Welcome to Comp 167!
We'd like to encourage students to come to class via Sococo, the department's virtual collaboration space, in which case you can connect in Zoom without knowing the Zoom link or password. Just put your avatar in the room "Cummings 475" on the lower left of the main floor of "Virtual Halligan," and the Zoom link opens automatically during class time. You do need Tufts two-factor authentication (2FA) to log into Sococo: see the description under "computational resources" below.
If you'd rather connect directly through Zoom, the class Zoom link is on the private class materials page, which is also described under computational resources. You need a CS account to log in here. Both a CS account and Tufts 2FA are available to anyone who is enrolled in the class - talk to course staff if you need help with either of these.
- Comprehend the biological background, nature, and relevance of
computational problems in
- 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
Mastery of these aims will be achieved and assessed through readings, problem sets, algorithm implementation or data analysis assignments, and in-class quizzes (which will replace longer and more formal exams typical in non-pandemic years). 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. Finally, we will introduce ongoing areas of research in the fields of bioinformatics and computational biology.
Students will also 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.
Professor Donna Slonim is the course instructor.
CS PhD student James Mattei will be our graduate teaching assistant. Office hours: Mondays 1:30-3:00 and Wednesdays 10:30-12:00. Office hours will be held in zoom link posted on Piazza and the private page.
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: Weds., 4:15-5:30 and Fri., 2:30-3:45, or by appointment. Office hours will be held in Slonim office on lower left of main floor in Sococo, or personal zoom room (see private course page for links).
Prerequisites: Comp 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 with no further prerequisites; check with the instructor, and read the following paragraphs first
Comfort writing complex programs from scratch in some programming language is essential, as homework assignments will include several implementation projects. We allow some flexibility on what language you choose for implementation; select something that you are comfortable with and that seems suitable for the task. The most common computer languages students have used successfully are Python, C++, and Java. If you have another preference, please discuss your choice of language with the TA.
Also essential will be some basic understanding of algorithm analysis, as covered in Comp 15. You should be familiar with asymptotic analysis of algorithmic running times and Big O notation, at least at an introductory level. Comp 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 it online. The cost of renting an online version for the duration of the semester was $35 when I last checked in January 2021; online orders are 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 are going to try something new this term that will help us connect with each other in this online format: collective "journal clubs." Please read the the journal club papers listed in the schedule before class on the indicated day. During class, you will join a group in a breakout room, and each group will be given a slide with questions on it about some aspect of the paper. You group will use your breakout room time to edit the slide with answers to the questions on that slide. We will then return to the full group, where each team will present their slide in order, culminating in 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. A new 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 cannonical 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.
- 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.
- You will need access
to a computer with an
internet connection, support for whatever
programming language / tools you intend to use, and
the ability to remotely log into the department's computer systems.
- CS account: The computer
science department will provide you with an account on our systems
for this purpose.
Your LDAP authentication credentials for this account will
enable you to log in to the private class materials page.
Thus, this account is essential. Such an account
will be automatically provided
to all students enrolled in SIS who do not already have one.
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.
Any code you write for your homework will be graded based on its ability to run on the machine homework.cs.tufts.edu. Please test your code there; just because it works on your laptop does not mean it will work on a different machine or platform.
- Gradescope: You will also need a Gradescope account to submit your work and receive feedback on it. The code for signing up is available on the private class materials page.
- Sococo: The class zoom link, office hours, and collaboration spaces
can also be reached in Sococo, the
online platform that the Computer Science Department has been using during the pandemic.
The web version only runs on Chrome, but you can download the Sococo app for use on most platforms.
Go to the above link or the app to get to the login screen,
click on "More ways to log in", and then choose "Unified
login (SAML)" - then log in with your Tufts UTLN and password and two-factor authentication.
You should be able to create an account this way - if it doesn't work, let us know and we
can create one for you.
You can access the class and office hours in Sococo even without the relevant zoom link.
Basic onboarding instructions for Sococo are available here.
- Piazza: Finally, there is a class Piazza site linked here that you are welcome to use to ask questions and discuss topics with your classmates and the course staff. We created it in response to student requests, and midterm evaluations from prior courses/terms suggest that using it more is something the students felt would help them get more out of the class. Please take advantage of this resource. You will get faster answers to your questions if you ask the entire group of students and staff at once than if you just email one of us individually.
- We will *not* be using Canvas this semester.
A note on privacy: This semester, we expect class attendance to be more variable than usual. Some students may even be in other time zones. Accordingly, please understand that we intend to record class meetings on Zoom and share them via the private course materials page. Links to the class materials will only be available to those with CS department accounts (this will include auditors with the approval of course staff). Office hours will not be recorded unless you are informed otherwise in particular cases (e.g. a review session on a particular topic that students have asked us to record). Please talk to course staff if you have concerns about this.
Grading: Grades will be based on five homework assignments, which will include both written and programming components (60%), five in-class quizzes (32%; the lowest grade will be dropped), and course participation (8%), which will include participating in in-class exercises, discussion, journal clubs, and contributing on Piazza.
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 in this class this semester. You will have 10 tokens for the term. You may use up to 3 tokens per assignment; each token gets you an extra day (24 hours as counted by Gradescope). 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 for partial credit. Turning work in on time is important both for consistency in grading, and because it allows us to discuss homeworks in class in a timely fashion.
As usual, in the case of 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 like 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 Services (SAS) office at Accessibility@tufts.edu or 617-627-4539 to make an appointment with an SAS representative to determine appropriate accommodations. Please be aware that accommodations cannot be enacted retroactively, making timeliness a critical aspect for their provision.
