CSE529/599b: Computational Genomics

Instructor: Sara Mostafavi, PhD.

Teaching Assistant: Ayse Dincer.

Time and place: Monday / Wednesday 11:30-12:50pm, CSE2 (Gates Build) 271 --> note classroom change.

Note: Classes will be in-person starting from Jan 31st.

Exception(s): Lecture on Feb 14 will be remote (Canvas has zoom link)

Office hours: Wednesdays 10-11am and Fridays 1-2pm

Discussion Board: Canvas

Description: Computational genomics is a new emerging field that brings together recent advances in computational methods, including machine learning, and genomic measurement technologies, to provide insights into how our genomes work and underlie health and disease.

Differences in our genomes underlies differences in our susceptibility to various diseases, including psychiatric disorders, heart disease, and immune responses to infection. Recent advances in measurement technologies now enable scientists to measure genomic data across individuals at multiple granularity, including the genome, epigenome and transcriptome, resulting in millions of measurements per individual. These data promise to enable prediction of disease risk and understand its molecular causes. However, these data are complex, heterogeneous, confounded, and noisy, thus posing significant challenges to our ability to extract meaningful patterns and predictions that can provide biological insights about cellular systems and disease. Formulating meaningful computational problems, and understanding how inferences go wrong and lead to misguided conclusions, not only requires statistical and computational insights, but also a scientific lense to experimental design and data interpretation.

This course will introduce computational and statistical approaches and practices for deriving robust and rigorous insights from modern genomics datasets. Lectures alternate between genomics-inspired problem formulation and foundational statistical and computational approaches for addressing them. In foundational lectures, we will cover basics of statistical inference, hidden confounding factors, causality and causal inference, deep neural networks and interpretation approaches to deep learning models. From the genomics side, we will cover the latest research problems in human genetics, regulatory genomics, multi-omic data, and single cell genomics.

See Schedule page for weekly topics

See Syllabus page for the following information:

• • Topics

  • Learning objectives

  • Course format

  • Pre-requisites

  • Student evaluation

  • Suggested background reading