Course information

Course:
COBI 8201/8202 (a.k.a. BIMS 8701/8702): Introduction to Computational Biology I/II
Term:
Spring 2025
Times:
Mon/Wed 4:00pm-5:30pm
Location:
McKim Hall 1023 (BEC Classroom) (see map)
Description:
This course introduces students to principles and practical applications of methods in computational biology.
Grading:
  • Homework assignments - 75% (6 assignments per module)
  • Quizzes - 25% (6 quizzes per module, lowest score dropped)

Lectures

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

1 2025-02-10
Course overview and introduction to computational biology
Course overview, history of computational biology, introduction to Bioconductor
Nathan Sheffield
2 2025-02-12
Statistics and probability review
Random Variables, Probability Distributions, Central Limit Theorem, Hypothesis Testing, P-value, Type I and Type II Errors, Multiple Testing Correction, FDR
Chongzhi Zang
    3 2025-02-17
    Sequence alignment
    Local vs. global alignment, Dynamic programming, Heuristic approaches, BLAST, Short-read alignments
    Aakrosh Ratan
    4 2025-02-19
    Sequence alignment lab
    Smith-Waterman algorithm
    Aakrosh Ratan
      5 2025-02-24
      Genome assembly
      Pairwise overlaps, Overlap-layout-consensus strategy, De Bruijn graphs
      Aakrosh Ratan
      6 2025-02-26
      Genome assembly lab
      Shortest superstring problem, Removal of transitive edges, Eulerian walks
      Aakrosh Ratan
        7 2025-03-03
        Transcription factors
        Chongzhi Zang
        • Quiz 3
        • Assignment 3 due
        • Assignment 4
        8 2025-03-05
        Transcription factor lab
        Chongzhi Zang
            9 2025-03-10
            Differential expression analysis
            Stefan Bekiranov
            • Quiz 4
            • Assignment 4 due
            • Assignment 5
            10 2025-03-12
            Differential expression lab
            Stefan Bekiranov
                11 2025-03-17
                Molecular evolution and phylogenetics
                History of molecular evolution, Sequence divergence and models of sequence evolution, Tree-building, UPGMA, Neighbor-joining, parsimony, maximum likelihood.
                Nathan Sheffield
                12 2025-03-19
                Molecular evolution and phylogenetics lab
                Nathan Sheffield
                    13 2025-03-24
                    Special Topic: LLMs
                    Chirag Agarwal
                      14 2025-03-26
                      Module I Review
                      Aakrosh Ratan

                        Computational Biology II

                        15 2025-03-31
                        Dimensionality reduction
                        Chongzhi Zang
                        16 2025-04-02
                        Dimensionality reduction lab
                        Chongzhi Zang
                            17 2025-04-07
                            Genomic interval analysis
                            Algorithms and data structures for genomic interval arithmetic, enrichment analysis.
                            Nathan Sheffield
                            18 2025-04-09
                            Genomic interval analysis lab
                            Nathan Sheffield
                                19 2025-04-14
                                Deep learning in biology
                                Stefan Bekiranov
                                20 2025-04-16
                                Deep learning lab
                                Stefan Bekiranov
                                    21 2025-04-21
                                    Network analysis
                                    John Platig
                                    22 2025-04-23
                                    Network analysis lab
                                    John Platig
                                        23 2025-04-28
                                        RNA Binding proteins
                                        John Platig
                                          • Quiz 9
                                          • Assignment 10 due
                                          • Assignment 11
                                          24 2025-04-30
                                          RNA Binding proteins lab
                                          John Platig
                                              25 2025-05-05
                                              Special topic
                                              Gloria Sheynkman
                                              26 2025-05-07
                                              Module II Review
                                              Nathan Sheffield

                                                Class participation

                                                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.

                                                Quizzes

                                                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.

                                                Assignments

                                                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.

                                                Collaboration and AI on assignments

                                                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.

                                                Late assignments

                                                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.

                                                Office hours

                                                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.

                                                Missing lectures

                                                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.

                                                Recordings

                                                We do not intend to record or broadcast lectures.