2025 Summer School

Modality: Hybrid, but in-person encouraged

Course Closes: May 15

Description:

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.

Instructor Bio:

Dr. Alexandra Lukasiewicz is a current post-doctoral researcher in the lab of Dr. Lydia Contreras, with extensive research experience in computational biology and bioinformatics. Their research focuses on biophysical systems modeling of protein-RNA interactions in bacteria. They have 6 years of experience programming in R, Python, and Unix/ Bash, as well as assisting in instruction of introductory programming courses.

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.

Modality: Hybrid

Course Closes: May 22

Description:

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.

Instructor Bio:

Matt Bramble has recently joined the CBRS team after working 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:

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.

Modality: Hybrid, but in-person recommended

Course Closes: May 29

Description:

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.

Instructor Bio:

Anna Battenhouse is a research scientist in the lab of Dr. Edward Marcotte, is a member of UT Austin’s Bioinformatics Consulting Group, and leads the Biomedical Research Computing Facility’s mission to support IT and computational needs of the biological sciences community. She has extensive experience working with NGS data over the last 15 years, and develops and maintains NGS analysis scripts for UT’s BioITeam. Anna received a B.A. in English Literature from Carleton College in 1978. After a long career in commercial software development Anna began her “retirement career” at UT Austin in 2007, and obtained a B.S. in Biochemistry in 2013.

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 by clicking here.

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.

Modality: Hybrid, but in-person encouraged

Course Closes: May 28

Description:

This short 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.

Instructor Bio:

Layla Guyot is a data analyst, educator, and researcher, who joined the department of Statistics and Data Sciences at UT Austin in Fall 2020. She studied mathematics and physics as an undergraduate before completing an M.S. in Applied Probability and Statistics, just by chance. After gaining experience as a biostatistician, she combined her interests in teaching, statistics, and research to complete her Ph.D. in Mathematics Education at Texas State University. Layla has over a decade of experience coding in R and brings that expertise to explore real-world applications, emphasizing hands-on, active learning in her courses.

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.

Modality: In-person

Course Closes: June 4

Description:

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).

Instructor Bio:

James Derry is a senior systems administrator and has taught a semester-long introduction to programming course each long semester for the last 14 years.

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.

Modality: Hybrid, but in-person encouraged

Course Closes: June 4

Description:

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.

Instructor Bio:

Dhivya Arasappan has over 10 years experience analyzing NGS data from multiple platforms. Her areas of expertise include RNA-Seq analysis (specifically involving large-scale brain expression datasets and coexpression network analysis), de novo genome assembly (particularly using hybrid sequencing data) and benchmarking of bioinformatics tools. She is the research educator for the Big Data in Biology Freshman Research Initiative stream.

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.

Modality: Hybrid

Course Closes: June 12

Description:

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.

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 in immunology, did postdoctoral work modeling the transmission of infectious disease, and spent six years as a bioinformatician in industry.

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.