Tutorials on statistical challenges for genome resequencing data
This tutorial session precedes a research workshop on genome resequencing starting in the afternoon at the Isaac Newton Institute. The tutorial session is self-contained and open to all, including individuals not registered for the research workshop. The aim of the tutorial session is to provide an introduction to resequencing data, some experimental techniques that exploit new sequencing technologies, and statistical and computational issues in interpreting the data. It is intended principally for those new to the field at the PhD or early RA level, some familiarity with genetics and genomics will be helpful. No advance registration is required and there is no charge to attend.
Please note that the Tutorial Sessions are seperate from the main Workshop and will take place at The Centre for Mathematical Sciences (CMS), adjacent to the Isaac Newton Institute.
Cheap, fast, sequencing platforms now allow near-complete genome sequences to be quickly and affordably obtained from individual members of any species. The DNA sequences of individual humans, their pathogens and model organisms will have an enormous impact on population genetics and evolutionary theory, as well as on epidemiology, particularly our understanding of infectious disease.
The motivation for the workshop is to bring together leading mathematical and biological researchers in an interdisciplinary environment to discuss the mathematical, statistical and computational challenges that lie ahead. We plan to discuss the most pressing open problems and the most promising avenues of future research necessary to deliver the full benefits of genome resequencing.
The workshop will consist of invited talks and contributed posters, as well as some social activities and opportunities for informal discussions. We plan to organise an introductory tutorial session on the Tuesday monrning, details to be confirmed. The topics for the invited talks will cover basecalling, sequence assembly, and mapping; imputation and inference of relatedness; cancer genomics and methylation; CNV/indels, RNA-seq, ChIP-seq and pathogens.
The mathematical techniques involved will be wide-ranging, including statistical and machine-learning techniques for high-dimensional classification and regression, as well as techniques from signal processing and various mathematical models of population genetics and evolutionary processes.