Highly sensitive and ultrafast read mapping for RNA-seq analysis

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). Medina, I.; Tárraga, J.; Martínez, H.; Barrachina, S.; Castillo, MI.; Paschall, J.; Salavert Torres, J... (2016). Highly sensitive and ultrafast read mapping for RNA-seq analysis. DNA Research. 1(1):1-8. https://doi.org/10.1093/dnares/dsv039 Garber, M., Grabherr, M. G., Guttman, M., & Trapnell, C. (2011). Computational methods for transcriptome annotation and quantification using RNA-seq. Nature Methods, 8(6), 469-477. doi:10.1038/nmeth.1613 Marioni, J. C., Mason, C. E., Mane, S. M., Stephens, M., & Gilad, Y. (2008). RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Research, 18(9), 1509-1517. doi:10.1101/gr.079558.108 Li, H., & Homer, N. (2010). A survey of sequence alignment algorithms for next-generation sequencing. Briefings in Bioinformatics, 11(5), 473-483. doi:10.1093/bib/bbq015 Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10(3), R25. doi:10.1186/gb-2009-10-3-r25 Li, R., Yu, C., Li, Y., Lam, T.-W., Yiu, S.-M., Kristiansen, K., & Wang, J. (2009). SOAP2: an improved ultrafast tool for sho