Course Aims and Description
- Comprehend the biological background, nature, and relevance of
computational problems in
molecular biology.
- Assess the efficiency of computational methods for handling data-rich problems in the field.
- Understand computational techniques and probabilistic models for working effectively with
large data sets.
- Discuss and evaluate tradeoffs involved in choosing how to tackle hard
computational problems.
- Develop experience applying theoretical CS material in a practical setting.
Mastery of these aims will be achieved and assessed through readings, discussions, problem sets, algorithm implementation or data analysis assignments, two in-class midterms, and a final exam. About half of the course will focus on molecular sequences and sequence manipulation; the rest will focus on issues of interpretation, which require more complex data and methods. We will talk about scalability and how and when approximate solutions are appropriate. In addition, we will introduce some ongoing areas of research in the fields of bioinformatics and computational biology.
Students will be expected to contribute to class discussion and group activities, to do the assigned reading, and to read supplementary background materials as they find necessary.
Course Staff and office hours:
Professor Donna Slonim (she/her) is the course instructor.
CS PhD student Blessing Kolawole will be our graduate teaching assistant. CS Major Adhvith Reddy is our undergraduate course assistant. TA office hours: Mondays at 7-8:30pm and Thursdays at 12:30-2pm, in JCC 349.
Email addresses are firstname dot lastname at tufts dot edu, but you can reach all the course staff at once via Piazza at all times.
Instructor Office Hours: Tuesdays, 1:30-2:45pm, and Fridays, 2:30-4pm, or by appointment. In-person office hours will be held in JCC 322. Zoom office hours (any day that we have classes online, or by request) and online appointments will be at my personal Zoom room (see private course page for links).
Course Requirements
Prerequisites: CS 15 and at least one 100-level computer science course, or graduate standing in Computer Science, or permission of the instructor.
No biology background required!
Graduate standing in a related field (Biomedical Engineering, Biology, Genetics) may be sufficient providing your computer programming background is strong enough and you know something about algorithm analysis; check with the instructor, and read the following paragraphs first.
Homework assignments will include several implementation projects in Python. We will learn about algorithms in class and in the readings, but you will then be expected to implement them from scratch and apply them, without much formal help in designing the code.
Also essential will be some basic understanding of algorithm analysis, as is typically covered in CS 15. You should be familiar with asymptotic analysis of algorithmic running times and Big O notation, at least at an introductory level. CS 160 (Algorithms) is helpful but not essential as a prerequisite; material used here will help you when you take Algorithms if you have not yet done so.
Readings: The course textbook is Understanding Bioinformatics by Marketa Zvelebil and Jeremy O. Baum, published by Garland Science (a subsidiary of Taylor & Francis Group). Copies of the text should be available in the Medford campus bookstore, or you can order or rent a copy online. Online orders are typically available immediately.
Readings from this text will be listed in the schedule where appropriate. Supplementary readings from the literature or from some of the recommended textbooks listed below appear on the schedule as well.
If you have no biology background, you may want to supplement the readings as well by getting a good introductory molecular biology text. (Several online texts are available for looking up occasional details).
We will do two or three collective "journal club" activities to introduce some class material. These dates are announced on the web schedule. Please read the the journal club papers listed in the schedule before class on the indicated day. During class, you will be assigned to join a group of students. Each group will be given a slide with questions on it about some aspect of the paper. You group will collaboratively edit the slide with answers to the questions on that slide. We will then have each team present their slide in order, making up a presentation covering the key points of the whole paper.
Other recommended books:
- Bioinformatics and Functional Genomics , by Jonathan Pevsner. A readable introduction to the field. Aimed primarily at biologists, provides somewhat less detail than the course text but may be slightly more approachable.
- The Cartoon Guide to Genetics by Larry Gonick and Mark Wheelis. A surprisingly good and serious introduction to the biological concepts covered in this course.
- An Introduction to Bioinformatics Algorithms, by N. Jones and P. Pevzner. An algorithms text focusing on examples motivated by computational biology. Helpful if you've never taken an algorithms class; provides a more gentle introduction to selected topics than the following book.
- Introduction to Algorithms, by T. Cormen, C. Leiserson, R. Rivest, and C. Stein. The canonical algorithms textbook. Has nothing to do with biology, but should be on every computer scientist's bookshelf.
- Introduction to Computational Molecular Biology, by J. Setubal and J. Meidanis. A detailed text focused on computational biology algorithms, aimed at computer scientists. From 1997, but covers several complex topics in depth.
- Biological Sequence Analysis, by R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. A good computational biology text focusing on sequence analysis, HMMs, and phylogeny. Includes an excellent whirlwind introduction to statistics.
- Molecular Biology, by David Freifelder. A general introductory molecular biology text. Easy to read, a gentle introduction to the topic.
- Molecular Biology of the Gene, by J. Watson, N. Hopkins, J. Roberts, J. Steitz, and Alan Weiner. A more advanced and detailed molecular biology text. A very thorough index makes this a good reference book.
Computational resources:
If you need help in obtaining computational resources, you need an account but never received email about its creation, or you are a non-traditional student or auditor who may not be enrolled in SIS, please contact the instructor or teaching assistant as soon as possible. If you didn't receive email but did take a CS course in a prior semester, try resetting your password first; most of the time the account still exists and this will work.
