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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.

Abstract

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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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 34 PATTERNS FOR PARALLEL PROGRAMMING ON GPUS Edited by: F. Magoulès Chapter 11 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 ²Minds Planet, France doi:10.4203/csets.34.11 purchase the full-text of this chapter Full Bibliographic Reference for this chapter 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. Abstract 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. purchase the full-text of this chapter (price £20) go to the previous chapter go to the next chapter return to the table of contents return to the book description purchase this book (price £85 +P&P)
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