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10.1093/bib/bbab349 http://scihub22266oqcxt.onion/10.1093/bib/bbab349 34505137!8499876!34505137     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=34505137&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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\34505137.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Brief+Bioinform 2022 ; 23 (1): ä Nephropedia Template TP {{tp|p=34505137|t=2022. Serverless computing in omics data analysis and integration |pdf=|usr=}}{{34505137}} gab.com Text Serverless computing in omics data analysis and integration JO: Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34505137 #FOAvip A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform's underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic. Twit Text FOAVip Serverless computing in omics data analysis and integration ????Brief Bioinform http://www.kidney.de/pget.php?&tw=1&pmid=34505137 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34505137 #FOAvip English Wikipedia <ref>{{Cite pmid|34505137}}</ref> Serverless computing in omics data analysis and integration #MMPMID34505137 Grzesik P; Augustyn DR; Wycislik L; Mrozek D Brief Bioinform 2022[Jan]; 23 (1): ä PMID34505137show ga A comprehensive analysis of omics data can require vast computational resources and access to varied data sources that must be integrated into complex, multi-step analysis pipelines. Execution of many such analyses can be accelerated by applying the cloud computing paradigm, which provides scalable resources for storing data of different types and parallelizing data analysis computations. Moreover, these resources can be reused for different multi-omics analysis scenarios. Traditionally, developers are required to manage a cloud platform's underlying infrastructure, configuration, maintenance and capacity planning. The serverless computing paradigm simplifies these operations by automatically allocating and maintaining both servers and virtual machines, as required for analysis tasks. This paradigm offers highly parallel execution and high scalability without manual management of the underlying infrastructure, freeing developers to focus on operational logic. This paper reviews serverless solutions in bioinformatics and evaluates their usage in omics data analysis and integration. We start by reviewing the application of the cloud computing model to a multi-omics data analysis and exposing some shortcomings of the early approaches. We then introduce the serverless computing paradigm and show its applicability for performing an integrative analysis of multiple omics data sources in the context of the COVID-19 pandemic. |*Cloud Computing[MESH] |*Computational Biology[MESH] |*Genomics[MESH] |*Pandemics[MESH] |*SARS-CoV-2/genetics/metabolism[MESH] |*Software[MESH] |COVID-19/epidemiology/*genetics/*metabolism[MESH]Serverless computing in omics data analysis and integration (epmc).pdf DeepDyve Pubget Overpricing