BMI 209

Statistical Methods in Bioinformatics: Case Studies

This course is intended to expose students to approaches to formulating and tackling important data analytic problems that arise in the context of contemporary, high-throughput technologies.  These include DNA microarrays, ChIP-chip studies, SNP arrays, whole-genome sequence, and proteomic data. It is expected that students will acquire the ability to frame statistical hypotheses in such settings and be able to identify corresponding data analytic techniques. While such techniques will be introduced here via case studies, they pertain to more broadly encountered research questions and study domains. Examples (and the settings where they arise) include data preprocessing (expression, tiling and SNP arrays; mass spectrometry), multiple hypothesis testing (evolution, CpG island methylation), sequence analysis (motif finding), clustering (SNP arrays), and classification methods (CpG island methylation, copy number data).

Target audience: BMI, PQB, BMS students; Interested auditors