Any code you write for your homework will be graded based on its ability to run on the machine homework.cs.tufts.edu. Please be sure to test your code there; just because it works on your laptop does not mean it will work on a different machine or platform!
Policies
Grading: Grades will be based on homework assignments (45%), including both written and programming components, two in-class midterms (15% each), a final exam (20%), and class participation (5%).
Late policy: Submissions are due by midnight on the indicated date; Gradescope's timestamp is official. For late work, we are going to use a token policy. You will have 10 tokens for the term. You may use up to 2 tokens per assignment; each token gets you an extra day, which is 24 hours as counted by Gradescope. (Exception: homework 4 has a hard 2nd-token deadline of 6pm on April 2nd, to allow for midterm review without compromising the homework.) To use a token, you don't need to tell anyone, just submit and we will count the number of late days as the number of tokens used. It is your job to keep track of your token usage. Beyond the 10 tokens, we will not accept late submissions; submit what you have by the deadline for partial credit.
Turning work in on time is important for consistency in grading, because it allows us to discuss homework content in class in a timely fashion. Content builds on previous material, so it is important to figure out quickly if you are lost.
As usual, in the case when your studies are interrupted by serious illness or other truly exceptional circumstances (e.g., situations where your Academic Dean is involved), let us know and we will work something out.
Diversity, Inclusion, and Collegiality: Tufts, the Computer Science Department, and the course staff intend to create a welcoming environment in which all students feel supported and believe that their learning needs and perspectives are valued. We intend to present materials in ways that are respectful to students of any background, ethnicity, race, culture, gender, sexual orientation, or age. We welcome your suggestions on how to improve course effectiveness for yourself or others. If you have religious conflicts with class meetings or requirements, please connect with the course staff.
In this class, we will encourage questions, discussions, and some assignments that involve interacting in groups. While disagreements and differing opinions can be an important part of the learning experience, we expect all students to treat each other with collegiality and respect. Please reach out to course staff if there are any issues with inter-student interactions. While we do not expect this will be necessary, please be reminded that we will, if needed, follow the steps outlined in Tufts' sexual misconduct and non-discrimination policies.
Please also be aware that Tufts faculty are "mandated reporters": if we see, hear, or learn about any kind of discrimination or sexual misconduct, we are required to report it to the university. If you would prefer to access confidential counseling for an issue, you can find relevant resources here.
Accomodation for Students with Disabilities: Tufts University values the diversity of our students, staff, and faculty, recognizing the important contribution each student makes to our unique community. Tufts is committed to providing equal access and support to all qualified students through the provision of reasonable accommodations, so that each student may fully participate in the Tufts experience.
If you have a disability that requires reasonable accommodations, please contact the Student Accessibility and Academic Resources (StAAR) Center or call 617-627-4539 to make an appointment with a StAAR representative to determine appropriate accommodations. Please be aware that accommodations cannot be enacted retroactively, making timeliness a critical aspect for their provision.
In addition to following the standard procedures, if you have a disability and would like to discuss how we can better support your learning, please feel free to set up an appointment with course staff.
Academic Integrity: The Tufts academic integrity policy and code of conduct appears here. In particular, plagiarism will not be tolerated. Submitting as your own any written work or code that you did not write yourself, without the help of any other person or entity, is a violation of the academic integrity process.
Please see our collaboration policy below describing what is and is not acceptable in the context of this course. If you are not certain what constitutes plagiarism, please see the academic integrity resources at the link above or ask the course staff.
Please be aware that if Tufts faculty find evidence of academic misconduct, we are required to report it to the university. Penalties can be truly draconian. The time you save in using someone else's work will be lost ten times over as you work through the academic integrity process. So please, don't put yourself through it. We are eager to help you learn what you need to in order to complete complete the work yourself.
Collaboration Policy: All written work and code submitted should be your own unless you obtain prior permission to collaborate. You are free to discuss assignments with others in the class unless specifically asked not to, but you must write up your answers and code yourself.
We reserve the right to use computational tools to identify instances of plagiarism or materials (text or code) first written by someone - or something - else, whether published online or previously or concurrently submitted at Tufts. We may make use of plagiarism or similarity detection tools such as TurnItIn, Moss, GPTZero, or other methods to detect inappropriate conduct. We also reserve the right to ask you to verbally explain, in person, any content you submit under your name.
All sources used should be cited. In other words, if you discuss a homework problem with a classmate, you should list that classmate as one of your references for that problem. Please also be warned that not everything you read online is correct. (This is true of print sources as well, but the risk increases greatly online.) Chatbots notoriously hallucinate. Even data from supposedly reputable sources, such as slides posted by faculty at Tufts or other universities, may not have been reviewed by an editor and might contain crucial mistakes. For this reason, I'd like to discourage you from using Google to tackle the problem sets, but if you choose to do so, you must cite the URL(s) that you used. Directly copying text or code from any source without attribution is plagiarism and will be dealt with accordingly.
