DiffSplice: The genome-wide detection of differential splicing events with RNA-seq

摘要: The RNA transcriptome varies in response to cellular differentiation as well environmental factors, and can be characterized by the diversity abundance of transcript isoforms. Differential transcription analysis, detection differences between transcriptomes different cells, may improve understanding cell development enable identification biomarkers that classify disease types. availability high-throughput short-read sequencing technologies provides in-depth sampling transcriptome, making it possible accurately detect transcriptomes. In this article, we present a new method for visualization differential transcription. Our approach does not depend on or gene annotations. It also circumvents need full inference quantification, which is challenging problem because short read lengths, various biases. Instead, our takes divide-and-conquer localize difference form alternative splicing modules (ASMs), where isoforms diverge. starts with ASMs from splice graph, constructed directly exons introns predicted RNA-seq alignments. residing each ASM estimated sample compared across groups. A non-parametric statistical test applied significant controlled false discovery rate. sensitivity specificity have been assessed using simulated data sets other state-of-the-art approaches. Experimental validation qRT-PCR confirmed selected set genes are differentially expressed lung study breast cancer set, demonstrating utility experimental biological sets. software DiffSplice available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.