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 |
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Computational Biology I |
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1 | 2025-02-10 | Course overview and introduction to computational biologyCourse overview, history of computational biology, introduction to Bioconductor |
Nathan Sheffield | ||
2 | 2025-02-12 | 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 | 2025-02-17 | Sequence alignmentLocal vs. global alignment, Dynamic programming, Heuristic approaches, BLAST, Short-read alignments |
Aakrosh Ratan |
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4 | 2025-02-19 | Sequence alignment labSmith-Waterman algorithm |
Aakrosh Ratan | ||
5 | 2025-02-24 | Genome assemblyPairwise overlaps, Overlap-layout-consensus strategy, De Bruijn graphs |
Aakrosh Ratan |
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6 | 2025-02-26 | Genome assembly labShortest superstring problem, Removal of transitive edges, Eulerian walks |
Aakrosh Ratan | ||
7 | 2025-03-03 | Transcription factors |
Chongzhi Zang |
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8 | 2025-03-05 | Transcription factor lab |
Chongzhi Zang |
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9 | 2025-03-10 | Differential expression analysis |
Stefan Bekiranov |
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10 | 2025-03-12 | Differential expression lab |
Stefan Bekiranov |
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11 | 2025-03-17 | 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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12 | 2025-03-19 | Molecular evolution and phylogenetics lab |
Nathan Sheffield |
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13 | 2025-03-24 | Special Topic: LLMs |
Chirag Agarwal |
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14 | 2025-03-26 | Module I Review |
Aakrosh Ratan | ||
Computational Biology II |
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15 | 2025-03-31 | Dimensionality reduction |
Chongzhi Zang | ||
16 | 2025-04-02 | Dimensionality reduction lab |
Chongzhi Zang |
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17 | 2025-04-07 | Genomic interval analysisAlgorithms and data structures for genomic interval arithmetic, enrichment analysis. |
Nathan Sheffield |
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18 | 2025-04-09 | Genomic interval analysis lab |
Nathan Sheffield |
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19 | 2025-04-14 | Deep learning in biology |
Stefan Bekiranov |
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20 | 2025-04-16 | Deep learning lab |
Stefan Bekiranov |
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21 | 2025-04-21 | Network analysis |
John Platig |
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22 | 2025-04-23 | Network analysis lab |
John Platig |
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23 | 2025-04-28 | RNA Binding proteins |
John Platig |
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24 | 2025-04-30 | RNA Binding proteins lab |
John Platig |
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25 | 2025-05-05 | Special topic |
Gloria Sheynkman |
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26 | 2025-05-07 | Module II Review |
Nathan Sheffield |
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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. Quizzes are done individually, so you should not consult with outside help (web sites, AI, classmates) during the quizzes.
Each module includes 6 homework assignments. These assignments will include programming, theoretical problems, and data analysis. Each assignment is worth 12.5% of the final grade for the module. The assignments will be assigned each week, and will be due one week later.
We encourage using whatever tools will help you learn best, including each other or external AI. However, you will learn most if you sincerely attempt the lessons on your own before seeking outside help. Therefore, we expect you to give a sincere attempt on all assignments individually. We expect that you will use external resources (each other, AI, etc) to answer specific questions after you have attempted them on your own. Do not simply copy solutions to assignments from classmates or AI.
All assignments are due one week later. Please turn in your assignments on time. It is easier for the instructor to grade assignments all at once, and turning in assignments late will get you behind because you will already have another assignment. Nevertheless, because we know sometimes things are busy, all assignments will have a one week grace period after the deadline in which you may turn them in without penalty. But to encourage you to complete the assignments in a reasonable timeframe, any assignment turned in more than a week after the posted deadline will incur a 50% penalty.
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. We are also often available for a few minutes after class.
If you need to miss a lecture, we will address it on a case-by-case basis. Contact the instructor of the lecture you need to miss, who will suggest any materials you should review for that unit. All the lectures are also posted, so you can go through them on your own. Your lowest quiz score in each module will be dropped automatically to accommodate absences. Please try not to miss more than one quiz per module.
We do not intend to record or broadcast lectures.