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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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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. |
format | Online Article Text |
id | pubmed-8323418 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT koppadsaraswati cloudcomputingenabledbigmultiomicsdataanalytics AT bannappa cloudcomputingenabledbigmultiomicsdataanalytics AT gkoutosgeorgiosv cloudcomputingenabledbigmultiomicsdataanalytics AT acharjeeanimesh cloudcomputingenabledbigmultiomicsdataanalytics |