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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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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