Syllabus for CMSC423: Bioinformatics Algorithms, Databases, and Tools

Here you’ll find an overview of the course — the material I expect we’ll cover, the breakdown of course assignments and credit, and the course policies.

Logistics

  • Course Website : https://umd-cmsc423.github.io/f2025/
  • Instructor : Rob Patro
  • Instructor office hours: Thurs. 12:30 - 1:30 PM (IRB 3220)
  • Class location: IRB 2207
  • Class days/time: Tuesdays/Thursdays 11:00 AM — 12:15 PM
  • TA :
    • Rachel Parsons (rparsons@umd.edu)
      • Office Hours: Thurs. 3 - 4 PM (AVW 4140)

Note: In any e-mail correspondence related to the course, please be sure to include the string “[CMSC423-F25]” in the e-mail subject, to ensure that the e-mail receives appropropriate priority and doesn’t get routed to SPAM.

Course Content

Links:

  • In general, this website is the place to look for course content and course news. Any content that is private / restricted (e.g. grades) will be made available on the ELMS page for this course.
  • Assignment announcements and deadlines will be posted on the Gradescope page for this course, and grading information will be made available there.
  • The course also has a Piazza page for discussions. The Piazza page will be used actively this semester, and is the most relevant location for asking and finding answers for questions related to specific course content, including the projects and exams. Please register for this course on Piazza here.

Textbook(s): Based on previous feedback in the course, there is no required textbook for the class. Resources, where relevant, will be provided via links on the course website accompanying the slides or lecture notes. However, this is an upper-level course, and you should absolutely seek out other sources explaining these topics from different angles, using different notations and examples, etc. Of course, you should also absolutely reach out to the TA and me if you are having trouble understanding a topic in the course and have been unable to become comfortable with it from the lecture slides and other sources. Here are some (non-required) textbooks that I personally recommend as references for different topics:

Genomics algorithms, data structures, and statisitcal models:

Basics of algorithms and data structures:

This course will assume familiarity with basic algorithms and data structures, though I will attempt to refresh everyone’s memory on relevant concepts when we cover them. If you need a refresher on algorithmic basics, I recommend the following resources:

Molecular biology:

We will cover the basic required molecular Biology in the course. However if you’re not familiar with basic molecular Biology, there are some useful resources worth reading:

Software development and the command line

While you should have had, in the prerequisites for this course, exposure to the relevant tools (e.g. how to properly create a tarball, how to invoke the compiler from the command line / a script), I realize it may have been a while since you have used these skills. If you feel you need a refresher on these topics, I would strongly suggest checking out “The missing semester” (an MIT course dedicated to these various miscellaneous topics).

Expectations: Since this is a computational biology course, you will be expected to become familiar with the relevant biology — it is an important and inextricable part of the material, and the underlying biology provides motivation for the computational problems we will tackle. However, as an upper-level computer science course, our focus will be on the computational aspects of bioinformatics and genomics. It is expected that you enter the class with a strong understanding of algorithms and basic data structures, and that you leave the class with a knowledge of how algorithm and data structure design can be fruitfully applied to biological data. Additionally, this course makes use of programming assignments to help reinforce material we cover during lectures and to help you understand certain concepts in even more depth (by implementing them). As such, programming is an important part of this course, and dynamic languages (e.g. Python) are ill-suited for many of the problems we will tackle. We attempt to provide maximal freedom in the languages you can use for the assignemnts in the class, but we will require the assignments to be completed in a compiled language. The submission server supports, Rust, C, C++, Java, and Go — we will consider reasonable requests for other compiled languages as well.

Course Objectives

The main objective of this course will be to provide an understanding of some of the algorithms, data structures, and methods that underlie modern computational genomics. This course is intended as a broad introduction to bioinformatics and computational biology. However, this is a huge field, so we will not cover everything, and what we do cover will not all be at the same depth (e.g. we will spend more time discussing indexing than clustering). Our perspective will be a computational and algorithmic one, though we will take the time to understand the necessary biology and motivation for the problems we discuss. At the end of this course, you should have a good understanding of how new challenges in genomics drive algorithmic innovations and how algorithmic innovations enable new and improved biological analyses.

Course Resources

The course website is https://umd-cmsc423.github.io/f2025, which is probably where you are reading this right now.

