BRC investigators played an active part in the kickoff workshop for the Beyond Bioinformatics [http://www.samsi.info/programs/2014-15-program-beyond-bioinformatics-statistical-and-mathematical-challenges-bioinformatic]
program organized by the Statistical and Applied Mathematical Sciences Institute (SAMSI), and will be playing key roles in the program throughout the 2014-2015 academic year. SAMSI, located in the Research Triangle Park, is the only research center funded by the National Science Foundation to advance the discipline of statistics. The convergence of statistical and biological sciences, along with SAMSI activities, make this an especially intellectually rich time for bioinformatics at N.C. State. The year-long program “Beyond Bioinformatics: Statistical and Mathematical Challenges” includes working groups on a variety of topics, including statistical issues that arise in evolutionary inference and analysis of Big Data.
The “Dependence in Evolutionary Models” working group includes N.C. State bioinformaticians Xiang Ji, Chris Nasrallah, Jeremy Ash, and Jeff Thorne. Additional organizers include N.C. State mathematician Seth Sullivant, and Duke statistician Scott Schmidler. The working group has also brought in internationally acclaimed visitors, including Jotun Hein (Oxford University), David Pollock (University of Colorado Denver), Richard Goldstein (University College London), and Ziheng Yang (University College London). Professors Hein, Yang, Sullivant, Schmidler, and Thorne are teaching a related graduate course on statistical molecular evolution at the SAMSI facility in Fall 2014. The evolution working group is concentrating on two questions: 1. How can evolutionary inferences be made when changes at one position in a DNA sequence influence the rate of changes at other positions?; and 2. Which evolutionary scenarios can and cannot be disentangled by making inferences from DNA sequence data?
The “Multiple Hypothesis Testing and Simultaneous Inference” working group is organized by Yi-Hui Zhou and Fred Wright from the BRC, with graduate student fellow Ajay Kumar from the NCSU department of Statistics. The working group is inspired by the critical need to perform false positive control in the presence of large numbers of statistical tests. Related problems are posed by the desire to perform inference on effect sizes for numerous parameters, such as effects of SNPs on disease risk, etc. This working group will consider current work on multiple testing and simultaneous inference, considering complicating situations posed by new technologies or special sampling situations.