CSE427 - Handouts

CSE427

Introduction (1 lecture)

Sequence Analysis (3.5 lectures)

Systems Biology (8.5 lectures)

Genetics (5 lectures)

Applications (2 lectures)

Introduction (1 lecture)

A zero-knowledge based introduction to molecular biology for computer scientists
Brief introduction of the example projects

Lecture 1 (1/7):

Introduction to the course [PPT] [PDF]
A zero-knowledge based introduction to molecular biology [PPT] [PDF]
- Transcription/translation: https://www.youtube.com/watch?v=9kOGOY7vthk
- Central dogma (10 mins): https://www.youtube.com/watch?v=J3HVVi2k2No

Sequence Analysis (3.5 lectures)

dynamic programming
sequence alignment

Lecture 2 (1/9) Global sequence alignment [PPT] [PDF]

Chap 2.1-2.4 of Durbin, Eddy, Krogh and Mitchinson "Biological Sequence Analysis" [link]
What is dynamic programming?, Nature Biotechnology 2004 [link]

Lecture 3 (1/14) Local alignment [PPT] [PDF]

Chap 2.6-2.8 of Durbin [link]

Lecture 4 (1/16) Database search, Linear space algorithm [PPT] [PDF]

Chap 2.5 of Durbin [link]
BLOSUM: Amino acid substitution matrices from protein blocks, PNAS 1992 [link]

Lecture 5 (1/21) Scoring matrices [PPT] [PDF]

Where did the BLOSUM62 alignment score matrix come from? Nature Biotechnology 2004 [link]

Systems Biology (8.5 lectures)

Bayesian networks: parameter learning, structure learning
gene expression data, biological network learning, disease classification

Lecture 5 (1/21) Gene expression data, basics of Bayesian networks [PPT] [PDF]

A Primer on Learning in Bayesian Networks for Computational Biology [link]
Chap 2.1-2.3, 3.1-3.3, 5 of Koller and Friedman "Probabilistic Graphical Models: Principles and Techniques"

Lecture 6 (1/23) Basics of Bayesian networks, gene regulatory networks [PPT] [PDF]

A Primer on Learning in Bayesian Networks for Computational Biology [link]
Chap 2.1-2.3, 3.1-3.3, 5 of Koller and Friedman "Probabilistic Graphical Models: Principles and Techniques"
Genome Assembly
- Prof. Ben Langmead's lecture video and lecture note

Lecture 7 (1/28) Parameter learning of Bayesian networks, maximum likelihood estimation (MLE) [PPT] [PDF]

A Primer on Learning in Bayesian Networks for Computational Biology [link]
Parameter learning: Chap 17 of Koller and Friedman "Probabilistic Graphical Models: Principles and Techniques"

Lecture 8 (1/30) MLE in Bayesian networks, structure learning of Bayesian networks [PPT] [PDF]

A Primer on Learning in Bayesian Networks for Computational Biology [link]
Parameter learning: Chap 17 of Koller and Friedman "Probabilistic Graphical Models: Principles and Techniques"
David Heckerman, "A Tutorial on Learning With Bayesian Networks" [link]

Lecture 9 (2/4) Learning undirected graphical models [PPT] [PDF]

Graphical lasso: Sparse inverse covariance estimation with the graphical lasso [link]
Karthik Mohan, Mike Chung, Seungyeop Han, Daniela Witten, Su-In Lee, and Maryam Fazel (2012). Structured Learning of Gaussian Graphical Models. Neural Information Processing Systems (NIPS). [link]
New Insights and Faster Computations for the Graphical Lasso, Journal of Computational and Graphical Statistics 2011 [link]
The joint graphical lasso for inverse covariance estimation across multiple classes, Journal of the Royal Statistical Society Series B 2014 [link]
Estimating Sigma Inverse for Graphical Lasso [link]

Lecture 10 (2/6) Learning the regulatory network I [PPT] [PDF]

Chap 18 of Koller and Friedman "Probabilistic Graphical Models: Principles and Techniques"
Module Networks, Nature Genetics. 2003 [link]
Learning a Prior on Regulatory Potential from eQTL Data, PLoS Genetics. 2009 [link]

Lecture 11 (2/11) Learning the regulatory network II [PPT] [PDF]

Module Networks, Nature Genetics. 2003 [link]
Module Graphical Lasso, ICML 2014 [link]
INSPIRE, Genome Medicine 2016 [link]

Lecture 12 (2/13) Module Undirected Graphical Model, Clustering [PPT] [PDF]

Module Graphical Lasso, ICML 2014 [link]
INSPIRE, Genome Medicine 2016 [link]

Lecture 13 (2/18) Motif finding [PPT]

MEME: Unsupervised learning of multiple motifs in biopolymers using expectation maximization, Machine Learning. 1995 [link]
AlignACE: Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation, Nature Biotechnology. 1998 [link]

Genetics (5 lectures)

L1/L2 regularized linear regression, EM algorithm
QTL analysis, genome-wide association studies, haplotype reconstruction

Lecture 14 (2/20) Basics of genetics, Quantitative trait loci (QTL) analysis [PPT]

Review of statistical methods for QTL mapping in experimental crosses [link]
Quantitative trait loci analysis, Nature Education 2008 [link]
An interesting video on meiosis [link]

Lecture 15 (2/25) Single-/multi- marker model; significance analysis [PPT]

Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions, BMC proceedings 2012 [link]
Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society, Series B. 1996 [link]

Lecture 16 (2/27) Haplotype reconstruction [PPT]

Clark's haplotyping algorithm: Inference of haplotypes from PCR-amplified samplese of diploid populations, Mol. Biol. Evol. 1990 [link]
The EM-based haplotyping algorithm: Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population, Mol. Biol. Evol. 1995 [link]
Divide-And-Conquer Approximation: Partition-ligation-expectation-maximization algorithm for haplotype inference with single-nucleotide polymorphisms, America Journal of Human Genetics 2002 [link]

Lecture 17 (3/3) Disease association studies [PPT]

Evaluating and improving power in whole-genome association studies using fixed marker sets, Nature Genetics 2006 [link]
International HapMap Project [link]
Genotype imputation, Annual Review of Genomics and Human Genetics 2009 [link]

Lecture 18 (3/5) Methods for multiple hypothesis testing correction; statistical methods for rare variants association studies (RVASs) [PPT] [Video 1] [Video 2]

Clark's haplotyping algorithm: Inference of haplotypes from PCR-amplified samplese of diploid populations, Mol. Biol. Evol. 1990 [link]
The EM-based haplotyping algorithm: Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population, Mol. Biol. Evol. 1995 [link]

Applications (2 lectures)

Lecture 19 (3/10) Learning chromatin networks using undirected graphical models I [Zoom] (password sent to mailing list) [PPT]

Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, and Su-In Lee. ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data. Genome Biology 2016 Apr 30;17(1):82. [link]

Lecture 20 (3/12) Learning chromatin networks using undirected graphical models [Zoom] (password sent to mailing list) [PPT]

Scott M. Lundberg, William B. Tu, Brian Raught, Linda Z. Penn, Michael M. Hoffman, and Su-In Lee. ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data. Genome Biology 2016 Apr 30;17(1):82. [link]

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