Course Materials
For homeworks, slides, and other class information, go to the private course materials page. You will need to log in using your CS department account and password. An account will be created for all students registered for the course in SIS who do not already have one.Tentative Course Schedule:
Updates will occur during the term: check back frequently. Shaded rows refer to past dates.| DATE | TOPICS | READING | OPTIONAL READING | |
| Thurs., Jan. 16 | Class overview and administrivia. Introduction to sequences and sequence comparison. |
This course Syllabus. Zvelebil & Baum (ZB): Chapter 1 and Section 4.1 |
For CS students new to biology: Larry Hunter's article,
Molecular Biology for Computer Scientists. For bio or BME students or others with less formal CS background: either Corman, Leiserson, Rivest and Stein Chapters 2 + 3, or Jones and Pevzner, Chapter 2: Bio O notation, NP-completeness. |
|
| Tues., Jan. 21 | Sequence alignment: Global alignment. Dynamic programming. Hwk 1 out |
This course syllabus ZB: Sections 4.2, 4.5 (pp. 87-89 only); 5.2 (pp. 127-135 only) |
Global alignment: Durbin, pp. 17-22. | |
| Thurs., Jan. 23 | Local alignment. Scoring schemes, gaps. | ZB: Sections 4.4, 5.2 (pp. 135-140), | Local alignment: Durbin, pp. 23-24, 29-30 | |
| Tues., Jan. 28 |
Scoring matrices, PAM and BLOSUM.
Hwk 1 due |
ZB: Sections 4.3, 5.1 | ||
| Thurs., Jan. 30 | Database search: introduction,
BLAST Hwk 2 out |
ZB: 4.6-4.7, 5.4 | ||
| Tues., Feb. 4 | BLAST scoring, Information content; FASTA | Altschul's tutorial on statistics of sequence similarity scores. Altschul's slides on information theory, scoring matrices, and E-values. | ||
| Thurs., Feb. 6 | Multiple sequence alignment: introduction, star alignment,
scoring, NP-completeness Hwk 2 part 1 due |
Ron Shamir's MSA notes | ZB: 4.5 (pp. 90-93), 6.4-6.5; Durbin, 6.1--6.4 | |
| Tues., Feb. 11 | MSA iterative and progressive methods | ZB: 6.1 | ||
| Thurs., Feb. 13 |
DNA motifs, profiles.
Hwk 2 part 2 due |
ZB: 6.6 | ||
| Tues., Feb. 18 | Midterm 1 | |||
| Thurs., Feb. 20 | NO CLASS: Monday schedule | |||
| Tues., Feb. 25 | Compressive BLAST journal club, sublinear search | Compressive BLAST paper | ||
| Thurs., Feb. 27 | Gibbs sampling for motif discovery.
Hwk 3 out |
Original paper on the Gibbs sampler for local multiple alignment | Durbin, 6.1--6.4; Original paper on MEME algorithm | |
| Tues., Mar. 4 | Sequence assembly: deBruijn graphs; Eulerian paths | The paper about the SOAPdenovo assembler. | ||
| Thurs., Mar. 6 | Overlap graphs, Hamiltonian paths, OLC assembly. Hwk 3 part 1 due |
ZB: 5.3(pp. 141-3) | ARACHNE paper on overlap-based whole genome assembly. | |
| Tues., Mar. 11 | Gene finding; Markov models; HMM intro | ZB: 9.2-9.7 | ||
| Thurs., Mar. 13 | Hidden Markov Models (HMMs) - Viterbi
Hwk 3 part 2 due |
Rabiner handout, pp. 257-266. | ||
| Tues., Mar. 18 | NO CLASS: Spring Break | |||
| Thurs., Mar. 20 | NO CLASS: Spring Break | |||
| Mon., Mar. 24 | Hwk 4 out | |||
| Tues., Mar. 25 | Finish Hidden Markov Models; HMM uses in gene finding. | Durbin: chapter 3 | ||
| Thurs., Mar. 27 | HMMs in Gene Finding; EM algorithm journal club | short paper on EM algorithms | ||
| Mon., Mar. 31 | Hwk 4 due | |||
| Tues., Apr. 1 | Gene expression: detecting differential expression, multiple testing | ZB: 15.1, 16.1, 16.4 | Slonim review article | |
| Thurs., Apr. 3 | Midterm 2 | |||
| Tues., Apr. 8 | Gene expression: RNA sequence alignment; clustering and classification
Hwk 5 out |
ZB: 16.2-16.3, 16.5 | Golub and Slonim et al., on leukemia classification | |
| Thurs., Apr. 10 | Transcriptomic interpretation; functional enrichment | |||
| Tues., Apr. 15 | Bioinformatics ethics discussion I Hwk 5 part 1 due |
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| Thurs., Apr. 17 | Gene set enrichment analysis journal club | Gene Set Enrichment Analysis | ||
| Tues., Apr. 22 | Bioinformatics ethics discussion II Hwk 5 part 2 due |
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| Thurs., Apr. 24 | Transcriptomics research: precision medicine, network inference; class wrap-up | |||
| Tues., Apr. 29 | Make-up class if needed | |||
| Tues., May 6, 3:30pm-5:30pm | Final Exam |