Leveraging transcript quantification for fast computation of alternative splicing profiles

Alternative splicing plays an essential role in many cellular processes and bears major relevance in the understanding of multiple diseases, including cancer. High-throughput RNA sequencing allows genome-wide analyses of splicing across multiple conditions. However, the increasing number of available data sets represents a major challenge in terms of computation time and storage requirements.

Researchers at Universitat Pompeu Fabra have developed SUPPA, a computational tool to calculate relative inclusion values of alternative splicing events, exploiting fast transcript quantification. SUPPA accuracy is comparable and sometimes superior to standard methods using simulated as well as real RNA-sequencing data compared with experimentally validated events. The researchers assess the variability in terms of the choice of annotation and provide evidence that using complete transcripts rather than more transcripts per gene provides better estimates. Moreover, SUPPA coupled with de novo transcript reconstruction methods does not achieve accuracies as high as using quantification of known transcripts, but remains comparable to existing methods. Finally, they show that SUPPA is more than 1000 times faster than standard methods. Coupled with fast transcript quantification, SUPPA provides inclusion values at a much higher speed than existing methods without compromising accuracy, thereby facilitating the systematic splicing analysis of large data sets with limited computational resources.


SUPPA pipeline. (A) SUPPA calculates possible alternative splicing events with the operation generateEvents from an annotation, which can be obtained from a database or built from RNA-seq data using a transcript reconstruction method. For each event, the transcripts contributing to either form of the event are stored and the calculation of the Ψ value per sample for each event is performed using the transcript abundances per sample (TPMs) (Materials and Methods). From one or more transcript quantification files, which can be obtained from any transcript quantification method, SUPPA calculates for each event the Ψ value per sample with the operation psiPerEvent. (B) Events generated from the annotation are given a unique identifier that includes a code for the event type (SE, MX, A5, A3, RI, AF, AL) and a set of start (s) and end (e) coordinates that define the event (shown in the figure) (Materials and Methods). In the figure, the form of the alternative splicing event that includes the region in black is the one for which the relative inclusion level (Ψ) is given: For SE, the PSI indicates the inclusion of the middle exon; for A5/A3, the form that minimizes the intron length; for MX, the form that contains the alternative exon with the smallest start coordinate (the left-most exon) regardless of strand; for RI, the form that retains the intron; and for AF/AL, the form that maximizes the intron length. The gray area indicates the alternative form of the event.

Availability – The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa

Alamancos GP, Pagès A, Trincado JL, Bellora N, Eyras E. (2015) Leveraging transcript quantification for fast computation of alternative splicing profiles. RNA [Epub ahead of print]. [article]

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