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.
Explore Upcoming Courses
Short Courses in Bioinformatics & Biocomputing
Big Data in Biology Summer School
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.
Reporting and Validating Genetic Background in Mouse Models for Biomedical Research
This class will address the best practices for reporting and validating the genetic background of mouse models. The genetic background of mouse models can influence the phenotype of the condition being studied and affect data interpretation and reproducibility. Accurate reporting and validation of the genetic background are becoming increasingly important as mouse models are shared among research teams. This class will explore the recent Laboratory Animal Genetic Reporting (LAG-R) framework report (https://doi.org/10.1038/s41467-024-49439-y) and offer practical insights on how to implement the new framework in your lab.
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, model performance metrics such as AUC, etc.) and/or linear algebra will be helpful for getting the most out of this course.
Introduction of Tag Seq (3′ Targeted Sequencing)
Tag Seq is a method of sequencing the 3′ ends of mRNA in order to identify differential gene expression using a significantly cost-effective method. This course is an introduction to this Tag Seq method as well as the bioinformatics involved in analyzing a Tag Seq dataset. We will discuss library prep, quality assessment, read mapping, gene quantification, differential expression analysis and downstream analysis. There are no prerequisites for taking this course.
Introduction to Next Generation Sequencing Services at the GSAF
This course will offer an introduction into the services provided by the Genomic Sequencing and Analysis Facility available to researchers at the University of Texas. The course will cover the platforms and services available, best practices and how to submit samples to the core.
Introduction to Single Cell Data Analysis
This course provides an introduction to the bioinformatics analysis of single-cell RNA-seq (scRNA-seq) data, with a particular focus on methods especially appropriate for analysis of 10X Genomics data. Differences between bulk RNA-seq and scRNA-seq will be discussed in order to develop understanding of both what new methods are required versus what established RNA-seq analysis methods can be retained. A typical workflow for single cell RNA-seq analysis using Seurat will be presented.
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.
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.
Statistical Modeling for Biologists
This course will survey some of the basic principles of statistics crucial to the modeling of biological data. Specific topics will include hypothesis testing, false discovery rate adjustment, and applications and generalizations of linear models.
Introduction to Unix
This is a two-part course, with substantial hands-on practice in a shared computing environment. Participants will learn the basics of using UNIX from the command line. Introductory topics include manipulating text files using standard UNIX utilities, how to string utilities together, and how to output the results to files. The goal of the course is to develop some basic comfort at the command line, get a sense of what’s possible, and learn how to find help.
Introduction to Flow Cytometry Analysis & Cell Sorting
Flow cytometry allows for rapid, simultaneous analysis of multiple parameters of single cells, which can then be sorted into individual populations according to these parameters. In this class, Richard will cover flow cytometry and cell sorting technology, applications of these techniques, best practices for experiments, the use of spectral flow cytometry for complex antibody panels, the fundamentals of performing compensation, and methods for presenting flow cytometry data.
Intermediate Unix
This is a two-part course, with substantial hands-on practice in a shared computing environment. Participants will learn more about using UNIX/Linux from the command line. Topics will build on those in the introductory course, including more on the filesystem, the Bash shell, and text processing. The course will emphasize manipulating text using standard Linux utilities and stringing commands together using pipes. We’ll also introduce some of the powerful Linux utilities such as cut, sort, grep and awk, with the goal of continuing the climb up the steep Linux learning curve.
Introduction to Light Microscopy
Light microscopy allows scientists to visualize and analyze the structure and function of biological and material samples. In this class, Quinn will cover the fundamentals of fluorescence and image resolution; the theory behind a variety of microscopy techniques, including confocal, spinning disk confocal, TIRF, multiphoton, and super-resolution microscopes; applications of these techniques; and best practices for imaging experiments.
Advanced Bash Scripting
This is a two-part course, with substantial hands-on experience in a shared computing environment. The course will cover advanced topics in writing Bash shell scripts, providing tips, examples and best practices for creating robust “pipeline scripts” that execute multiple processing steps. Topics include defining functions, argument processing and defaulting, error checking, effective use of utilities such as awk and grep, as well as subtleties of UNIX streams and text manipulation.
Introduction to R for Biologists
This four-day course will introduce how to use the R programming language to analyze and visualize biological data on small and large scales. We will focus on the practical tools you need to quickly import your data, clean it up, analyze it, and then generate publication-quality plots. Along the way we’ll briefly address best practices for coding in R and how to effectively find help online. The structure of the course is “learn one, see one, do one”–for each topic (e.g., data manipulation or visualization), there will be a brief lecture on the basic principles, then a demonstration of the code in R, and then you will complete a similar problem in a coding worksheet. This course primarily uses the tidyverse ecosystem of R packages, and upon completion you’ll have used dplyr, tidyr, ggplot2, tidygraph, and more.
Preferred or Prerequisite Skills:
No previous programming experience is required.
Computer Requirement:
Students must have their own laptops that are able to connect to the utexas network. Prior installation of R and RStudio is not necessary but will be covered in this course.
Introduction to Biocomputing: from files to functions to plots
This course will cover the Unix command line and data analysis in R within the context of biocomputing. We will start at the Unix command line and cover command line tools for manipulating data files, before transitioning to RStudio to engage with some more complex data analysis methods in R. The course will finish up with tidyverse tools and methods for visualizing data using ggplot2.
Preferred or Prerequisite Skills:
Some general familiarity with a programming language is assumed. Introductory topics in R will be covered, but at a relatively fast pace.
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.
Introduction to Core NGS Concepts and Tools
This five-day course provides an introduction to the concepts and vocabulary of Next Generation Sequencing (NGS) with an emphasis on common protocols, tools and file formats used in NGS data analysis. Subjects covered include quality assessment and manipulation of raw NGS sequences (FastQC, cutadapt), read mapping (bwa, bowtie2), the Sequence Alignment Map (SAM) format, and tools for manipulating BAM files (samtools, bedtools). Participants will gain hands-on experience using these and other NGS tools in the Linux command line environment at TACC, as well as exposure to the many bioinformatics resources TACC makes available.
Preferred or Prerequisite Skills:
None. UNIX/Linux command line experience is not required, and becoming familiar with how to use the command line for NGS analysis will be a major focus of this course. However, to get a head start on developing this important skill you can register for our Introductory UNIX short courses.
Computer Requirement:
In order to participate fully in the hands-on exercises students should have their own laptop computer with an SSH client program. Macs have SSH available in the Terminal application. Recent Windows versions have an SSH client built into its PowerShell and Command Prompt programs, or PuTTy can be used if SSH is not available. A TACC Account and UT EID are also required. To obtain a UT EID, go here. To sign up for a TACC account, go here.
Introduction to Statistical Modeling
This course is a hands-on introduction to building and interpreting statistical models in R, with a focus on real-world applications. We will cover key concepts in hypothesis testing, multiple linear regression, and logistic regression. You will learn how to choose appropriate modeling approaches, fit models using R, check assumptions, interpret results, and clearly communicate your findings. Each topic will include a brief introduction to foundational concepts, a demonstration of analysis in R, and guided practice through interactive coding exercises. Emphasis will be placed on using statistical modeling to answer research questions within reproducible workflows. By the end of the course, the goal is for you to be able to apply statistical modeling to your own data.
Preferred or Prerequisite Skills:
This course is recommended for students with some prior knowledge of R (in particular, we recommend taking the “Introduction to R for Biologists” summer school course offered above).
Computer Requirement:
Participants are expected to provide their own laptops.