CSE427: Computational Biology

Week 1: Intro and review

• Lecture 1 (link) - Intro to Computational Biology

• Lecture 2 (link) - Review: ML and statistical inference

• Optional: 

  • Short videos describing central dogma of biology here and here

  • Intro to Mol Biology for Computer Scientists (Hunter) here

  • Introduction to ML, chapter 1 of Bishop here

Week 2: Human genetics and GWAS I

• Lecture 3 (link) - Human Genetics intro

• Lecture 4 (link) - Genome Wide Association Studies (GWAS)

  • Optional:

    • Maximum likelihood and derivation of logistic regression here

    • GWAS review paper here

    • Videos by StatQuest: logistic regression here, and FDR here. 

Week 3: Statistical inference for GWAS II

• Lecture 5 (link) - Linear association testing and multiple testing correction

• Lecture 6 (link) - Confounding and statistical adjusment

  • Optional:

    • Population structure in genetics review article here 

    • Accounting for population structure using LMMs here

    • PCA videos: 20min video from StatQuest

Week 4: Statistical inference for GWAS III

• Lecture 7 (link) - Linear interaction models 

• Lecture 8 (link) - Review of genetics and GWAS

  • Optional:

    • Videos on statistical power here and power analysis here. 

    • Interaction effect and statistical testing here. More advanced reading here. 

    • Article on challenges and opportunities in g-by-e research here. 

Week 5: Multi-omic association studies

• Lecture 9 (link) - GWAS mechanisms and eQTL studies

• Lecture 10 (link) - Regularized Regression and TWAS

  • Optional:

    • Review from Khan Academy summarizing the process of gene expression here. 

    • Video on regulation of gene transcription here. 

    • Short videos on regularized regression Part 1, Part 2, Part 3, and Part 4

    • First TWAS paper here

Week 6: Confounding and causality

• Lecture 11 (link) - CV, Gene expression, and causality

• Lecture 12 (link) - Gene expression, confounding, and causality cont'd

  • Optional:

    • Cross-validation video lecture here. 

    • Historical paper by Jeff Leek and co-authors, studies batch effect issues in gene expression datasets here.

    • Causality, "an introduction" blog post here. 

    • Video lecture on SVD from Prof. Strang (MIT) here. 

Week 7: Regulatory Genomics I

• Lecture 13 (link) - Gene regulation - network biology I

• Lecture 14 (link) - Gene regulation - TF motif finding

  • Optional:

    • Paper explaining label propagation algorithm here. 

    • Short video on Position Weight Matrices and motif finding here. 

    • Review of epigenomic measurement assays here. 

Week 8: Regulatory Genomics II

• Lecture 15 (link) - Gene regulation - Intro Convolutional Neural Networks

• Lecture 16 (link) - Gene regulation - Intro Explainable AI algorithms

  • Optional:

    • Blog post on Convoltional Neural Networks (CNNs) here

    • Review article on Explainable AI for understanding gene regulation here. 

    • Pioneering research paper on using CNNs for understanding gene regulation here. 

Week 9: Single Cell Genomics I

• Lecture 17 (link) - Intro to single cell technologies

  • A nice tutorial for single-cell genomics by Sanger Insistute 

  • Great lecture by Aviv Regev - An Atlas of Human Cell

• Lecture 18 (link) - Unsupervised learning

  • StateQuest video on k-means clustering here. 

  • Lecture on unsupervised ML algorithms for single cell datasets here

Week 10: Single Cell Genomics II

• Lecture 19 (link) - Data integration

• Lecture 20 (link) - Current research in single cell genomics

  • Optional:

    • Two recent papers on benchmarking data integration methods here and here

    • Lecture by Geoff Hinton, from PCA to autoencoders here