The course has a Piazza page, and you can enroll here. I encourage you to interact with each other, raise questions, and discuss course topics using Piazza. This is also the best place to raise general questions about material we cover in the course (as opposed to e.g. an e-mail), since other students can see the response and ask follow-up questions. This helps to reduce redundancy in the answering of questions.

Programming assignments will be assigned and collected via Gradescope. You should be automatically added via roster sync with Canvas; however, if you find you are unable to access the Gradescope page, please reach out to the course staff to let us know so that we can make sure you are added.

Course Schedule

The following is a planned schedule of the material we will cover in the course, as well as when we will cover it. The mapping between content and dates below is subject to change depending on how quickly we move. The current schedule is optimistic, and I would like to cover all of this material. However, it’s much more important that the class understand the material we cover than that we get to all of the topics I’d like to discuss. Thus, the time we spend on certain topics and the precise list of topics we cover is subject to change throughout the semester depending on our pace.

  • Week of Sept 1.
    • Course introduction, logistics & goals
    • Overview of bioinformatics, basic biology & biotechnology
  • Week of Sept 8.
    • Exact string matching
    • KMP & Z algorithms
  • Week of Sept 15.
    • Exact string matching continued (semi-numeric matching)
  • Week of Sept 22.
    • Text indexing for rapid search (overview & Suffix Trie)
    • Suffix Arrays
  • Week of Sept 29.
    • BWT and FM-index
    • more BWT and FM-index
  • Week of Oct 6.
    • Sequence alignment & dynamic programming
    • Midterm 1 (in class; Thurs. Oct 9th)
  • Week of Oct 13.
    • No class Oct. 14th; Fall break
    • Sequence alignment continued (local and semi-local variants)
  • Week of Oct 20.
    • Guest lecture Oct 21st (Rob attending CZI Open Science meeting)
    • Sequence alignment continued, linear space & bounded edit distance
  • Week of Oct 27.
    • Seeding / chaining and read alignment
    • Spliced alignment & RNA sequencing
  • Week of Nov 3.
    • Phylogenomics parsimony
    • Phylogenomics maximum likelihood
  • Week of Nov 10.
    • Estimating transcript expression
    • Midterm 2 (in class; Thurs. Nov 13th)
  • Week of Nov 17.
    • More estimation of transcript expression
  • Week of Nov 24.
    • Genome sequencing and assembly
    • Nov. 27-th Thanksgiving; no class
  • Dec 1.
    • The de Bruijn graph and compacted de Bruijn graph
    • Other uses of the de Bruijn graph
  • Dec 8.
    • Current problems & research topics in computational biology
    • Wrap-up/review last day of class
  • Dec 15.
    • Final exam on Monday Dec. 15th, 10:30 - 12:30, in the normal class room.

Course Policies

Coursework and grading: The coursework will consist of a number of different programming projects, a midterm exam and a final exam. The breakdown of weights for these different assignments will be as follows:

  • Programming assignments - 50%
  • Midterm 1 - 12%
  • Midterm 2 - 13%
  • Final Exam — 25%

Late policy: Assignments that are turned in late will be docked 1% for each hour they are late up to the first 48 hours. The policy for “rounding” hours is in the student’s favor (i.e., the penalty will be truncated to the nearest complete hour). After 48 hours, late assignments will not be accepted. Each student is allowed one free late assignment turnin (you can turn the assignment in up to 48 hours late with no penalty). However, you must let us know (e-mail the TA) that you are using your free late assignment when you turn in your assignment. The decision to use the free late pass is non-revocable (e.g. if you decide to use the free late assignment for assignment 3, you can’t later request to take the late penalty for 3 and use the late assignment for assignment 4).

Regrade policy: All requests to re-grade, re-check, or re-mark an assignment or exam question must be made in writing. When the assignment is re-graded, it will be re-checked in its entirety. This means that it is possible to lose points on other problems if they were graded incorrectly or too leniently the first time. Therefore, I urge you to thoroughly consider each regrade request you make.

Absences / scheduling accomodations

  • medical reasons: If, for a health-related or medical reason, you will miss two or more consecutive classes, or will miss class on a recurring basis, or were unable to meet a particular academic obligation of this course, I will require a written note from the Student Health Service or a healthcare provider documenting the range of dates for which you were unable to meet your academic obligations. This note need not contain any diagnostic information.

