rQuant is a software of quantitative detection of alternative transcripts with RNA-Seq data.High-throughput sequencing technologies open exciting new approaches to transcriptome profiling. For the important task of inferring transcript abundances from RNA-Seq data, the author developed a new technique, called rQuant, based on quadratic programming. Our method estimates biases introduced by experimental settings and is thus a powerful tool to reveal and quantify novel (alternative) transcripts.
FusionSeq is a computational framework for detecting chimeric transcripts from paired-end RNA-seq experiments. It includes filters to remove spurious candidate fusions with artifacts, such as misalignment or random pairing of transcript fragments, and provides a ranked list of fusion-transcript candidates that can be further evaluated via experimental methods. FusionSeq also contains a module to identify exact sequences at breakpoint junctions.
The purpose of sSeq is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution.