@article {7, title = {Highly sensitive and ultrafast read mapping for RNA-seq analysis.}, journal = {DNA Res}, year = {2016}, month = {2016 Jan 5}, 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.

}, issn = {1756-1663}, doi = {10.1093/dnares/dsv039}, author = {Medina, I and T{\'a}rraga, J and Mart{\'\i}nez, H and Barrachina, S and Castillo, M I and Paschall, J and Salavert-Torres, J and Blanquer-Espert, I and Hern{\'a}ndez-Garc{\'\i}a, V and Quintana-Ort{\'\i}, E S and Dopazo, J} } @article {4, title = {Using GPUs for the Exact Alignment of Short-Read Genetic Sequences by Means of the Burrows-Wheeler Transform}, journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)}, volume = {9}, year = {2012}, month = {2012}, chapter = {1245}, abstract = {General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup larger than 12{\times}, when compared to CPU execution. This implementation exploits the parallelism by concurrently searching different sequences on the same reference search tree, maximizing memory locality and ensuring a symmetric access to the data. The paper describes the behavior of the algorithm in GPU, showing a good scalability in the performance, only limited by the size of the GPU inner memory.}, issn = {1545-5963}, doi = {10.1109/TCBB.2012.49}, url = {http://dl.acm.org/citation.cfm?id=2223945}, author = {Jose Salavert Torres and Ignacio Blanquer Espert and Andres Tomas Dominguez and Vicente Hernendez and Medina, Ignacio and Joaquin Terraga and Dopazo, Joaqu{\'\i}n} }