skip to primary navigationskip to content
 

Next Generation Sequencing: Dealing with big data

last modified Jun 18, 2015 12:17 PM
Nitin Sharma, Centre for Molecular Informatics, University of Cambridge

Next Generation Sequencing: Dealing with big data

Nitin Sharma, Postdoctoral Research Associate Centre for Molecular Informatics University of Cambridge

Abstract

The human genome project was driven by the motivation that understanding genome could provide clues and guide formulation of preventive and diagnostic strategies. The successful completion of human genome project highlighted the need for faster, cost effective yet accurate ways to sequence genome, which resulted in development of next generation sequencing technology (NGS). During the last decade NGS has revolutionised the field of genomics by reducing the time and cost of sequencing without compromising the accuracy of the results. However, the generation of large amount of data results in the big data problem. Big data is one part of the problem but the key challenge lies in processing, quality control and analysis in a way that can be understood, reproduced and reused by others, which impedes the implementation of NGS data into clinical practice.

NGS has not only been applied in understanding static genome but also to study DNA-protein interactions (ChIP-seq) and has been extensively used to measure gene expression known as RNA-seq. RNA-seq provides a snapshot of the active genome at a particular time and delivers precise measurement of transcripts and their isoform levels. In this presentation, utility and limitations associated with NGS using RNA-seq technology such as complexity of transcriptome data, experimental design, validation and analysis approaches will be highlighted. In addition, the application of RNA-seq technology in clinical diagnostics and therapeutics will be discussed.