ESclassifier: A random forest classifier for detection of exon skipping events from RNA-Seq data

Abstract

Detecting exon skipping (ES) events is an essential part in genome-wide alternative splicing event detection. In this paper, we propose a novel method ESclassifier to detect ES events from RNA-seq data. ESclassifier conducts thorough studies on predicting features and figures out proper features according to their relevance for ES event detection. Experimental results on real human heart and liver RNA-seq data show that ESclassifier could effectively filter out false positives with high predictive accuracy. The codes of ESclassifier are available at http://mlg.hit.edu.cn/ybai/ES/ESclass.html.