH.

Authors:  Fred Kippert; Dietlind L Gerloff
Journal:  PLoS One       Date:  2009-09-24       Impact factor: 3.240

10.  Refining homology models by combining replica-exchange molecular dynamics and statistical potentials.

Authors:  Jiang Zhu; Hao Fan; Xavier Periole; Barry Honig; Alan E Mark
Journal:  Proteins       Date:  2008-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.