Shark: fishing in a sample to discard useless RNA-Seq reads

Abstract

Recent advances in high throughput RNA-Seq technologies allow to produce massive datasets. When a study focuses only on a handful of genes, most reads are not relevant and degrade the performance of the tools used to analyze the data. Removing such useless reads from the input dataset leads to improved efficiency without compromising the results of the study.\n\nTo this aim, in this paper we introduce a novel computational problem, called gene assignment and we propose an efficient alignment-free approach to solve it. Given a RNA-Seq sample and a panel of genes, a gene assignment consists in extracting from the sample the reads that most probably were sequenced from those genes. The problem becomes more complicated when the sample exhibits evidence of novel alternative splicing events.\n\nWe implemented our approach in a tool called Shark and assessed its effectiveness in speeding up differential splicing analysis pipelines. This evaluation shows that Shark is able to significantly improve the performance of RNA-Seq analysis tools without having any impact on the final results.\n\nThe tool is distributed as a stand-alone module and the software is freely available at https://github.com/AlgoLab/shark.