BCH394P BCH364C 2026

BCH394P/BCH364C Systems Biology & Bioinformatics

Course unique #: 57450 / 57345
Lectures: Tues/Thurs 9:30 – 11:00 AM WEL 2.246
Instructor: Edward Marcotte, marcotte @ utexas.edu

• Office hours: Mon 4 – 5 PM on the class Zoom channel (available on Canvas)
  

TA: Zoya Ansari, zansari @ utexas.edu

• TA Office hours: Tues 11:30-12:30 / Wed 1-2 in MBB 3.204 / 3.304 (respectively) or by appointment on Zoom
  

Class Canvas site: https://utexas.instructure.com/courses/XXXXX

Lectures & Handouts

Jan 13, 2026 - Introduction

• Today's slides
  
• We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH394P/364C (Spring 2026) Systems Biology/Bioinformatics using this link. Homework #1 (worth 10% of your final course grade) has already been assigned on Rosalind and is due by 10:00PM January 21.
• We'll be using the free academic Anaconda distribution of Python and Jupyter (download here). Note that there are many other options out there, such as Google colab. You're welcome to use those, but we'll restrict our teaching and TA help sessions to Jupyter/Anaconda for simplicity.
• A request for when you turn in code for Rosalind: please just cut the text code out of Jupyter and paste/upload that into Rosalind, rather than uploading the .ipynb files (which are not intended to be human readable)

Here are some online Python resources that you might find useful:

• First and foremost, and very, very useful if you're a complete Python newbie: Eric Matthes's Python Crash Course book. He made some GREAT, free Python command cheat sheets to support the book.
• Practical Python, worth checking out!
• If you have any basic experience at all in other programming languages, Google offered an extremely good, 2-day intro course to Python (albeit version 2) that is now available on Youtube.
• Khan Academy has archived their older intro videos on Python here (again, version 2)
  
• and, of course, you can always ask chatbots like ChatGPT to explain Python code to you, which can be extremely useful.

Syllabus & course outline

Course syllabus

An introduction to systems biology and bioinformatics, emphasizing quantitative analysis of high-throughput biological data, and covering typical data, data analysis, and computer algorithms. Topics will include introductory probability and statistics, basics of Python programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, proteomics, analysis of large-scale gene expression data, data clustering & classification, biological pattern recognition, gene and protein networks, AI/machine learning, and protein 3D structure prediction/design.

Open to graduate students and upper division undergrads (with permission) in natural sciences and engineering. Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds. Undergraduates have additional prerequisites, as listed in the catalog.

Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use these, the focus of the course will be on learning the underlying algorithms, exploratory data analyses, and their applications, esp. in high-throughput biology. By the end of the course, students will know the fundamentals of important algorithms in bioinformatics and systems biology, will be able to design and implement computational studies in biology, and will have performed an element of original computational biology research.

Most of the lectures will be from research articles and slides posted online, with some material from the...
Optional text (for sequence analysis): Biological sequence analysis, by R. Durbin, S. Eddy, A. Krogh, G. Mitchison (Cambridge University Press),

For biologists rusty on their stats, The Cartoon Guide to Statistics (Gonick/Smith) is very good. A reasonable online resource for beginners is Statistics Done Wrong. A truly excellent stats book with a free download is An Introduction to Statistical Learning, by James, Witten, Hastie, Tibshirani, and Taylor, and is accompanied by many supporting Python examples and applications.

Two other online probability & stats references: #1, #2 (which has some lovely visualizations)

No exams will be given. Grades will be based on in class attendance points (randomly assessed throughout the semester and counting 12% of the grade), online homework (counting 18% of the grade), 3 problem sets (given every 2-3 weeks and counting 15% each towards the final grade) and a course project (25% of final grade), which can be collaborative (1-3 students/project). The course project will consist of a research project on a bioinformatics topic chosen by the student (with approval by the instructor) containing an element of independent computational biology research (e.g. calculation, programming, database analysis, etc.). This will be turned in as a link to a web page. The final project is due by 10 PM, April 15, 2026. The last 3 classes will be spent presenting your projects to each other. The presentation will account for 5 (of 25) points of the project grade.

Slides will be posted before class so you can follow along with the material. It is expected that students will attend every class in person. We'll also record the lectures & post the recordings afterward on Canvas so you will have the opportunity to review lectures later. Note that the recordings will only be available on Canvas and are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction could lead to Student Misconduct proceedings. For office hours using the class zoom link, log in to the UT Canvas class page for the zoom link, or, if you are auditing, email the TA and we will send the link by return email.

Online homework will be assigned and evaluated using the free bioinformatics web resource Rosalind.

All projects and homework will be turned in electronically and time-stamped. No makeup work will be given. Instead, all students have 5 days of free “late time” (for the entire semester, NOT per project, and counting weekends/holidays). For projects turned in late, days will be deducted from the 5-day total (or what remains of it) by the number of days late (in 1-day increments, rounding up, i.e. 10 minutes late = 1 day deducted). Once the full 5 days have been used up, assignments will be penalized 10 percent per day late (rounding up), i.e., a 50-point assignment turned in 1.5 days late would be penalized 20%, or 10 points.

Homework, problem sets, and the project total to a possible 100 points. There will be no curving of grades, nor will grades be rounded up. We’ll use the plus/minus grading system, so: A= 92 and above, A-=90 to 91.99, etc. Just for clarity's sake, here are the cutoffs for the grades: 92% = A, 90% = A- < 92%, 88% = B+ < 90%, 82% = B < 88%, 80% = B- < 82%, 78% = C+ < 80%, 72% = C < 78%, 70% = C- < 72%, 68% = D+ < 70%, 62% = D < 68%, 60% = D- < 62%, F < 60%.

Students are welcome to discuss ideas and problems with each other, but all programs, Rosalind homework, problem sets, and written solutions should be performed independently (except for the final collaborative project). Students are expected to follow the UT honor code. Cheating, plagiarism, copying, & reuse of prior homework, projects, or programs from CourseHero, Github, or any other sources are all strictly forbidden and constitute breaches of academic integrity and cause for dismissal with a failing grade, possibly expulsion (UT's academic integrity policy). In particular, no materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may be shared online or with anyone outside of the class unless you have the instructor’s explicit, written permission. Any materials found online (e.g. in CourseHero) that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.

The use of artificial intelligence tools (such as ChatGPT or Github co-pilot) in this class shall be permitted on a limited basis for programming assignments. You are also welcome to seek my prior-approval to use AI writing tools on any assignment. In either instance, AI writing tools should be used with caution and proper citation, as the use of AI should be properly attributed. Using AI writing tools without my permission or authorization, or failing to properly cite AI even where permitted, shall constitute a violation of UT Austin’s Institutional Rules on academic integrity.

Students with disabilities may request appropriate academic accommodations from UT Disability and Access (https://disability.utexas.edu/).

The final project website is due by 10 PM April 15, 2026

• How to make a website for the final project
  • Google Site: https://sites.google.com/new
  • You might also consider streamlit, which lets you generate websites on the fly direct from Python