Koppad Saraswati, B Annappa, Gkoutos Georgios V, Acharjee Animesh
Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India.
Institute of Cancer and Genomic Sciences and Centre for Computational Biology, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
Bioinform Biol Insights. 2021 Jul 28;15:11779322211035921. doi: 10.1177/11779322211035921. eCollection 2021.
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.
高通量实验使研究人员能够通过对组学数据进行大规模分析来探索复杂的多因素疾病。此类高维数据集面临的挑战包括存储、分析和共享。计算技术和方法的最新创新，尤其是云计算方面的创新，为生物信息学领域提供了一种前景广阔、低成本且高度灵活的解决方案。云计算在分子建模、组学数据分析（如RNA测序、代谢组学或蛋白质组学数据集）以及表型数据的整合、分析和解释方面正迅速证明其越来越有用。我们回顾了先进的基于云的大数据技术在处理和分析组学数据方面的应用，并深入探讨了云生物信息学的最新应用。
