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COS557/MOL557:

Analysis & Visualization of Large Scale Genomic Data Sets

Fridays 1:30-4:00pm

Rm. 302 in CS building

(35 Olden street)

Expression of genes predictive of outcome in lung cancer.

Course Info

The goal of this course is to introduce students to computational issues involved in analysis and display of large-scale biological data sets. Techniques covered will include clustering and machine learning techniques for gene expression microarrays and proteomics data analysis, biological networks and pathways modeling, data integration in genomics, and visualization issues for large-scale data sets.

A short introduction to the field of bioinformatics and the nature of biological data will be provided, no prior knowledge of biology is required. In depth knowledge of computer science is not required, but students must have some understanding of computation.

The course will be taught in a mixed lectures and seminar format, and will involve completing a project and a final exam. The course is open to graduate and advanced undergraduate students from all departments.

Level: Graduate and upper level undergraduate

Background: Some understanding of computation (programming background not required)

Format: Mixed lectures and seminar-style

Instructor: Prof. Olga Troyanskaya

Grading: 40% class presentations, 15% class participation (including attendance), 45% final project (15% project proposal, 30% final project report)

Auditors: Auditors are welcome, but every auditor must participate in presentations and discussions (but does not need to do the final project).

Administrative:

There is no required book for this class. Material will be presented in lectures, and readings will be based on current literature. However, here are a few recommendations for the curious.

If you need to catch up on molecular biology and genetics:

DOE primer on human genetics

R. Brent. Genomic Biology. Cell 100:169-183, 2000.

L. Hunter. Molecular Biology for Computer Scientists. In Artificial Intelligence and Molecular Biology, L. Hunter editor, 1993, AAAI Press.

Introduction to bioinformatics:

P.L. Elkin. Primer on Medical Genomics Part V: Bioinformatics. In Mayo Clinic Proceedings.

NCBI bioinformatics primer

NCBI primer on microarray analysis

Presentations:

Each presentation should be 30mins, with 10-15mins for discussion afterwards. Presentations should be in power point (or another slides format), and you must e-mail me the power point after your presentation before I can grade it.

A good presentation would include:

-a brief overview of the paper

-outline of major methods and findings

-analysis of what the paper did well

-analysis of problems/issues with the approach

-what is the future (don’t just retype the “future work” section, we’re looking for your analysis here)

Course Announcements (check here often):

Wk	Topic	Papers	Presenters
1	Introduction to the course and bio	Intro to the course and introduction to biology and bioinformatics Reading: “Systems biology 101-what you need to know”	Lecture
2	Microarrays	Microarray analysis introduction and overview Required reading: Comparison and validation of statistical clustering techniques for microarray gene expression data Suggested readings: Lockhart et al "Genomics, gene expression, and DNA microarrays" (general microarray) Kaminski N et al "Practical approaches to analyzing results of microarray experiments" (review) **Hand and Heard “Finding groups in gene expression data” (a very nice and fairly complete review of clustering microarray data)	Lecture David Braun
3	Microarrays	Normalization and analysis of cDNA micro-arrays using within-array replications applied to neuroblastoma cell response to a cytokine. Generalized singular value decomposition for comparative analysis of genome-scale expression data sets of two different organisms Combined static and dynamic analysis for determining the quality of time-series expression profiles.	Yingying Fan Ronny Luss Qing Wang
4		CLASS CANCELLED
5 (3/10)	Regulation (modules and pathways)	Inferring quantitative models of regulatory networks from expression data Combining phylogenetic motif discovery and motif clustering to predict co-regulated genes. Genome-wide discovery of transcriptional modules from DNA sequence and gene expression.	Sonya Nikolova Wei Dong Clifford Lam
6 (3/17)	Data integration	Introduction and overview of data integration and networks prediction	Lecture
7 (3/31)	Interactions and Networks	Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Emergent behavior of growing knowledge about molecular interactions	Tim O’Connor Wei Dong
8 (4/7)	Bayesian methods in biology and medicine	Guest Lecture Required reading: Inference in Bayesian Networks The synthetic genetic interaction spectrum of essential genes (bio)	Joseph Kahn, Novartis Pharmaceuticals Adam Litterman
9 (4/14)	Interactions and Networks	Probabilistic model of the human protein-protein interaction network Creation and implications of a phenome-genome network Systematic interpretation of genetic interactions using protein networks	Jeffrey Traer Bernstein Jim Tonn David Braun
10 (4/21)	Comparative Genomics et al.	Functional genomic hypothesis generation and experimentation by a robot scientist A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules Detection of parallel functional modules by comparative analysis of genome sequences	Lisa Chung Tim O’Connor Adam Litterman
11 (4/28)	Project presentaitons and visualization	Final project proposals (5 min presentations, only students taking the course for credit. Using process diagrams for the graphical representation of biological networks Visualization methods for statistical analysis of microarray clusters.	Lisa Chung Jeffrey Traer Bernstein
12 (5/5)	Visualization	Click and Expander: a system for clustering and visualizing gene expression data Cluster stability and the use of noise in interpretation of clustering. (Interesting clustering algorithm + visualization) Visualization and analysis of microarray and gene ontology data with treemaps	Qing Wang Sonya Nikolova Jim Tonn
	Additional articles of interest	Inferring pathways and networks with a Bayesian framework