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Detection of divergent genes in microbial aCGH experiments
Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
Department of Biology and Biochemistry/Bioinformatics, University of Potsdam, Germany.ORCID iD: 0000-0002-7173-5579
Microbial Gene Technology, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
Institute of Medical Biometry and Statistics, University at Lübeck, Germany.
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2006 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, article id 181Article in journal (Refereed) Published
Abstract [en]

Background: 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.

Methods: 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.

Results: 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 Staphylococcus aureus and Streptococcus pneumoniae.

Conclusion: 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.

Place, publisher, year, edition, pages
London, UK: BioMed Central, 2006. Vol. 7, article id 181
National Category
Bioinformatics and Systems Biology
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URN: urn:nbn:se:oru:diva-40644DOI: 10.1186/1471-2105-7-181ISI: 000240415100001PubMedID: 16573812Scopus ID: 2-s2.0-33748623795OAI: oai:DiVA.org:oru-40644DiVA, id: diva2:778046
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2018-01-30Bibliographically approved

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