Information Theory for DNA / RNA Sequencing

Extraordinary advances in sequencing technology in the past decade have revolutionized biology and medicine. In this talk, we will survey some of our work on an information theoretic approach to some string reconstruction problems that arise from RNA sequencing and for DNA variant calling. Algorithms derived from this approach are provably near-optimal in the information theoretic sense, while simultaneously being computationally efficient. Interesting connections to sparse flow decomposition in networks and to iterative algorithms will be discussed. To highlight the benefits of this approach, we will also describe our development of new software package that achieves significant informational gains over existing software for RNA assembly. This software has applications in several areas of biology and medicine including cancer.

Bio: Sreeram Kannan is currently a faculty at University of Washington, Seattle. He was a postdoctoral scholar at University of California, Berkeley till recently, before which he received his Ph.D. in Electrical Engineering and M.S. in mathematics from the University of Illinois Urbana Champaign. He is a recipient of the Van Valkenburg research award from UIUC, 2013, a co-recipient of the Qualcomm Cognitive Radio Contest first prize, 2010, a recipient of Qualcomm CTO Roberto Padovani outstanding intern award, 2010, a recipient of the S.V.C. Aiya medal from the Indian Institute of Science, 2008, and a co-recipient of Intel India Student Research Contest first prize, 2006. His research interests include the applications of information theory and approximation algorithms to computational biology and wireless networks.