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.
DiffSplice is for the detection and visualization of differential transcription. DiffSplice circumvents the need for full-length transcript inference and quantification and localizes its search at alternative splicing modules (ASMs). These modules represent the genomic regions, where alternative transcripts diverge, localizing the nature of the difference and decreasing the complexity of the differential analysis by comparing corresponding ASMs between samples. The ASMs are detected automatically from a transcriptome-wide expression-weighted splice graph (ESG), which is built directly from read alignments and captures all the sample-relevant splicing events including novel ones. Expression estimation of associated isoforms and tests for differential transcription start from the simplest ASMs, which yield estimation that is more robust to sequencing bias, and work outward. A non-parametric statistical test is introduced to assign the significance level of the differential transcription in the ASMs with a controlled false discovery rate (FDR). By design, differential analysis on ASM can be performed using short reads.
You can download the DiffSplice release package here. Both binary files and source codes can be found in the release package.
DiffSplice: the genome-wide detection of differential splicing events with RNA-seq, Nucl. Acids Res. doi:10.1093/nar/gks1026
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