COS557/MOL557:
Analysis & Visualization
of Large Scale Genomic Data Sets
Tuesdays 1:00pm-3:30pm
Rm. 280 in CIL (2nd
floor of the genomics building)
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 (though no need to know programming).
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.
Administrative
info:
Level: Graduate and upper level undergraduate
Background: Some understanding of computation, basic understanding of molecular biology can be acquired through suggested readings below
Format: Mixed lectures and seminar-style
Grading: 40% presentations
15% quizzes
15% participation (including attendance and participation in discussions)
30% final project (10% project proposal, 20% final project report)
Auditors: Auditors are welcome, must participate in presentations and discussions (but do not need to do the final project).
Please register on blackboard asap (even if you are an auditor). That’s where I’ll post the lecture slides and that’s where I’ll email announcements to the class.
When you access papers below, make sure you are doing so from the PU domain (you can use VPN if you are doing so from off-campus).
For more admin info, see syllabus):
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.
Some
suggested readings:
If you need to catch up on molecular biology and 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:
NCBI primer on microarray analysis
Some primers on computational techniques in bioinformatics:
What is principal component analysis?
What is the expectation maximization algorithm?
Getting started in probabilistic graphical models
Presentations:
Each presentation should be 30mins, with 15min 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, with background of important concepts (e.g. if the paper uses Dynamic Bayesian Networks, give an intro of what they are)
-critically evaluate the paper: what the paper did well, *what are problems/issues with the approach*, what puzzled you
-what should be the future of this method (don’t just retype the “future work” section, we’re looking for your analysis here)
Course Announcements (check here often):
PLEASE sign
up for the course on blackboard, or you won’t get any of the course-related
emails, which are important.
If you are
auditing, sing up for audit. If you are
a postdoc and can’t officially sign up, let me know,
and I’ll make sure
to
copy you on e-mails.
Course
schedule:
|
Wk |
Topic |
Papers |
Presenters |
|
1 (2/3) |
Introduction to the
course and bio |
Intro to the course and introduction to
biology and bioinformatics |
Lecture |
|
2 (2/10) |
Microarrays |
Microarray analysis introduction and
overview Hand and Heard “Finding
groups in gene expression data” (a very nice review of clustering
microarray data, present general concepts and choose 2-3 methods (not
hierarchical or kmeans or SOM) to describe in a bit
more detail) 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) Ehrenreich A. “DNA microarray technology for
the microbiologist: an overview.” (a nice intro to types of microarrays and
how microarray experiments work) |
Lecture Chris C._ |
|
3 (2/17) |
Microarrays |
Microarray data analysis:
from disarray to consolidation and consensus |
Jenny Harish |
|
4 (2/24) |
Regulation (modules
and pathways) |
A
factor graph nested effects model to identify networks from genetic
perturbations Inferring transcriptional
modules from ChIP-chip, motif and microarray data. A modular
approach for integrative analysis of large-scale gene-expression and
drug-response data |
Ana Chris P Jesse F |
|
5 (3/3) |
Next Generation
Sequencing |
Introduction to
Next generation sequencing Computational methods for Next Generation sequencing |
Lecture Lecture: Lars Philip |
|
6 (3/10) |
Bayesian methods in
biology and medicine |
Guest Lecture Required reading: Inference
in Bayesian Networks |
Guest Lecture Guest Lecture |
|
7 (3/24) note that 3/27 is spring break |
Data integration |
Introduction and overview of data
integration and networks prediction Guest Lecture: Intro to metabolomics |
Lecture Chris C. |
|
8 (3/31) |
Interactions and
Networks |
A
genomewide functional network for the laboratory
mouse Analysis of
the human protein interactome and comparison with
yeast, worm and fly interaction datasets (bio + analysis).
|
Alice Z Muneeb Aaron |
|
9 (4/7) |
Project proposal presentations |
Students present proposals for their final projects, graded on the quality of the proposed project, related prior work investigation, and quality of the presentation. Feedback on projects will be provided. |
All students taking the course for credit |
|
10 (4/14) |
Interactions,
networks, and pathways |
Global mapping of pharmacological space Refinement and expansion of signaling pathways: The osmotic response network in yeast Inferring gene networks from time series microarray data using dynamic Bayesian networks. |
Joe Irgon Matt Rich Chong W |
|
11 (4/21) |
Function prediction & Visualization |
Predicting gene function in a hierarchical context with an ensemble of classifiers Dynamic querying for pattern identification in microarray and genomic data Click and Expander: a system for clustering and visualizing gene expression data |
Timothy L Wei H_ David S. |
|
12 (4/28) |
Project in-progress presentations |
Presentation
of progress on the final project.
Graded on progress and presentation, students will receive feedback to
help them proceed. |
All students taking the course for credit |
|
|
Additional articles of interest (not required and won't be discussed in class) |
Inferring pathways and networks with a Bayesian framework 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) Factorgrams: A tool for visualizing multi-way
associations in biological data V Cheung, I Givoni,
D Dueck, BJ Frey Normalization
of Microarray Data: Single-Labeled and Dual-Labeled Arrays Getting
started in tiling microarray analysis |
|