Fall 2025 Semester

Modality: Hybrid, but in-person encouraged

Course Closes: October 1

Description:

Python is a simple and popular programming language that can be used across platforms, and is useful for a wide variety of tasks. This short course is a basic introduction to scripting using Python. Skills taught will include data structures, loops, conditional statements, function definitions, and if time permits, file input and output. These tools will be useful for researchers in many fields for data management, automating tedious computational tasks, and handling “big data.” This course is taught at an introductory level and is appropriate for students with no programming experience, but will contain material and techniques helpful to moderately experienced programmers new to Python.

Instructor Bio:

Matt Bramble has recently joined the CBRS team after six years at MD Anderson Cancer Center analyzing a wide range of NGS data in epigenomics. His areas of expertise include: Hi-C (chromatin conformation) analysis, mouse somatic variant analysis, and single cell RNAseq analysis. He has 10 years of experience with R and Python, and Master’s degrees from UT in Molecular Biology and Statistics.

Modality: Hybrid, but in-person encouraged

Course Closes: October 6

Description:

This domain non-specific course is designed for Python programmers who have basic experience with the language. Learners are expected to be familiar with control flow and basic Python data structures (variable assignment, lists, dictionaries). This course will cover the knowledge to make code modular, readable and reproducible. A major focus will be object-oriented programming and Python’s implementation of the object-oriented paradigm.

Instructor Bio:

Matt Bramble has recently joined the CBRS team after six years at MD Anderson Cancer Center analyzing a wide range of NGS data in epigenomics. His areas of expertise include: Hi-C (chromatin conformation) analysis, mouse somatic variant analysis, and single cell RNAseq analysis. He has 10 years of experience with R and Python, and Master’s degrees from UT in Molecular Biology and Statistics.

Modality: Hybrid, but in-person encouraged

Course Closes: October 8

Description:

This course will build up on the concepts covered in the Introduction to Python and Intermediate Python courses. We will introduce the use of Pandas Data frames to read in, subset, analyze and visualize RNA-Seq gene expression data.

Instructor Bio:

Dhivya Arasappan has 15 years experience analyzing NGS data from multiple platforms: Illumina, PacBio and SOLiD. Her areas of expertise include: de novo genome assembly, particularly using hybrid sequencing data, RNA-Seq analysis, exome analysis, and benchmarking of bioinformatics tools. She is the research educator for the Big Data in Biology Freshman Research Initiative stream and teaches an RNA-Seq course as part of the Summer School for Big Data in Biology.

Modality: Hybrid (In-person or Zoom)

Course Closes: October 13

Description:

Building further on the concepts covered in the Introduction, Intermediate, and Data Science Python courses, we will introduce Python as a tool for training and testing machine learning (ML) models with a particular focus on deep learning approaches. Specific topics will include an introduction to the PyTorch software library and a brief survey of some of the basic model architectures which it implements. Some prior familiarity with the basic ideas of ML (underfitting vs. overfitting, use of training and test data sets, etc.) and/or linear algebra will be helpful for getting the most out of this course.

Instructor Bio:

Dennis Wylie joined the Bioinformatics group in 2015. He has experience in NGS data analysis including variant calling and RNA-Seq-based biomarker discovery and predictive modeling (classification, regression, etc.). Prior to UT, he earned a PhD in Biophysics from UC Berkeley applying stochastic simulation methods to problems in immunology, did postdoctoral work modeling the transmission of infectious disease, and spent six years as a bioinformatician in industry.

Modality: Hybrid (In-person or Zoom)

Course Closes: October 15

Description:

This course provides a high-level introduction to concepts and best practices for Next Generation Sequencing (NGS) analysis. Participants will gain familiarity with NGS vocabulary and file formats as well as popular tools commonly used in early processing. We will touch on the main skills and resources you need to get started, and aim to help you better understand what it takes to bridge the bench-scientist-to-bioinformatician divide.

