Resources include both required reading as well as additional secondary sources for your own follow-up. The (!!) icon indicates required reading; all other sources are secondary.
| # | Date | General topic | Instructor | Resources | Assignments/Quizzes |
|---|---|---|---|---|---|
Computational Biology I |
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| 1 | 2024-02-12 | Course overview and introduction to computational biologyCourse overview, history of computational biology, introduction to Bioconductor |
Nathan Sheffield | ||
| 2 | 2024-02-14 | Statistics and probability reviewRandom Variables, Probability Distributions, Central Limit Theorem, Hypothesis Testing, P-value, Type I and Type II Errors, Multiple Testing Correction, FDR |
Chongzhi Zang |
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| 3 | 2024-02-19 | Sequence alignmentLocal vs. global alignment, Dynamic programming, Heuristic approaches, BLAST, Short-read alignments |
Aakrosh Ratan |
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| 4 | 2024-02-21 | Sequence alignment labSmith-Waterman algorithm |
Aakrosh Ratan | ||
| 5 | 2024-02-26 | Genome assemblyPairwise overlaps, Overlap-layout-consensus strategy, De Bruijn graphs |
Aakrosh Ratan |
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| 6 | 2024-02-28 | Genome assembly labShortest superstring problem, Removal of transitive edges, Eulerian walks |
Aakrosh Ratan | ||
| 7 | 2024-03-04 | Molecular evolution and phylogeneticsHistory of molecular evolution, Sequence divergence and models of sequence evolution, Tree-building, UPGMA, Neighbor-joining, parsimony, maximum likelihood. |
Nathan Sheffield |
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| 8 | 2024-03-06 | Molecular evolution and phylogenetics lab, Perspective by Bill Pearson |
Nathan Sheffield, Bill Pearson | ||
| 9 | 2024-03-11 | Differential expression analysis |
Stefan Bekiranov |
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| 10 | 2024-03-13 | Differential expression lab |
Stefan Bekiranov |
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| 11 | 2024-03-18 | Transcription factors |
Chongzhi Zang |
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| 12 | 2024-03-20 | Transcription factor lab |
Chongzhi Zang |
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| 13 | 2024-03-25 | Module I Review |
Aakrosh Ratan |
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Computational Biology II |
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| 14 | 2024-03-27 | Dimensionality reduction |
Chongzhi Zang | ||
| 15 | 2024-04-01 | Dimensionality reduction lab |
Chongzhi Zang |
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| 16 | 2024-04-03 | Deep learning in biology |
Stefan Bekiranov |
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| 17 | 2024-04-08 | Deep learning lab |
Stefan Bekiranov |
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| 18 | 2024-04-10 | Genomic interval analysisAlgorithms and data structures for genomic interval arithmetic, enrichment analysis. |
Nathan Sheffield |
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| 19 | 2024-04-15 | Genomic interval analysis lab |
Nathan Sheffield |
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| 20 | 2024-04-17 | Hidden Markov ModelsMarkov chains, Hidden Markov Models, Viterbi, Baum-Welch, and forward-backward algorithms |
Nathan Sheffield |
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| 21 | 2024-04-22 | Hidden Markov Models lab |
Nathan Sheffield |
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| 22 | 2024-04-24 | Network analysis |
Aakrosh Ratan |
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| 23 | 2024-04-29 | Network analysis lab |
Aakrosh Ratan |
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| 24 | 2024-05-01 | Protein structure prediction |
Gloria Sheynkman |
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| 25 | 2024-05-06 | Protein structure prediction lab |
Gloria Sheynkman |
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| 26 | 2024-05-08 | Review |
Nathan Sheffield |
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Each module includes 6 homework assignments. These assignments will include programming, theoretical problems, and data analysis. The assignments will be assigned each week, and will be due one week later. Each assignment is worth 12.5% of the final grade for the module.
Students are expected to attend class. There is no textbook, but each lecture will have reading material posted. Students should read the lecture material before the lecture. You should plan to invest roughly 3 hours per week on reading the posted outside material. Quizzes are there to convince you to prepare for the lectures. The lectures will be most useful if you do the reading before the accompanying lecture so that you can come prepared with some background to ask questions.
Each week will start with a short (5-10 minute) quiz. The quiz will cover 1. The content of the preparatory reading material for the current week; and 2. The content from the lecture and lab component from the previous week.
Given the diversity of instructors in the course, we do not plan to hold regular office hours, but students should feel free to reach out to any instructor via e-mail to schedule a meeting. We will be available to meet individually with students as needed.
If you need to miss a lecture, we will address it on a case-by-case basis. Your lowest quiz score in each module will be dropped automatically. Please try not to miss more than one quiz per module.
We do not intend to record or broadcast lectures.
