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PATTERNS FOR PARALLEL PROGRAMMING ON GPUS
Edited by: F. Magoulès
Bioinformatics of Non-Coding RNAs and GPUs, A Case Study: Prediction at Large Scale of MicroRNAs in Genomes
F. Tahi1, V.D. Tran1, S. Tempel1 and E. Mahé2
1IBISC-IBGBI, University of Evry-Val d'Essonne/Genopole, France
F. Tahi, V.D. Tran, S. Tempel, E. Mahé, "Bioinformatics of Non-Coding RNAs and GPUs, A Case Study: Prediction at Large Scale of MicroRNAs in Genomes", in F. Magoulès, (Editor), "Patterns for Parallel Programming on GPUs", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 11, pp 249-279, 2014. doi:10.4203/csets.34.11
Keywords: genomics, bioinformatics, non-coding RNA, microRNA, RNA structure, ncRNA prediction, GPU, CPU.
Non-coding RNAs are functional RNAs that are not translated into proteins. Computational studies of non-coding RNAs have recently become an important challenge in bioinformatics, including their identification and structure prediction. We introduced in our EvryRNA platform several algorithms for those purposes: P-DCFold, SSCA, Tfold, miRNAFold, ncRNAclassifier and BoostSVM.
With the development of next-generation sequencing technologies, huge amounts of genomic and RNA sequences data have been produced. A parallelization of such tools is thus required to overcome the long execution time issue. GPU computing has appeared as an effective way for realizing parallel tasks. miRNAFold, an algorithm we developed for the search for microRNAs in genomic sequences, and initially written in C, was implemented in CUDA, which allows the use of GPUs, to search at large scale for microRNAs in genomic sequences.
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