Modeling and cleaning RNA-seq data significantly improve detection of differentially expressed genes.

ABSTRACT:

Background

RNA-seq has become a standard technology to quantify mRNA. The measured values usually vary by several orders of magnitude, and while the detection of differences at high values is statistically well grounded, the significance of the differences for rare mRNAs can be weakened by the presence of biological and technical noise.

Results

We have developed a method for cleaning RNA-seq data, which improves the detection of differentially expressed genes and specifically genes with low to moderate transcription. Using a data modeling approach, parameters of randomly distributed mRNA counts are identified and reads, most probably originating from technical noise, are removed. We demonstrate that the removal of this random component leads to the significant increase in th

SUBMITTER: Deyneko IV 

PROVIDER: S-EPMC9670425 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature