RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.

ABSTRACT:

Background

Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectability for certain differentially expressed genes (DEGs). Whether the transcriptional dynamics of a gene can be captured accurately depends on experimental design/operation and the following data analysis processes. The workflow of subsequent data processing, such as reads alignment, transcript quantification, normalization, and statistical methods for ultimate identification of DEGs can influence the accuracy and sensitivity of DEGs analysis, producing a certain number of false-positivity or false-negativity. Machine learning (ML) is a mu

SUBMITTER: Wang L 

PROVIDER: S-EPMC6053725 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature