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B M I 877

STATISTICAL METHODS FOR MOLECULAR BIOLOGY

3 credits.

Statistical and computational methods in statistical genomics for human and experimental populations. Review methods for quality control, experimental design, clustering, network analysis, and other downstream analysis of next-generation sequencing studies along with methods for genome wide association studies.

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2024 Learning Outcomes: 1. Understand the statistical and computational background underlying many state-of-the-art techniques for the pre-processing and analysis of high-throughput genomics datasets Audience: Graduate 2. Identify the appropriateness and limitations of such methods in a variety of settings. Audience: Graduate 3. Discuss scientific problems and identify the statistical and computational aspects embedded in the processing and analysis of genomics datasets. Audience: Graduate 4. Become proficient in select software packages commonly used in analysis of next-generation sequencing data. Audience: Graduate Learning Outcomes: 1. Understand the statistical and computational background underlying many state-of-the-art techniques for the pre-processing and analysis of high-throughput genomics datasets Audience: Graduate 2. Identify the appropriateness and limitations of such methods in a variety of settings. Audience: Graduate 3. Discuss scientific problems and identify the statistical and computational aspects embedded in the processing and analysis of genomics datasets. Audience: Graduate 4. Become proficient in select software packages commonly used in analysis of next-generation sequencing data. Audience: Graduate

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