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Cloud Computing Enabled Big Multi-Omics Data Analytics

High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in...

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Autores principales: Koppad, Saraswati, B, Annappa, Gkoutos, Georgios V, Acharjee, Animesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323418/
https://www.ncbi.nlm.nih.gov/pubmed/34376975
http://dx.doi.org/10.1177/11779322211035921
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author Koppad, Saraswati
B, Annappa
Gkoutos, Georgios V
Acharjee, Animesh
author_facet Koppad, Saraswati
B, Annappa
Gkoutos, Georgios V
Acharjee, Animesh
author_sort Koppad, Saraswati
collection PubMed
description High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications.
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spelling pubmed-83234182021-08-09 Cloud Computing Enabled Big Multi-Omics Data Analytics Koppad, Saraswati B, Annappa Gkoutos, Georgios V Acharjee, Animesh Bioinform Biol Insights Review High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications. SAGE Publications 2021-07-28 /pmc/articles/PMC8323418/ /pubmed/34376975 http://dx.doi.org/10.1177/11779322211035921 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Review
Koppad, Saraswati
B, Annappa
Gkoutos, Georgios V
Acharjee, Animesh
Cloud Computing Enabled Big Multi-Omics Data Analytics
title Cloud Computing Enabled Big Multi-Omics Data Analytics
title_full Cloud Computing Enabled Big Multi-Omics Data Analytics
title_fullStr Cloud Computing Enabled Big Multi-Omics Data Analytics
title_full_unstemmed Cloud Computing Enabled Big Multi-Omics Data Analytics
title_short Cloud Computing Enabled Big Multi-Omics Data Analytics
title_sort cloud computing enabled big multi-omics data analytics
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323418/
https://www.ncbi.nlm.nih.gov/pubmed/34376975
http://dx.doi.org/10.1177/11779322211035921
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