The following archive contains previously offered CBRS courses and workshops. These listings reflect our ongoing commitment to providing high-quality training and technical education for researchers.
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Short Courses in Bioinformatics & Biocomputing
Big Data in Biology Summer School
Introduction to Python
This five-day course will introduce students to basic concepts in programming using the Python language, establishing a foundation for scientific computing. Trainees will learn introductory topics such as data structures, control flow, functions, file input/output, and data parsing. The class will work with SciPy libraries like Pandas. Trainees will have full access to the teacher’s course book and course content (datasets, scripts, and jupyter notebooks).
Preferred or Prerequisite Skills:
None
Computer Requirement:
This class is offered in-person. Students must provide laptops able to connect to the internet, and a Firefox or Chrome browser. UT EID is required for wireless access. Please be sure you know your UT EID when you come to class. To obtain a UT EID, go here.
Introduction to RNA-Seq
This five-day course provides an introduction to methods for analysis of RNA-seq data. It assumes familiarity and comfort with Linux command line. A typical RNA-seq workflow will be featured, starting from quality assessment of raw data, mapping (bwa, kallisto), differential expression analysis (DESeq2), and downstream analyses and visualization. The course also describes analysis methods for dealing with single-cell RNA-Seq data. Participants will gain hands-on experience using these tools in a Linux command line environment.
Preferred or Prerequisite Skills:
Familiarity working in a UNIX environment. Consider taking the “Introduction to Biocomputing” or “Introduction to Core NGS Concepts and Tools” summer school course to refresh your UNIX skills.
Computer Requirement:
Students should have their own laptop computer. UT EID is required for wireless access on campus. Please be sure you know your UT EID when you come to class. To obtain a UT EID, go here.
Principles of Machine Learning for Bioinformatics
This four-day course will introduce a selection of machine learning methods used in bioinformatic analyses with a focus on RNA-seq gene expression data. We will cover unsupervised learning, dimensionality reduction and clustering; feature selection and extraction; and supervised learning methods for classification (e.g., random forests, SVM, LDA, kNN, etc.) and regression (with an emphasis on regularization methods appropriate for high-dimensional problems). Participants will have the opportunity to apply these methods as implemented in R and python to publicly available data.
Preferred or Prerequisite Skills:
This course is recommended for students with some prior knowledge of either R or python. Participants are expected to provide their own laptops with recent versions of R and/or python installed. Students will be instructed to download several free software packages (including R packages and python libraries including pandas and sklearn).
Computer Requirement:
Students should have their own laptop computer. UT EID is required for wireless access. Please be sure you know your UT EID when you come to class. To obtain a UT EID, go here.
Introduction to Python
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.
Intermediate Python
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.
Python for Data Science
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.
Python for Machine Learning/AI
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.
Introduction to Next Generation Sequencing
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.
Preferred or Prerequisite Skills:
Basic familiarity with DNA and RNA.
Best Practices in Mouse Colony Management
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.
Preferred or Prerequisite Skills:
No prerequisites, although an understanding of basic genetics is presumed.
Introduction to RNA-seq
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.
Overview of the Biological Mass Spectrometry Facility
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.
Basics of Image Processing for cryo-EM-Part I
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:
- 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
- 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
- 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
- Cryo-EM: Back to Basics (Chapter 1) – A concise introduction to sample preparation, imaging, and data processing fundamentals. Watch on YouTube
- Single-Particle Data Analysis Walkthrough – A step-by-step CryoSPARC workflow: motion correction, CTF estimation, particle picking, classification, and refinement. Watch on YouTube
Basics of Image Processing for cryo-EM-Part II
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.
Introduction to R
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.
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.
Data Visualization using R
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.
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.
Introduction to Python
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.
Intermediate Python
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.
Biomolecular Structure Determination by CryoEM
Transmission Electron Microscopy (TEM) and Cryogenic electron microscopy (cryo-EM)are versatile methods for biological structural characterization. Technological advancements made in the past decade have enabled cryo-EM to become an approachable high-resolution method capable of generating atomic structures.
The course will be divided into three sections: an initial 45-minute section on the room temperature (classical) EM services offered by the core; followed by two sections: an overview of the basic principles of single particle cryo-EM along the related services our core offers users, split into two approximately one hour sections with breaks between all sections. We will also discuss the practical applications of cryoEM and what a user will need to perform a successful experiment.
Python for Machine Learning/AI
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, model performance metrics such as AUC, etc.) and/or linear algebra will be helpful for getting the most out of this course.
Introduction to Microscopy and Flow Cytometry Resources
This course offers an overview of how the Microscopy and Flow Cytometry Facility can help researchers answer their scientific questions. Participants will learn about the state-of-the-art instruments and services available at the MFC, including advanced fluorescence microscopes, flow cytometers, and cell sorters. The course will also cover the types of experiments that can be conducted using these instruments, introduce the facility's expert staff scientists, and provide detailed information on how to access and use the MFC's resources.