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10.2147/AABC.S64482 http://scihub22266oqcxt.onion/10.2147/AABC.S64482 C4655901!4655901 !26604801     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26604801 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Adv+Appl+Bioinform+Chem 2015 ; 8 (ä): 23-35 Nephropedia Template TP {{tp|p=26604801 |t=2015. Parallel computing in genomic research: advances and applications |pdf=|usr=}}{{26604801 }} gab.com Text Parallel computing in genomic research: advances and applications JO: Adv Appl Bioinform Chem http://www.kidney.de/pget.php?&tw=1&pmid=26604801 #FOAvip Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities. Twit Text FOAVip Parallel computing in genomic research: advances and applications ????Adv Appl Bioinform Chem http://www.kidney.de/pget.php?&tw=1&pmid=26604801 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26604801 #FOAvip English Wikipedia <ref>{{Cite pmid|26604801 }}</ref> Parallel computing in genomic research: advances and applications #MMPMID26604801 Ocaña K ; de Oliveira D Adv Appl Bioinform Chem 2015[]; 8 (ä): 23-35 PMID26604801 show ga Today's genomic experiments have to process the so-called "biological big data" that is now reaching the size of Terabytes and Petabytes. To process this huge amount of data, scientists may require weeks or months if they use their own workstations. Parallelism techniques and high-performance computing (HPC) environments can be applied for reducing the total processing time and to ease the management, treatment, and analyses of this data. However, running bioinformatics experiments in HPC environments such as clouds, grids, clusters, and graphics processing unit requires the expertise from scientists to integrate computational, biological, and mathematical techniques and technologies. Several solutions have already been proposed to allow scientists for processing their genomic experiments using HPC capabilities and parallelism techniques. This article brings a systematic review of literature that surveys the most recently published research involving genomics and parallel computing. Our objective is to gather the main characteristics, benefits, and challenges that can be considered by scientists when running their genomic experiments to benefit from parallelism techniques and HPC capabilities. äParallel computing in genomic research: advances and applications (epmc).pdf DeepDyve Pubget Overpricing