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  aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data

Yang, W., Rosenstiel, P., & Schulenburg, H. (2019). aFold – using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data. BMC Genomics, 20: 364. doi:10.1186/s12864-019-5686-1.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-4AE3-9 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-4AE4-8
Genre: Journal Article

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 Creators:
Yang, Wentao, Author
Rosenstiel, Philip, Author
Schulenburg, Hinrich1, Author              
Affiliations:
1Max Planck Fellow Group Antibiotic Resistance Evolution, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_2600692              

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 Abstract: Data normalization and identification of significant differential expression represent crucial steps in RNA-Seq analysis. Many available tools rely on assumptions that are often not met by real data, including the common assumption of symmetrical distribution of up- and down-regulated genes, the presence of only few differentially expressed genes and/or few outliers. Moreover, the cut-off for selecting significantly differentially expressed genes for further downstream analysis often depend on arbitrary choices.

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Language(s): eng - English
 Dates: 2018-11-282019-04-012019-05-102019
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1186/s12864-019-5686-1
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Title: BMC Genomics
Source Genre: Journal
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Publ. Info: BioMed Central
Pages: - Volume / Issue: 20 Sequence Number: 364 Start / End Page: - Identifier: ISSN: 1471-2164
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905010