Instructor Bio:

Anna Battenhouse is a research scientist in the lab of Dr. Edward Marcotte, is a Bioinformatics Consultant, and leads the Biomedical Research Computing Facility in its mission to support the computational needs of the UT Austin biomedical research community. She has extensive experience working with NGS data, develops and maintains analysis scripts for the Bioinformatics Consulting Group, and teaches the Introduction to NGS Tools course in the Big Data in Biology Summer School as well as several CBRS short courses.

Preferred or Prerequisite Skills:

Basic familiarity with DNA and RNA.

Modality: In-person

Course Closes: October 20

Description:

The goal of this course is to provide an overview of best practices for maintaining genetically engineered mouse colonies. Topics will include mouse reproductive basics, genetic validation, colony sizing, record keeping, and genetic drift.

Instructor Bio:

Bill Shawlot received his Ph.D. from the Baylor College of Medicine and did his post-doctoral training with Richard Behringer at the M.D. Anderson Cancer Center. He was an Assistant Professor in the Department of Genetics, Cell Biology, and Development at the University of Minnesota before joining the Texas A&M Institute for Genomic Medicine (TIGM). He led TIGM’s knockout mouse production efforts in the International Knockout Mouse Consortium program. He has over 30 years of experience in the transgenic mouse field and serves on the External Advisory Committee for the NIH’s Mutant Mouse Resource and Research Center program.

Preferred or Prerequisite Skills:

No prerequisites, although an understanding of basic genetics is presumed.

Modality: Hybrid (In-person or Zoom)

Course Closes: October 22

Description:

This is a theory course that will introduce some basics (both in experimental design and bioinformatics) that need to be considered when doing an RNA-Seq experiment. We will discuss library prep options, quality assessment, and bioinformatics analysis pipelines. We will also talk about analysis of single-cell and 3′ targeted RNA-Seq data. This course is designed to give you an idea of the options that are available when designing an RNA-Seq study or analyzing an RNA-Seq data set.

Instructor Bio:

Dhivya Arasappan has 15 years experience analyzing NGS data from multiple platforms: Illumina, PacBio and SOLiD. Her areas of expertise include: de novo genome assembly, particularly using hybrid sequencing data, RNA-Seq analysis, exome analysis, and benchmarking of bioinformatics tools. She is the research educator for the Big Data in Biology Freshman Research Initiative stream and teaches an RNA-Seq course as part of the Summer School for Big Data in Biology.

Modality: Hybrid (In-person or Zoom)

Course Closes: October 29

Description:

The class will be an overview of the Biological Mass Spectrometry Facility covering staff, equipment, services, and collaborative opportunities. New equipment for proteomics and metabolomics will be highlighted.

Instructor Bio:

Maria Person obtained her Ph.D. in molecular dynamics and has been working in the field of biological mass spectrometry since 2000, specializing in protein post-translational modifications. She has been the director of the facility since 2003.

Modality: Hybrid, but in-person encouraged

Course Closes: November 3

Description:

This course provides an introduction to important concepts and practical workflows of cryo-electron microscopy (cryo-EM) image processing. Participants will learn the fundamentals of single-particle analysis, including motion correction, CTF estimation, particle picking, 2D classification, 3D reconstruction, and refinement. The course consists of two sessions, with the first session consisting of a lecture, and the second session consisting of a hands-on processing session. Students intending to attend the hands-on session should ensure they have also taken this first lecture.

In order to best prepare for this course, students should learn basic concepts in cryo-EM from a short online course on cryo-EM (e.g. Cryo-EM university), as the course will build on a basic knowledge of concepts presented there.

