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Full Stream Name: Big Data in Biology 

Research Educators: Dhivya Arasappan

Principal Investigators: Hans Hofmann & Vishy Iyer

Credit Options: Spring & Fall

How can we detect meaningful patterns from terabytes of sequence data?

Advances in Next Generation Sequencing (NGS) technologies allow us to generate data at unprecedented speed and throughput. As a consequence, we can now study biological systems at the level of whole genomes and whole transcriptomes instead of at the single gene level. Importantly, this technology not only impacts research, but also how medical care is provided; hospitals will soon be generating sequence data for every patient who walks in the door in an effort to customize diagnosis and treatment to that patient. However, the biggest challenge for utilizing the power of such data is our limited ability to quickly and reliably obtain insights from this data.

In the Big Data in Biology Stream, we will explore methods for analyzing large-scale NGS datasets using computational algorithms, statistical tools, and supercomputers.  The skills required for analysis of large-scale sequence data can be applied to answer many different biological questions.  We will apply these skills on an array of research projects, including parsing through brain transcriptomics datasets to identify genes and pathways linked to addiction and assembling and annotating plant genomes to identify pathways of potential medicinal interest. For more information, please visit the stream's open house during the times specified on the FRI Open House Hours page. Some programming experience is preferred, but not required.

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Biology, Computer Science, Math