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Big data analytics framework for real-time genome analysis: A comprehensive approach

Publication Type : Journal Article

Publisher : Journal of Computational and Theoretical Nanoscience

Source : Journal of Computational and Theoretical Nanoscience. 16. 3419-3427. 10.1166/jctn.2019.8302.

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Campus : Chennai

School : School of Engineering

Department : Computer Science and Engineering

Year : 2019

Abstract : Big Data Technologies are well-accepted in the recent years in bio-medical and genome informatics. They are capable to process gigantic and heterogeneous genome information with good precision and recall. With the quick advancements in computation and storage technologies, the cost of acquiring and processing the genomic data has decreased significantly. The upcoming sequencing platforms will produce vast amount of data, which will imperatively require high-performance systems for on-demand analysis with time-bound efficiency. Recent bio-informatics tools are capable of utilizing the novel features of Hadoop in a more flexible way. In particular, big data technologies such as MapReduce and Hive are able to provide high-speed computational environment for the analysis of petabyte scale datasets. This has attracted the focus of bio-scientists to use the big data applications to automate the entire genome analysis. The proposed framework is designed over MapReduce and Java on extended Hadoop platform to achieve the parallelism of Big Data Analysis. It will assist the bioinformatics community by providing a comprehensive solution for Descriptive, Comparative, Exploratory, Inferential, Predictive and Causal Analysis on Genome data. The proposed framework is user-friendly, fully-customizable, scalable and fit for comprehensive real-time genome analysis from data acquisition till predictive sequence analysis.

Cite this Research Publication : Shandilya, Shishir K & Sehar, Sountharrajan & Shandilya, Smita & Suganya, E.. (2019). Big Data Analytics Framework for Real-Time Genome Analysis: A Comprehensive Approach. Journal of Computational and Theoretical Nanoscience. 16. 3419-3427. 10.1166/jctn.2019.8302.

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