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Bioinformatics. 2018 Mar 15;34(6):1066-1068. doi: 10.1093/bioinformatics/btx690.

DIEGO: detection of differential alternative splicing using Aitchison's geometry.

Author information

Transcriptome Bioinformatics Group, Interdisciplinary Center for Bioinformatics, Leipzig University, 04107 Leipzig.
Chair of Bioinformatics, Faculty of Mathematics and Computer Science, Leipzig University, 04107 Leipzig, Germany.
ecSeq Bioinformatics, 04103 Leipzig, Germany.
Institute of Human Genetics, University of Ulm and University of Ulm Medical Center, 89081 Ulm, Germany.
Computational Biology Group, Leibniz Institute on Ageing - Fritz Lipmann Institute (FLI) and Friedrich-Schiller-University Jena, 07745 Jena, Germany.



Alternative splicing is a biological process of fundamental importance in most eukaryotes. It plays a pivotal role in cell differentiation and gene regulation and has been associated with a number of different diseases. The widespread availability of RNA-Sequencing capacities allows an ever closer investigation of differentially expressed isoforms. However, most tools for differential alternative splicing (DAS) analysis do not take split reads, i.e. the most direct evidence for a splice event, into account. Here, we present DIEGO, a compositional data analysis method able to detect DAS between two sets of RNA-Seq samples based on split reads.


The python tool DIEGO works without isoform annotations and is fast enough to analyze large experiments while being robust and accurate. We provide python and perl parsers for common formats.

Availability and implementation:

The software is available at:


Supplementary information:

Supplementary data are available at Bioinformatics online.

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