SpliceGrapherXT

Predicting the structure of genes from RNA-Seq data remains a significant challenge in bioinformatics. Although the amount of data available for analysis is growing at an accelerating rate, the capability to leverage these data to construct complete gene models remains elusive. In addition, the tools that predict novel transcripts exhibit poor accuracy. We present a novel approach to predicting splice graphs from RNA-Seq data that uses patterns of acceptor and donor sites to recognize when novel exons can be predicted unequivocally. This simple approach achieves much higher precision and higher recall than methods like Cufflinks or IsoLasso when predicting novel exons from real and simulated data. The ambiguities that arise from RNA-Seq data can preclude making decisive predictions, so we use a realignment procedure that can predict additional novel exons while maintaining high precision. We show that these accurate splice graph predictions provide a suitable basis for making accurate transcript predictions using tools such as IsoLasso and PSGInfer. Using both real and simulated data, we show that this integrated method predicts transcripts with higher recall and precision than using these other tools alone, and in comparison to Cufflinks. SpliceGrapherXT is available from the SpliceGrapher web page at http://SpliceGrapher.sf.net.