Secondary recommended video resources:

1. Single Particle Cryo-EM Overview by Yifan Cheng – Explains the principles of single-particle cryo-EM and 3D reconstruction from 2D images. Watch on YouTube
2. Introduction and Cryo-EM Fundamentals (Part 1 of 6) – Covers data collection, motion correction, CTF estimation, particle picking, and 2D/3D classification using CryoSPARC. Watch on YouTube
3. S2C2 Cryo-EM Image Processing Workshop (Full Session) – A nearly 5-hour workshop covering motion correction, CTF estimation, particle picking, 2D classification, and ab initio reconstruction. Watch on YouTube
4. Cryo-EM: Back to Basics (Chapter 1) – A concise introduction to sample preparation, imaging, and data processing fundamentals. Watch on YouTube
5. Single-Particle Data Analysis Walkthrough – A step-by-step CryoSPARC workflow: motion correction, CTF estimation, particle picking, classification, and refinement. Watch on YouTube

Instructor Bio:

Axel Brilot obtained his Ph.D. in Biophysics and Structural Biology developing and applying cryo-EM methods for single-particle reconstruction. He has 15 years’ experience in the cryo-EM field and has been the facility director of the Sauer Structural Biology Laboratory since 2021.

Modality: Hybrid, but in-person encouraged

Course Closes: November 7

Description:

This course provides an introduction to important concepts and practical workflows of cryo-electron microscopy (cryo-EM) image processing. Participants will learn the fundamentals of single-particle analysis, including motion correction, CTF estimation, particle picking, 2D classification, 3D reconstruction, and refinement. This second session consists of a hands-on tutorial in image processing performed in cisTEM. Participants should attend the first lecture prior to attending this course.

Instructor Bio:

Axel Brilot obtained his Ph.D. in Biophysics and Structural Biology developing and applying cryo-EM methods for single-particle reconstruction. He has 15 years’ experience in the cryo-EM field and has been the facility director of the Sauer Structural Biology Laboratory since 2021.

Modality: Hybrid, but in-person encouraged

Course Closes: November 12

Description:

This course will introduce the fundamentals of programming in R. Topics will include coding guidelines, data types, functions, reading/writing files, and data manipulations within dataframes. We will also step into the world of the Tidyverse and learn how to manipulate dataframes within this new paradigm. This course is designed for students with little to no programming experience (prior installation of R is not required). The goal of this course is to become comfortable working in an R environment.

Instructor Bio:

Matt Bramble has recently joined the CBRS team after six years at MD Anderson Cancer Center analyzing a wide range of NGS data in epigenomics. His areas of expertise include: Hi-C (chromatin conformation) analysis, mouse somatic variant analysis, and single cell RNAseq analysis. He has 10 years of experience with R and Python, and Master’s degrees from UT in Molecular Biology and Statistics.

Preferred or Prerequisite Skills:

Students are expected to bring their own laptop and are able to connect to the UT WiFi network. As an introductory course, no prior knowledge of R programming is required.

Modality: Hybrid, but in-person encouraged

Course Closes: November 26

Description:

This course introduces both principles and practice of scientific data visualization, especially as applied to large multivariate data sets. Will cover common methods of visually summarizing data and illustrating relationships between variables of various common types (continuous, categorical, etc.) as well as design concepts for increasing the clarity of quantitative graphical communication. Will introduce modern “grammar of graphics” ideas as foundation for thinking about, relating, and ultimately building new types of informative plots. Implementations of covered methods in R will be presented. Students should bring their own laptops to the course with R and the associated packages dplyr, ggplot2 and pheatmap installed.

Instructor Bio:

Dennis Wylie joined the Bioinformatics group in 2015. He has experience in NGS data analysis including variant calling and RNA-Seq-based biomarker discovery and predictive modeling (classification, regression, etc.). Prior to UT, he earned a PhD in Biophysics from UC Berkeley applying stochastic simulation methods to problems in immunology, did postdoctoral work modeling the transmission of infectious disease, and spent six years as a bioinformatician in industry.

Preferred or Prerequisite Skills:

Some prior knowledge of R is required to get the most out of this class. The “Introduction to R” class would be useful for those not already comfortable with R programming prior to this course.