Computational Biology: Genomes, Networks, Evolution
Lecture Notes
This section contains scribe notes and slides from selected lectures. Students were required to write up scribe notes; see the syllabus for more details.
All scribe notes are courtesy of 6.047 students and are used with permission.
|
SES # |
TOPICS |
SCRIBE NOTES |
SLIDES |
|
L1 |
Introduction: biology, algorithms, machine learning |
(PDF) |
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|
L2 |
Global / local alignment, dynamic programming |
(PDF) |
(PDF) |
|
L3 |
String search, BLAST, database search |
(PDF) |
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|
L4 |
Clustering basics, gene expression, sequence clustering |
|
(PDF) |
|
L5 |
Classification, feature selection, SVM |
(PDF) |
(PDF) |
|
L6 |
HMMs 1: evaluation, parsing |
(PDF) |
(PDF) |
|
L7 |
HMMs 2: posterior decoding, learning |
(PDF) |
(PDF) |
|
L8 |
Generalized HMMs and gene prediction |
(PDF) |
|
|
L9 |
Regulatory motifs, Gibbs sampling, EM |
|
(PDF) |
|
L10 |
Gene evolution: phylogenetic algorithms, NJ, ML, parsimony |
(PDF) |
(PDF) |
|
L11 |
Molecular evolution, coalescence, selection, Ka/Ks Guest lecturer: Daniel Neafsey, Broad Institute |
(PDF) |
(PDF) (Courtesy of Daniel Neufsey. Used with permission.) |
|
L12 |
Population genomics: fundamentals Guest lecturer: Pardis Sabeti, Harvard Systems Biology |
(PDF) |
|
|
L13 |
Population genomics: association studies Guest lecturer: Pardis Sabeti, Harvard Systems Biology |
(PDF) |
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L14 |
Midterm |
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|
L15 |
Genome assembly, Euler graphs |
(PDF) |
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L16 |
Comparative genomics 1: biological signal discovery, evolutionary signatures |
(PDF) |
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L17 |
Comparative genomics 2: phylogenetics, gene and genome duplication |
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L18 |
Conditional random fields, gene finding, feature finding |
(PDF) |
(PDF) |
|
L19 |
Regulatory networks, Bayesian networks |
|
(PDF) |
|
L20 |
Inferring biological networks, graph isomorphism, network motifs |
(PDF) |
|
|
L21 |
Metabolic modeling 1: dynamic systems modeling |
(PDF) |
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L22 |
Metabolic modeling 2: flux balance analysis and metabolic control analysis |
(PDF) |
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|
L23 |
Systems biology Guest lecturer: Uri Alon, Weizmann Institute of Science |
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|
L24 |
Module networks Guest lecturer: Aviv Regev, Broad Institute |
(PDF) |
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|
L25 |
Synthetic biology Guest lecturer: Tom Knight, MIT Computer Science and Artificial Intelligence Laboratory |
(PDF) |
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L26 |
Final presentations |
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Recitations
Weekly recitations are held during the first eight weeks of the course. Notes are available for selected recitations below.
These notes are courtesy of the course TAs, Pouya Kheradpour and Matt Rasmussen, and are used with permission.
|
SES # |
TOPICS |
|
R1 |
Probability, statistics, biology (PDF) |
|
R2 |
Affine gaps alignment, hashing with combs |
|
R3 |
Microarrays (PDF) |
|
R4 |
Posterior decoding review, Baum-Welch learning |
|
R5 |
Entropy, information, background models (PDF) |
|
R6 |
Gene trees, species trees, reconciliation (PDF) |
|
R7 |
Population genomics |
|
R8 |
Brainstorming for final projects |
Assignments
Problem Sets
|
PROBLEM SETS |
SUPPORTING FILES |
|
Problem set 1 (PDF) |
(ZIP) (The ZIP file contains 7 .fa files and 4 .py files.) |
|
Problem set 2 (PDF) |
(ZIP) (The ZIP file contains 5 .txt files and 1 .py file.) |
|
Problem set 3 (PDF) |
(ZIP) (The ZIP file contains 4 .py files and 8 files with no extensions.) |
|
Problem set 4 (PDF) |
(ZIP) (The ZIP file contains 3 .txt files and 1 .gff file.) |
Final Project
|
PROJECT FILES |
|
Milestone 1 (PDF) |
|
Milestone 2 (PDF) |
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Guidelines for report and presentations (PDF) |
Exams
The Fall 2007 midterm was provided as a practice exam. This course had no final exam.
|
EXAMS |
|
Fall 2007 midterm (PDF) |
|
Fall 2008 midterm (PDF) |