scholarly journals Next-generation sequencing revolution through big data analytics

2016 ◽  
Vol 9 (2) ◽  
pp. 119-149 ◽  
Author(s):  
Rashmi Tripathi ◽  
Pawan Sharma ◽  
Pavan Chakraborty ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Big Data ◽  
Next Generation Sequencing ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Using “Big Data” in the Cost-Effectiveness Analysis of Next-Generation Sequencing Technologies: Challenges and Potential Solutions

2018 ◽  
Vol 21 (9) ◽  
pp. 1048-1053 ◽  
Author(s):  
Sarah Wordsworth ◽  
Brett Doble ◽  
Katherine Payne ◽  
James Buchanan ◽  
Deborah A. Marshall ◽  
...  
Keyword(s):  
Big Data ◽  
Cost Effectiveness ◽  
Next Generation Sequencing ◽  
Cost Effectiveness Analysis ◽  
Next Generation ◽  
Sequencing Technologies ◽  
Effectiveness Analysis ◽  
The Cost ◽  
Generation Sequencing

scholarly journals Big data from small samples: Informatics of next-generation sequencing in cytopathology

2016 ◽  
Vol 125 (4) ◽  
pp. 236-244 ◽  
Author(s):  
Sinchita Roy-Chowdhuri ◽  
Somak Roy ◽  
Sara E. Monaco ◽  
Mark J. Routbort ◽  
Liron Pantanowitz
Keyword(s):  
Big Data ◽  
Next Generation Sequencing ◽  
Small Samples ◽  
Next Generation ◽  
Generation Sequencing

Big Data Analysis Techniques for Visualization of Genomics in Medicinal Plants

Biotechnology ◽  
2019 ◽  
pp. 804-837
Author(s):  
Hithesh Kumar ◽  
Vivek Chandramohan ◽  
Smrithy M. Simon ◽  
Rahul Yadav ◽  
Shashi Kumar
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Next Generation Sequencing ◽  
Medicinal Plants ◽  
Big Data Analysis ◽  
Next Generation ◽  
Genome Data ◽  
Huge Data ◽  
Transcriptomics Data ◽  
Generation Sequencing

In this chapter, the complete overview and application of Big Data analysis in the field of health care industries, Clinical Informatics, Personalized Medicine and Bioinformatics is provided. The major tools and databases used for the Big Data analysis are discussed in this chapter. The development of sequencing machines has led to the fast and effective ways of generating DNA, RNA, Whole Genome data, Transcriptomics data, etc. available in our hands in just a matter of hours. The complete Next Generation Sequencing (NGS) huge data analysis work flow for the medicinal plants are discussed in the chapter. This chapter serves as an introduction to the big data analysis in Next Generation Sequencing and concludes with a summary of the topics of the remaining chapters of this book.

scholarly journals Visual programming for next-generation sequencing data analytics

2016 ◽  
Vol 9 (1) ◽  
Author(s):  
Franco Milicchio ◽  
Rebecca Rose ◽  
Jiang Bian ◽  
Jae Min ◽  
Mattia Prosperi
Keyword(s):  
Next Generation Sequencing ◽  
Data Analytics ◽  
Visual Programming ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

Data Mining, Big Data, Data Analytics

Web Services ◽  
2019 ◽  
pp. 89-104
Author(s):  
Priya P. Panigrahi ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Smart Devices ◽  
Data Life Cycle ◽  
The Impact ◽  
Special Relevance ◽  
Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

Data Mining, Big Data, Data Analytics

2017 ◽  
pp. 91-111
Author(s):  
Priya P. Panigrahi ◽  
Tiratha Raj Singh
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Biological Data ◽  
Smart Devices ◽  
Data Life Cycle ◽  
The Impact ◽  
Special Relevance ◽  
Generation Sequencing

In this digital and computing world, data formation and collection rate are growing very rapidly. With these improved proficiencies of data storage and fast computation along with the real-time distribution of data through the internet, the usual everyday ingestion of data is mounting exponentially. With the continuous advancement in data storage and accessibility of smart devices, the impact of big data will continue to develop. This chapter provides the fundamental concepts of big data, its benefits, probable pitfalls, big data analytics and its impact in Bioinformatics. With the generation of the deluge of biological data through next generation sequencing projects, there is a need to handle this data trough big data techniques. The chapter also presents a discussion of the tools for analytics, development of a novel data life cycle on big data, details of the problems and challenges connected with big data with special relevance to bioinformatics.

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