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

The RNA transcriptome varies in response to cellular differentiation as well as environmental factors, and can be characterized by the diversity and abundance of transcript isoforms. Differential transcription analysis, the detection of differences between the transcriptomes of different cells, may improve understanding of cell differentiation and development and enable the identification of biomarkers that classify disease types. The availability of high-throughput short-read RNA sequencing technologies provides in-depth sampling of the transcriptome, making it possible to accurately detect the differences between transcriptomes. In this article, we present a new method for the detection and visualization of differential transcription. Our 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. Instead, our method takes a divide-and-conquer approach to localize the difference between transcriptomes in the form of alternative splicing modules (ASMs), where transcript isoforms diverge. Our 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. Experimental validation using qRT-PCR confirmed a selected set of genes that are differentially expressed in a lung differentiation study and a breast cancer data set, demonstrating the utility of the approach applied on experimental biological data sets. The software of DiffSplice is available at http://www.netlab.uky.edu/p/bioinfo/DiffSplice.

A suite of molecular markers for identifying species, detecting introgression and describing population structure in spadefoot toads (Spea spp.).

Two congeneric species of spadefoot toad, Spea multiplicata and Spea bombifrons, have been the focus of hybridization studies since the 1970s. Because complex hybrids are not readily distinguished phenotypically, genetic markers are needed to identify introgressed individuals. We therefore developed a set of molecular markers (amplified fragment length polymorphism, polymerase chain reaction-restriction fragment length polymorphism and single nucleotide polymorphism) for identifying pure-species, F1 hybrids and more complex introgressed types. To do so, we tested a series of markers across both species and known hybrids using populations in both allopatry and sympatry. We retained those markers that differentiated the two pure-species and also consistently identified known species hybrids. These markers are well suited for identifying hybrids between these species. Moreover, those markers that show variation within each species can be used in conjunction with existing molecular markers in studies of population structure and gene flow.