  • non-medical reasons: If you will miss any classes or scheduled exams as a result of religious observances, you must submit this information to me, in writing, within the first two weeks of the semester to make necessary accommodations to complete the work that will be missed. In this course, this applies mostly to notifying me about expected absences from class. The homeworks and projects are assigned far-enough in advance that it is not reasonable that extensions or delays be provided for religious or temporary health-related reasons.

Final Grades: The grade you receive in this class will reflect, as much as possible, the degree to which you have mastered the necessary material. How much somebody “needs” an ‘A’ will have no bearing on whether or not (s)he receives an ‘A’, other than how this need or desire is reflected in the work that (s)he does. I want everyone to do well in this course, and will make every reasonable effort to help you understand the material as well as possible. However, barring errors in the grading of assignments, the grades you receive at the end of the semester are final, and I will not alter them for personal or non-academic reasons, so please do not ask me to!

Academic integrity

TLDR : Don’t cheat. Don’t copy code from friends, classmates, or the internet for the programming assignments. Don’t provide code to classmates for any of the assignments or projects. Don’t cheat on the exams. Be cool, and everything will be cool.

Long form : From the University’s Undergraduate Catalog Statement on Academic Integrity :

The University of Maryland is an academic community. Its fundamental purpose is the pursuit of knowledge. Like all other communities, the University can function properly only if its members adhere to clearly established goals and values. Essential to the fundamental purpose of the University is the commitment to the principles of truth and academic honesty. Accordingly, the Code of Academic Integrity is designed to ensure that the principle of academic honesty is upheld. While all members of the University share this responsibility, the Code of Academic Integrity is designed so that special responsibility for upholding the principle of academic honesty lies with the students.

The University’s Code of Academic Integrity is a nationally recognized honor code, administered by a Student Honor Council. Any of the following acts, when committed by a student, shall constitute academic dishonesty:

  • Cheating: fraud, deceit, or dishonesty in any academic course or exercise in an attempt to gain an unfair advantage and/or intentionally using or attempting to use unauthorized materials, information, or study aids in any academic course or exercise.

  • Fabrication: intentional and unauthorized falsification or invention of any information or citation in any academic course or exercise.

  • Facilitating academic dishonesty: Intentionally or knowingly helping or attempting to help another to violate any provision of the Code of Academic Integrity.

  • Plagiarism: Intentionally or knowingly representing the words or ideas of another as one’s own in any academic course or exercise.

If it is determined that an act of academic dishonesty has occurred, a grade of “XF” is considered the normal sanction for undergraduate students. The grade of “XF” is noted on the academic transcript as failure due to academic dishonesty. Lesser or more severe sanctions may be imposed when there are circumstances to warrant such consideration. Suspension or expulsion from the University may be imposed even for a first offense.

Academic integrity is a very serious issue. Any assignment, project or exam you complete in this course is expected to be your own work. If you are allowed to discuss the details of or work together on an assignment, this will be made explicit. Otherwise, you are expected to complete the work yourself. Plagarism is not just the outright copying of content. If you paraphrase someone else’s thoughts, words, or ideas and you don’t cite your source, this constitues plagraism. It is always much better to turn in an incorrect or incomplete assignment representing your own efforts than to attempt to pass off the work of another as your own. If you are academically dishonest in this course, you will recieve a grade of XF, and you will be reported to the university’s Office of Student Conduct.

Generative AI / ChatGPT Policy : The purpose of the programming assignments in the course is for you to understand and implement the data structures and algorithms we will cover. Therefore, you should not use generative AI (e.g. ChatGPT, Co-pilot, Claude, etc.) to implement any considerable or non-trivial part of your assignment. If you do make use of generative AI for any specific code (e.g. utility functions, file parsing, etc.), you must comment directly in the code the generative AI model you used, and the prompt to which the resulting code was a response. Use of generative AI for core algorithmic or data strcuture components of the project, or failure to disclose the use of generative AI for any part of a project, will constitute a violation of academic integrity with respect to this course.

Accessibility and Disability Service, ADS

Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide, to the instructor in office hours, a letter of accommodation from the Office of Accessibility and Disability Services (ADS) within the first two weeks of the semester. If for some reason ADS accommodation is only granted to a student mid-semester, it applies to coursework from then on, not retroactively. Please note: Students must make accommodated testing reservations 3 business days in advance of the test date.