You can find more information on Tufts accessibility policies and procedures here.
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.
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.
Please be aware that if Tufts faculty find evidence of academic misconduct, we are required to report it to the university.
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 else - whether published online or previously or concurrently submitted at Tufts.
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.) 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 typos. 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.
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.
|Mon., Feb. 1|| 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 Cormen, Leiserson, Rivest and Stein Chapters 2 + 3, or Jones and Pevzner, Chapter 2: Bio O notation, NP-completeness.
|Weds., Feb. 3|| Sequence alignment:
Global alignment. Dynamic programming.
|ZB: Sections 4.2, 4.5 (pp. 87-89 only); 5.2|| Global alignment: Durbin, pp. 17-22.
Local alignment: Durbin, pp. 23-24, 29-30
|Mon., Feb. 8||
Finish global alignment. Local alignment. Scoring schemes.
Hwk 1 out
|ZB: Sections 4.2, 4.5 (pp. 87-89 only); 5.2|| Global alignment: Durbin, pp. 17-22.
Local alignment: Durbin, pp. 23-24, 29-30
|Weds. Feb. 10||Sequence alignment: gaps, scoring matrices, PAM and BLOSUM||ZB: Sections 4.3, 4.4, 5.1|
|Fri., Feb. 12||Hwk 1 part 1 due|
|Mon., Feb. 15||NO CLASS (Holiday)|
| TUES., Feb. 16
| DB search, BLAST, Significance of alignment scores
|ZB: 4.6, 4.7, 5.4||Altschul's tutorial on statistics of sequence similarity scores. Altschul's slides on information theory, scoring matrices, and E-values.|
|Weds., Feb. 17||Compressive BLAST (journal club)||Compressive BLAST paper|
|Fri., Feb. 19||Hwk 1 part 2 due|
|Mon., Feb. 22||DNA motifs, profiles.||ZB: 6.1, 6.6|| Original
paper on the Gibbs sampler for local multiple alignment
Original paper on MEME algorithm
|Weds., Feb. 24||
Gibbs sampling. Iterative search methods. Multiple sequence alignment: scoring, optimal methods, star alignment
Hwk 2 out
|Ron Shamir's MSA notes, ZB: 4.5 (pp. 90-93)||Durbin, 6.1--6.4|
|Mon., Mar. 1||Multiple sequence alignment: iterative and progressive methods||ZB: 6.4-6.5|
|Tues., Mar. 2||Hwk 2 part 1 due|
|Weds, Mar. 3|| Sequence assembly: Introduction. deBruijn graphs and Eulerian paths.
|GAGE: Evaluating short-read assemblies|
|Fri., Mar. 5||Hwk 2 part 2 due|
|Mon., Mar. 8||deBruijn graphs, Eulerian paths||ZB: 5.3(pp. 141-3)||Dan Gusfield's introduction to suffix trees|
|Weds., Mar. 10||OLC Sequence assembly (journal club)||ARACHNE paper on overlap-based whole genome assembly; supplemental methods text on k-mer sorting|| Schuster's review article on sequencing methods;
Mardis' more detailed article about
"next generation" sequencing technologies.
The paper about the SOAPdenovo assembler.
|Mon., Mar. 15||Overlap graphs, Hamiltonian paths. Suffix trees for overlap detection.
Hwk 3 out
|ZB: 5.3(pp. 141-3)||Dan Gusfield's introduction to suffix trees|
|Weds., Mar. 17||Gene finding and intro to Hidden Markov Models (HMMs)||ZB: 9.2-9.7||Rabiner handout, pp. 257-266.|
|Fri., Mar. 19||Hwk 3 part 1 due|
|Mon., Mar. 22|| Hidden Markov Models (HMMs)
|Rabiner handout, pp. 257-266.|
|Weds., Mar. 24||NO CLASS: break day|
|Fri., Mar. 26||Hwk 3 part 2 due|
|Mon., Mar. 29||Hidden Markov Models;||Durbin: chapter 3|
|Weds., Mar. 31||Finish Hidden Markov Models; EM algorithms. HMM uses in gene finding.||ZB: 10.2- 10.8 ; short paper on EM algorithms|
|Mon., Apr. 5|| Gene expression: technology, normalization, detecting differential expression
Hwk 4 out
|ZB: 15.1, 16.1, 16.4||Slonim review article|
|Weds., Apr. 7||Gene expression: clustering and classification.||ZB: 16.2-16.3, 16.5|| Golub and Slonim et al., on
leukemia classification, |
|Fri., Apr. 9||Hwk 4 part 1 due|
|Mon., Apr. 12||Functional interpretation: gene set analysis, Gene Ontology, functional enrichment. (journal club)||Gene Set Enrichment Analysis|
|Weds., Apr. 14||Introduction to phylogeny.
|ZB: 7.1, 7.3||Mona Singh's phylogeny notes|
|Fri., Apr. 16||Hwk 4 part 2 due|
|Mon., Apr. 19||NO CLASS: Patriots' Day|
|Weds., Apr. 21|| Phylogeny
Hwk 5 out
|Mon., Apr. 26|| Bioinformatics ethics discussion|
Hwk 5 part 1 due
|Weds., Apr. 28|| Bioinformatics ethics discussion
|Mon., May 3|| anomaly detection for precision medicine
Hwk 5 part 2 due
|Noto, et al., 2015 on anomaly detection||Pietras, et al., 2020 on temporal anomaly detection|