Using Phylogeny to Improve Genome-Wide Distant Homology Recognition

The gap between the number of known protein sequences and structures continues to widen, particularly as a result of sequencing projects for entire genomes. Recently there have been many attempts to generate structural assignments to all genes on sets of completed genomes using fold-recognition meth...

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Main Authors: Abeln, Sanne, Teubner, Carlo, Deane, Charlotte M
Format: Online
Language:English
Published: Public Library of Science 2007
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1779300/
id pubmed-1779300
recordtype oai_dc
spelling pubmed-17793002007-01-27 Using Phylogeny to Improve Genome-Wide Distant Homology Recognition Abeln, Sanne Teubner, Carlo Deane, Charlotte M Research Article The gap between the number of known protein sequences and structures continues to widen, particularly as a result of sequencing projects for entire genomes. Recently there have been many attempts to generate structural assignments to all genes on sets of completed genomes using fold-recognition methods. We developed a method that detects false positives made by these genome-wide structural assignment experiments by identifying isolated occurrences. The method was tested using two sets of assignments, generated by SUPERFAMILY and PSI-BLAST, on 150 completed genomes. A phylogeny of these genomes was built and a parsimony algorithm was used to identify isolated occurrences by detecting occurrences that cause a gain at leaf level. Isolated occurrences tend to have high e-values, and in both sets of assignments, a sudden increase in isolated occurrences is observed for e-values >10−8 for SUPERFAMILY and >10−4 for PSI-BLAST. Conditions to predict false positives are based on these results. Independent tests confirm that the predicted false positives are indeed more likely to be incorrectly assigned. Evaluation of the predicted false positives also showed that the accuracy of profile-based fold-recognition methods might depend on secondary structure content and sequence length. We show that false positives generated by fold-recognition methods can be identified by considering structural occurrence patterns on completed genomes; occurrences that are isolated within the phylogeny tend to be less reliable. The method provides a new independent way to examine the quality of fold assignments and may be used to improve the output of any genome-wide fold assignment method. Public Library of Science 2007-01 2007-01-19 /pmc/articles/PMC1779300/ /pubmed/17238281 http://dx.doi.org/10.1371/journal.pcbi.0030003 Text en © 2007 Abeln et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Abeln, Sanne
Teubner, Carlo
Deane, Charlotte M
spellingShingle Abeln, Sanne
Teubner, Carlo
Deane, Charlotte M
Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
author_facet Abeln, Sanne
Teubner, Carlo
Deane, Charlotte M
author_sort Abeln, Sanne
title Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
title_short Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
title_full Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
title_fullStr Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
title_full_unstemmed Using Phylogeny to Improve Genome-Wide Distant Homology Recognition
title_sort using phylogeny to improve genome-wide distant homology recognition
description The gap between the number of known protein sequences and structures continues to widen, particularly as a result of sequencing projects for entire genomes. Recently there have been many attempts to generate structural assignments to all genes on sets of completed genomes using fold-recognition methods. We developed a method that detects false positives made by these genome-wide structural assignment experiments by identifying isolated occurrences. The method was tested using two sets of assignments, generated by SUPERFAMILY and PSI-BLAST, on 150 completed genomes. A phylogeny of these genomes was built and a parsimony algorithm was used to identify isolated occurrences by detecting occurrences that cause a gain at leaf level. Isolated occurrences tend to have high e-values, and in both sets of assignments, a sudden increase in isolated occurrences is observed for e-values >10−8 for SUPERFAMILY and >10−4 for PSI-BLAST. Conditions to predict false positives are based on these results. Independent tests confirm that the predicted false positives are indeed more likely to be incorrectly assigned. Evaluation of the predicted false positives also showed that the accuracy of profile-based fold-recognition methods might depend on secondary structure content and sequence length. We show that false positives generated by fold-recognition methods can be identified by considering structural occurrence patterns on completed genomes; occurrences that are isolated within the phylogeny tend to be less reliable. The method provides a new independent way to examine the quality of fold assignments and may be used to improve the output of any genome-wide fold assignment method.
publisher Public Library of Science
publishDate 2007
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1779300/
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