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  Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

Stegle, O., Drewe, P., Bohnert, R., Borgwardt, K., & Rätsch, G. (2010). Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts. Nature Precedings, 2010, 1-11. doi:10.1038/npre.2010.4437.1.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C02C-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-76C0-0
Genre: Journal Article

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Stegle, O1, 2, Author              
Drewe, P3, Author              
Bohnert, R3, Author              
Borgwardt, K1, 2, Author              
Rätsch, G3, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528696              
3Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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 Abstract: As a fruit of the current revolution in sequencing technology, transcriptomes can now be analyzed at an unprecedented level of detail. These advances have been exploited for detecting differential expressed genes across biological samples and for quantifying the abundances of various RNA transcripts within one gene. However, explicit strategies for detecting the hidden differential abundances of RNA transcripts in biological samples have not been defined. In this work, we present two novel statistical tests to address this issue: a "gene structure sensitive" Poisson test for detecting differential expression when the transcript structure of the gene is known, and a kernel-based test called Maximum Mean Discrepancy when it is unknown. We analyzed the proposed approaches on simulated read data for two artificial samples as well as on factual reads generated by the Illumina Genome Analyzer for two C. elegans samples. Our analysis shows that the Poisson test identifies genes with differential transcript expression considerably better that previously proposed RNA transcript quantification approaches for this task. The MMD test is able to detect a large fraction (75) of such differential cases without the knowledge of the annotated transcripts. It is therefore well-suited to analyze RNA-Seq experiments when the genome annotations are incomplete or not available, where other approaches have to fail.

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 Dates: 2010-05
 Publication Status: Published in print
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 Identifiers: DOI: 10.1038/npre.2010.4437.1
BibTex Citekey: 7053
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Title: Nature Precedings
Source Genre: Journal
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Pages: - Volume / Issue: 2010 Sequence Number: - Start / End Page: 1 - 11 Identifier: -