PT - JOURNAL ARTICLE AU - Gael P. Alamancos AU - Amadís Pagès AU - Juan L. Trincado AU - Nicolás Bellora AU - Eduardo Eyras TI - Leveraging transcript quantification for fast computation of alternative splicing profiles AID - 10.1101/008763 DP - 2015 Jan 01 TA - bioRxiv PG - 008763 4099 - http://biorxiv.org/content/early/2015/01/20/008763.short 4100 - http://biorxiv.org/content/early/2015/01/20/008763.full AB - Background 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 datasets represents a major challenge in terms of computation time and storage requirements.Results Here we describe SUPPA, a computational tool to calculate relative inclusion values (PSI or Ψ) of alternative splicing events, exploiting fast transcript quantification of a given annotation. SUPPA is more accurate than standard methods using simulated as well as real RNA sequencing data compared to experimentally validated events. We 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 better than existing methods. Finally, we also show that SUPPA is more than 1000 times faster than standard methodsConclusions SUPPA efficiently uses transcript quantification to obtain accurate estimates of local alternative splicing event Ψ values. Coupled with a fast transcript quantification method, SUPPA provides Ψ values at a much higher speed than existing methods without compromising accuracy. SUPPA facilitates the systematic splicing analysis of large datasets with limited computational resources. The software is implemented in Python 2.7 and is available under the MIT license at https://bitbucket.org/regulatorygenomicsupf/suppa.