Spring 2025 Semester

Modality: Hybrid, but in-person encouraged

Course Closes: February 28

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:

Dhivya Arasappan has 15 years experience analyzing NGS data from multiple platforms: Illumina, PacBio and SOLiD. Her areas of expertise include: RNA-Seq analysis, de novo genome assembly, particularly using hybrid sequencing data, 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

Course Closes: March 7

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: in person

Course Closes: March 19

Description:

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.

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, studying mammalian embryogenesis before joining the Texas A&M Institute for Genomic Medicine (TIGM) to help lead TIGM’s 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.

Modality: Hybrid, but in-person encouraged

Course Closes: March 21

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: RNA-Seq analysis, de novo genome assembly, particularly using hybrid sequencing data, 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: March 28

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, model performance metrics such as AUC, 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, but in-person encouraged

Course Closes: April 1

Description:

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.

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

Course Closes: April 2

Description:

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.

Instructor Bio:

Jessica Podnar joined the GSAF in 2010, prior to UT she worked at a biotech company focused on drug discovery and telomere biology. She has 15 years experience in cell and molecular biology and has been part of the rapidly changing field of Next Generation Sequencing for most of her career. She is familiar with an assortment of NGS technologies and is always ready to support new projects.

Modality: Hybrid, but in-person encouraged

Course Closes: April 4

Description:

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.

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, but in person encouraged.

Course Closes: May 5

Description:

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.

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.

Evan graduated with his Ph.D. in biochemistry from Dr. David Taylor’s lab at UT Austin in May of 2022. He used cryo-electron microscopy to study the biochemical mechanisms of CRISPR complexes to understand their evolution. Evan began working in the Sauer Structural Biology Laboratory in October 2022 and has over 8 years of experience in electron microscopy, mainly focusing on structures of biological proteins.

Michelle worked as a lab manager with more than 25 years of research experience before joining the CBRS in 2019. Since then, she has enjoyed assisting students and researchers with preparing and imaging samples from microbiology to materials engineering and looks forward to expanding her work with cryoEM.

Modality: Hybrid

Course Closes: April 9

Description:

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.

Instructor Bio:

Before becoming director of the Microscopy and Flow Cytometry Facility in 2021, Anna Webb was the light microscopy specialist at the MFC for 5 years and has worked in light microscopy support in core facilities for 15 years.

Modality: Hybrid

Course Closes: April 11

Description:

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.

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: In-person. Zoom access available upon request.

Course Closes: April 21

Description:

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.

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 IT and 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.

Prerequisites:

Students wishing to participate in the hands-on portions should bring their own laptop.

Modality: Hybrid

Course Closes: April 23

Description:

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.

Instructor Bio:

Richard Salinas has over 20 years’ experience in flow cytometry and multiple cell analyzer and sorting platforms: BD Biosciences, Sony Biotechnology, Cytek Biosciences and Beckman-Coulter. He also has extensive data analysis experience in using FlowJo and FCS Express software.

Modality: In-person. Zoom access available upon request.

Course Closes: April 28

Description:

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.

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 IT and 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.

Prerequisites:

Introduction to Unix or equivalent experience. Students wishing to participate in the hands-on portions should bring their own laptop.

Modality: Hybrid

Course Closes: April 30

Description:

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.

Instructor Bio:

Quinn Lee has over 8 years of experience with advanced microscopy techniques. She joined the Microscopy and Flow Cytometry Facility as Light Microscopy Specialist after earning her PhD at the University of Illinois at Chicago.

Modality: In-person. Zoom access available upon request.

Course Closes: May 5

Description:

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.

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 IT and 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.

Prerequisites:

Intermediate Unix or equivalent experience. Students wishing to participate in the hands-on portions should bring their own laptop.