Alternative splicing is central for cellular processes and substantially increases transcriptome and proteome diversity. Aberrant splicing events often have pathological consequences and are associated with various diseases and cancer types. The emergence of next generation RNA sequencing (RNA-seq) provides an exciting new technology to analyse alternative splicing on a large scale. However, algorithms that enable the analysis of alternative splicing from short-read sequencing are not fully established yet and there are still no standard solutions available for a variety of data analysis tasks.
Now a team led by researchers at German Cancer Research Center (DKFZ) have developed a new method and software to predict genes that are differentially spliced between two different conditions using RNA-seq data. Their method employs geometric angles between the high dimensional vectors of exon read counts. With this, differential splicing can be detected even if the splicing events comprise of higher complexity and involve previously unknown splicing patterns. They applied the approach to two case studies including neuroblastoma tumour data with favourable and unfavourable clinical courses. They show the validity of our predictions as well as the applicability of our method in the context of patient clustering.
AVAILABILITY: SplicingCompass is licensed under the GNU GPL and freely available as a package in the statistical language R at http://www.ichip.de/software/SplicingCompass.html
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- Aschoff M, Hotz-Wagenblatt A, Glatting KH, Fischer M, Eils R, König R. SplicingCompass: differential splicing detection using RNA-Seq data. Bioinformatics. [Epub ahead of print]. [abstract]