AFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.

ABSTRACT:

Background

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.

Results

We here introduce a new tool for estimating differential expression in noisy real-life data. It employs a novel normalization procedure (qtotal), which takes account of the overall distribution of read counts for data standardization enhancing reli

SUBMITTER: Yang W 

PROVIDER: S-EPMC6509820 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature