BIO900 Computational Functional Genomics (grad) 2005 MIT
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MIT
7.90J (Spring 2005)
Instructors: Prof. David Gifford Prof. Tommi Jaakkola Level Graduate
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Three steps in the transcription of protein-coding genes. (Image by Prof. David Gifford.)
Course Highlights
This course features a complete set of lecture notes.
Course Description
The course focuses on casting contemporary problems in systems biology
and functional genomics in computational terms and providing
appropriate tools and methods to solve them. Topics include genome
structure and function, transcriptional regulation, and stem cell
biology in particular; measurement technologies such as microarrays
(expression, protein-DNA interactions, chromatin structure);
statistical data analysis, predictive and causal inference, and
experiment design. The emphasis is on coupling problem structures
(biological questions) with appropriate computational approaches.
Course Resources
Course Lecture Notes
| Lec # | TOPICS | LECTURER | |
|---|---|---|---|
| Part 1: Using DNA Sequence to Explain Mechanism | |||
| 1 | Course Introduction (PDF) | David Gifford | |
| 2 | Pairwise Alignment (PDF) | David Gifford | |
| 3 | Finding Regulatory Sequences in DNA: Motif Discovery (PDF) | Tommi Jaakkola | |
| 4 | Finding Regulatory Sequences in DNA: Motif Discovery (cont.) (PDF) | Tommi Jaakkola | |
| Part 2: Observing the Mechanism of Transcriptional Regulation | |||
| 5 | Microarray Technology (PDF) | David Gifford | |
| 6 | Expression Arrays, Normalization, and Error Models (PDF) | Tommi Jaakkola | |
| 7 | Expression Profiles, Clustering, and Latent Processes (PDF) | Tommi Jaakkola | |
| 8 | Computational Functional Genomics (PDF) | David Gifford | |
| 9 | Stem Cells and Transcriptional Regulation | David Gifford | |
| 10 | Part One: An Example of Clustering Expression Data (PDF) Part Two: Computational Functional Genomics (cont.) (PDF) |
David Gifford | |
| 11 | Project Group Meetings | ||
| 12 | Project Group Initial Presentations | Students | |
| 13 | Computational Discovery of Regulatory Networks (PDF - 2.3 MB) (Courtesy of Georg Gerber. Used with permission.) | Georg Gerber (Guest Lecturer) | |
| 14 | RNA Silencing (PDF) | David Bartel (Guest Lecturer) | |
| Part 3: Building Predictive Network Models of Transcriptional Regulation | |||
| 15 | Computational Functional Genomics (cont.) (PDF) | David Gifford | |
| 16 | Human Regulatory Networks (PDF) | David Gifford | |
| 17 | Protein Networks | David Gifford | |
| 18 | Causal Models (PDF) | Tommi Jaakkola | |
| 19 | Causal Bayesian Networks, Active Learning (PDF) | Tommi Jaakkola | |
| 20 | From Biological Data to Biological Insight (PDF) | Nir Friedman (Guest Lecturer) | |
| 21 | Modeling Transcriptional Regulation (PDF) | Tommi Jaakkola | |
| 22 | Dynamics | David Gifford | |
Technical Requirements
Any number of biological sequence comparison software tools can be used to import the FASTA formatted sequence (.fa) files found on this course site. MATLAB® software is required to view and run the .m and .mat files found on this course site. Postscript viewer software, such as Ghostscript/Ghostview, can be used to view the .ps files found on this course site. File decompression software, such as Winzip® or StuffIt®, is required to open the .zip files found on this course site.
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Copyright 2007,
by the Contributing Authors.
Cite/attribute Resource.
administrator. (2009, April 25). BIO900 Computational Functional Genomics (grad) 2005 MIT. Retrieved March 11, 2010, from Free University Courses OCW Courses OpenCourseWare Freeversity Foundation Web site: http://freeversity.org/science-and-mathematics/biology/computational-functional-genomics-7.90j-6.874j.
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