CSE427: Computational Biology

Week 1: Intro and review

• Lecture 1 (link) - Intro to Computational Biology

  • Optional:

  • Short videos describing central dogma of biology here and here

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

Week 2: Intro and Genetics

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

• Lecture 3 (link) - GWAS I

• Optional: 

  • Introduction to ML, chapter 1 of Bishop here

  • ML/Stats short videos: (1) linear regression, (2) intro ML concepts, (3) computing p-values and (4) interpreting p-values 

Week 3: Human genetics and GWAS II

• Lecture 4 (link) - GWAS II

• Lecture 5 (link) -  Confounding and statistical adjustment

  • Optional:

    • Maximum likelihood and derivation of logistic regression here

    • GWAS review paper here

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

Week 4: Statistical inference for GWAS II

• Lecture 6 (link) - Heritability estimates

• Lecture 7 (link) - Linear interaction models 

  • Optional:

    • Population structure in genetics review article here 

    • Accounting for population structure using LMMs here

    • PCA videos: 20min video from StatQuest

    • 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 4: Multi-omics association studies 

• Lecture 8 (link) - Gene expression and eQTL studies

• Lecture 9 (link) - Multi-SNP regression and regularization

  • 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

Week 5: Multi-omic association studies II

• Lecture 10 (link) - TWAS & Causality

• Lecture 11 (link) - Causality

  • Optional:

    • First TWAS paper here

    • Causality, "an introduction" blog post here. 

Week 6: Regulatory Genomics I

• Lecture 12 (link) - Gene regulation - TF motif finding

• Lecture 13 (link) - Gene regulation - Epigenomic assays

  • Optional:

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

    • Review of epigenomic measurement assays here. 

Week 7: Regulatory Genomics II

• Lecture 14 (link) - Gene regulation - Intro Convolutional Neural Networks

• Lecture 15 (link) - Gene regulation - CNNs and regulatory genomics

  • Optional:

    • Blog post on Convoltional Neural Networks (CNNs) here

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

Week 9: Regulatory Genomics III

• Lecture 16 (link) - explainable AI algorithms and regulatory genomics

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

Week 9: Single Cell Genomics II

• Lecture 17 (link) - Inro single cell genomics

• Lecture 18 (link) - Unsupervised learning

  • StateQuest video on k-means clustering here. 

  • Lecture on unsupervised ML algorithms for single cell datasets here

  • 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 computational biology