Detection of divergent genes in microbial aCGH experiments

<p>Abstract</p> <p>Background</p> <p>Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to...

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Main Authors: Aakra Ågot, Ziegler Andreas, Nyquist Ludvig, Repsilber Dirk, Snipen Lars, Aastveit Are
Format: Article
Language:English
Published: BMC 2006-03-01
Series:BMC Bioinformatics
Online Access:http://www.biomedcentral.com/1471-2105/7/181
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spelling doaj-e81824ae831e4e0ca477e0a5451bc4032020-11-24T21:51:49ZengBMCBMC Bioinformatics1471-21052006-03-017118110.1186/1471-2105-7-181Detection of divergent genes in microbial aCGH experimentsAakra ÅgotZiegler AndreasNyquist LudvigRepsilber DirkSnipen LarsAastveit Are<p>Abstract</p> <p>Background</p> <p>Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.</p> <p>Methods</p> <p>We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity.</p> <p>Results</p> <p>The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of <it>Staphylococcus aureus </it>and <it>Streptococcus pneumoniae</it>.</p> <p>Conclusion</p> <p>When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.</p> http://www.biomedcentral.com/1471-2105/7/181
collection DOAJ
language English
format Article
sources DOAJ
author Aakra Ågot
Ziegler Andreas
Nyquist Ludvig
Repsilber Dirk
Snipen Lars
Aastveit Are
spellingShingle Aakra Ågot
Ziegler Andreas
Nyquist Ludvig
Repsilber Dirk
Snipen Lars
Aastveit Are
Detection of divergent genes in microbial aCGH experiments
BMC Bioinformatics
author_facet Aakra Ågot
Ziegler Andreas
Nyquist Ludvig
Repsilber Dirk
Snipen Lars
Aastveit Are
author_sort Aakra Ågot
title Detection of divergent genes in microbial aCGH experiments
title_short Detection of divergent genes in microbial aCGH experiments
title_full Detection of divergent genes in microbial aCGH experiments
title_fullStr Detection of divergent genes in microbial aCGH experiments
title_full_unstemmed Detection of divergent genes in microbial aCGH experiments
title_sort detection of divergent genes in microbial acgh experiments
publisher BMC
series BMC Bioinformatics
issn 1471-2105
publishDate 2006-03-01
description <p>Abstract</p> <p>Background</p> <p>Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.</p> <p>Methods</p> <p>We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity.</p> <p>Results</p> <p>The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of <it>Staphylococcus aureus </it>and <it>Streptococcus pneumoniae</it>.</p> <p>Conclusion</p> <p>When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.</p>
url http://www.biomedcentral.com/1471-2105/7/181
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