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

Yang, Wentao; Rosenstiel, Philip; Schulenburg, Hinrich

doi:10.1186/s12864-019-5686-1

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

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Schulenburg, Hinrich
Max Planck Fellow Group Antibiotic Resistance Evolution, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Citation

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.

Cite as: https://hdl.handle.net/21.11116/0000-0004-4AE3-9

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.