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  ARH-seq: identification of differential splicing in RNA-seq data

Rasche, A., Lienhard, M., Yaspo, M.-L., Lehrach, H., & Herwig, R. (2014). ARH-seq: identification of differential splicing in RNA-seq data. Nucleic Acids Research (London), 42(14): e110. doi:10.1093/nar/gku495.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0025-B5BA-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FA6E-C
Genre: Journal Article

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 Creators:
Rasche, Axel1, Author              
Lienhard, Matthias1, Author              
Yaspo, Marie-Laure2, Author              
Lehrach, Hans3, Author              
Herwig, Ralf1, Author              
Affiliations:
1Bioinformatics (Ralf Herwig), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479648              
2Human Chromosome 21 (Marie-Laure Yaspo), Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479652              
3Dept. of Vertebrate Genomics (Head: Hans Lehrach), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433550              

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Free keywords: Alternative Splicing Case-Control Studies Exons Gene Expression Profiling High-Throughput Nucleotide Sequencing/*methods Humans Sequence Analysis, RNA/*methods
 Abstract: The computational prediction of alternative splicing from high-throughput sequencing data is inherently difficult and necessitates robust statistical measures because the differential splicing signal is overlaid by influencing factors such as gene expression differences and simultaneous expression of multiple isoforms amongst others. In this work we describe ARH-seq, a discovery tool for differential splicing in case-control studies that is based on the information-theoretic concept of entropy. ARH-seq works on high-throughput sequencing data and is an extension of the ARH method that was originally developed for exon microarrays. We show that the method has inherent features, such as independence of transcript exon number and independence of differential expression, what makes it particularly suited for detecting alternative splicing events from sequencing data. In order to test and validate our workflow we challenged it with publicly available sequencing data derived from human tissues and conducted a comparison with eight alternative computational methods. In order to judge the performance of the different methods we constructed a benchmark data set of true positive splicing events across different tissues agglomerated from public databases and show that ARH-seq is an accurate, computationally fast and high-performing method for detecting differential splicing events.

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Language(s): eng - English
 Dates: 2014-06-112014-08
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1093/nar/gku495
ISSN: 1362-4962 (Electronic) 0305-1048 (Print)
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Title: Nucleic Acids Research (London)
  Other : Nucleic Acids Res
Source Genre: Journal
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Publ. Info: Oxford : Oxford University Press
Pages: - Volume / Issue: 42 (14) Sequence Number: e110 Start / End Page: - Identifier: ISSN: 0305-1048
CoNE: https://pure.mpg.de/cone/journals/resource/110992357379342