SpliceSeq: a resource for analysis and visualization of RNA-Seq data on alternative splicing and its functional impacts

Abstract

Summary: SpliceSeq is a resource for RNA-Seq data that provides a clear view of alternative splicing and identifies potential functional changes that result from splice variation. It displays intuitive visualizations and prioritized lists of results that highlight splicing events and their biological consequences. SpliceSeq unambiguously aligns reads to gene splice graphs, facilitating accurate analysis of large, complex transcript variants that cannot be adequately represented in other formats.Availability and implementation: SpliceSeq is freely available at http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview. The application is a Java program that can be launched via a browser or installed locally. Local installation requires MySQL and Bowtie.Contact: mryan@insilico.us.comSupplementary Information: Supplementary data are available at Bioinformatics online.

Alternative splicing of estrogen receptor alpha in hepatocellular carcinoma

“…The alternative splicing of ERa was generated from RNA-seq data which were acquired from TCGA data portal by TCGA SpliceSeq database [12]. Based on the coverage of different splicing isoforms, each alternative splicing event was assigned with a PSI (Percent Spliced In) value ranging from 0 to 1.…”

Aberrant RNA splicing in cancer; expression changes and driver mutations of splicing factor genes

“…[44][45][46][47][48][50][51][52]112,131, performed to compare individual differential splicing events among cancer types (these data were downloaded from http:// projects.insilico.us.com/TCGASpliceSeq/ and include splicing events detected by analyzing samples sequenced by TCGA with the SpliceSeq algorithm. 79 Further details of the analyses are included as Supplementary Methods). As expected, leukemia is clearly separated from the solid cancer types in this analysis, indicating a distinct splicing pattern in this hematologic cancer type (Supplementary Figure 1B).…”

How are Bayesian and Non-Parametric Methods Doing a Great Job in RNA-Seq Differential Expression Analysis? : A Review

“…In addition, the sample size in RNA-seq is much fewer than the number of variables (genes) (Anders et al, 2012;Beretta et al, 2014;Bernard et al, 2014;Bi and Davuluri, 2013;Bullard et al, 2010;Deng et al, 2011;Glaus et al, 2012;Han and Jiang, 2014;Hiller et al, 2009;Hiller and Wong, 2013;Howard and Heber, 2010;Hu et al, 2014;Jiang and Wong, 2009;Kaur et al, 2012;Kim et al, 2012;Kimes et al, 2014;Kumar et al, 2012;Lee et al, 2011;Leon-Novelo et al, 2014;Lerch et al, 2012;Li et al, 2014;Li et al, 2011;Li and Jiang, 2012;Ma and Zhang, 2013;Marioni et al, 2008;Mezlini et al, 2013;Mills et al, 2013;Mortazavi et al, 2008;Nariai et al, 2013;Nariai et al, 2014;Nicolae et al, 2011;Niu et al, 2014;Oh et al, 2013;Oshlack et al, 2010;Pandey et al, 2013;Patro et al, 2014;Pollier et al, 2013;Rehrauer et al, 2013;Roberts et al, 2011;Robinson and Oshlack, 2010;Ryan et al, 2012;Safikhani et al, 2013;<...>…”

“…And the calculation of optimal number of intra-replicates and inter-samples with power of detection by controlling FDR has been performed prior to differential expression analysis to derive more reliable statistical testing of comparison. In order to resolve these current issues in RNA-seq methods, Bayesian and non-parametric methods have been proposed and popularly done thus far by demonstrating equivalent at least or better performance in statistical tests compared to existing methods that are on the basis of classical parametric assumptions (Bi and Davuluri, 2013;Hardcastle and Kelly, 2010;Hu et al, 2014;Lee et al, 2011;León-Novelo et al, 2014;Nariai et al, 2013;Nariai et al, 2014;Oh et al, 2013;Ryan et al, 2012;Shen et al, 2012;Shen et al, 2014;Tarazona et al, 2011).…”