Une vue protéomique des tumeurs endocrines

Abstract

As sequencing technologies progress, the amount of data produced grows exponentially, shifting the bottleneck of discovery towards the data analysis phase. In particular, currently available mapping solutions for RNA-seq leave room for improvement in terms of sensitivity and performance, hindering an efficient analysis of transcriptomes by massive sequencing. Here, we present an innovative approach that combines re-engineering, optimization and parallelization. This solution results in a significant increase of mapping sensitivity over a wide range of read lengths and substantial shorter runtimes when compared with current RNA-seq mapping methods available.This work is supported by grants from the Spanish Ministry of Economy and Competitiveness (BIO2014-57291-R) and co-funded with European Regional Development Funds (ERDF), AECID (D/016099/08) and from the Conselleria d’Educacio of the Valencian Community (PROMETEOII/2014/025). This work has been carried out in the context of the HPC4G initiative (http://www.hpc4g.org) and the Bull-CIPF Chair for Computational Genomics. Funding to pay the Open Access publication charges for this article was provided by grant BIO2014-57291-R from the Spanish Ministry of Economy and Competitiveness (MINECO), co-funded with European Regional Development Funds (ERDF)