:: DESCRIPTION
pairedBayes is an R code for Bayesian modeling of paired RNA-seq experiment.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
BMC Bioinformatics. 2013 Mar 27;14:110. doi: 10.1186/1471-2105-14-110.
Differential expression analysis for paired RNA-Seq data.
Chung LM1, Ferguson JP, Zheng W, Qian F, Bruno V, Montgomery RR, Zhao H.
:: DESCRIPTION
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.
::DEVELOPER
the Biomedical Informatics Lab
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Bohnert, R, Behr, J, and Rätsch, G (2009):
Transcript quantification with RNA-Seq data,
BMC Bioinformatics, 10(S13):P5.
:: DESCRIPTION
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.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Andrea Sboner, Lukas Habegger, Dorothee Pflueger, Stephane Terry, David Z. Chen, Joel S. Rozowsky, Ashutosh K. Tewari, Naoki Kitabayashi, Benjamin J. Moss, Mark S. Chee, Francesca Demichelis, Mark A. Rubin, Mark B. Gerstein
FusionSeq: a modular framework for finding gene fusions by analyzing Paired-End RNA-Sequencing data
Genome Biology 21 Oct. 2010; 11:R104
:: DESCRIPTION
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.
::DEVELOPER
Laboratory for Statistical Proteomics and Bioinformatics , Purdue University
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2013 May 15;29(10):1275-82. doi: 10.1093/bioinformatics/btt143. Epub 2013 Apr 14.
Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size.
Yu D, Huber W, Vitek O.
