Differential Expression Analysis in RNA-Seq by a Naive Bayes Classifier with Local Normalization.

ABSTRACT: To improve the applicability of RNA-seq technology, a large number of RNA-seq data analysis methods and correction algorithms have been developed. Although these new methods and algorithms have steadily improved transcriptome analysis, greater prediction accuracy is needed to better guide experimental designs with computational results. In this study, a new tool for the identification of differentially expressed genes with RNA-seq data, named GExposer, was developed. This tool introduces a local normalization algorithm to reduce the bias of nonrandomly positioned read depth. The naive Bayes classifier is employed to integrate fold change, transcript length, and GC content to identify differentially expressed genes. Results on several independent tests show that GExposer has better performa

SUBMITTER: Dou Y 

PROVIDER: S-EPMC4538581 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature