A team led by researchers at University of Kentucky have devloped a new method for the detection and visualization of differential transcription. The approach does not depend on transcript or gene annotations. It also circumvents the need for full transcript inference and quantification, which is a challenging problem because of short read lengths, as well as various sampling biases. Their approach starts with the identification of ASMs from the splice graph, constructed directly from the exons and introns predicted from RNA-seq read alignments. The abundance of alternative splicing isoforms residing in each ASM is estimated for each sample and is compared across sample groups. A non-parametric statistical test is applied to each ASM to detect significant differential transcription with a controlled false discovery rate. The sensitivity and specificity of the method have been assessed using simulated data sets and compared with other state-of-the-art approaches.
The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.
- Hu Y, Huang Y, Du Y, Orellana CF, Singh D, Johnson AR, Monroy A, Kuan PF, Hammond SM, Makowski L, Randell SH, Chiang DY, Hayes DN, Jones C, Liu Y, Prins JF, Liu J. (2012) DiffSplice: the genome-wide detection of differential splicing events with RNA-seq. Nucleic Acids Res [Epub ahead of print]. [article]