2023 Summer School

Course Closes: May 23

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: Philip Sweet is a post-doctoral researcher in the lab of Dr. Lydia Contreras. Philip received his Ph.D. from the University of Texas at Austin in 2022 in Molecular Biology, with a focus on Bioinformatics. In addition, Philip completed a Portfolio in Applied Statistical Modeling from the Department of Statistics and Data Science. He has 8 years of experience applying computational approaches to biological questions and has assisted with the instruction of multiple R-based courses. In his research on bacterial tolerance of Reactive Oxygen Species (ROS), he primarily utilizes R for data wrangling and data visualization but also has experience with the construction of predictive models.

Preferred or Prerequisite Skills: No previous programming experience is required.

Course Closes: May 31 at 5 pm

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

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.

Course Closes: May 31 at 5 pm

Description: An introduction to the Unix command line and R. Unix basics will include file navigation, pipes, and core utilities. R basics will cover data types, loops, conditionals, and objects. After the basics are covered, the focus will turn to bioinformatics applications. No previous programming experience is assumed.

Instructor Bio: Nolan Bentley is currently a lecturer in the Department of Integrative Biology at UT Austin where he teaches the “Principles of Computational Biology” (BIO321G) course where he focuses on introducing students to R and computational analyses in biology. In his research, he primarily utilizes R, Unix / Bash, and various other programs to do genomic analyses in various agricultural crops to facilitate breeding efforts.

Preferred or Prerequisite Skills: No previous programming experience is assumed.

Computer Requirement: In order to participate fully in the hands-on exercises students should have their own laptop computer with an SSH client program and a web browser for accessing the cloud based RStudio server. 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.

Course Closes: June 2

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. We will work with SciPy libraries like Pandas.

Instructor Bio: James Derry is a Systems Administrator for CNS. He has been teaching researchers how to program in semester-long classes since 2011.

Preferred or Prerequisite Skills: None

Computer Requirement: 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.

Course Closes: June 7

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 and TACC. 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 at TACC.

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 and familiarity with TACC. For short video tutorials on using TACC, visit this page.

Computer Requirement: Students should have their own laptop computer. TACC Account and UT EID are required. Please be sure you know both your UT EID and your TACC username when you come to class. To obtain a UT EID, go here. To sign up for a TACC account, go here.

Course Closes: June 12

Description: This course is designed to teach you how to identify genomic variants in both prokaryotic and eukaryotic organisms from both short (Illumina) and long (Oxford Nanopore) reads from a variety of NGS library sources (mixed populations, whole genome, enriched/targeted panels, rare variant, amplicon, etc.). The course emphasizes using existing data sources to allow participants to analyze real data in the same step-by-step manner that one would analyze their own data. The modular nature of exercises allows participants of all computational skill levels to benefit from both instruction and hands-on practice in areas they are personally most interested in while providing introductory resources to analysis types they may encounter in the future. Additional lecture/discussion will focus on understanding strengths and weaknesses of different sequencing library types, alternative analysis programs, different sequencing platforms, and how to best utilize TACC resources and existing pipelines to make analysis faster. Major data analysis steps include: sequencing quality assessment and improvement, obtaining or constructing reference genomes, read mapping, variant calling, visualization, and reporting. Using programs and pipelines such as: FastQC, MultiQC, filtlong, cutadapt, fastp, SPAdes, Unicycler, flye, minimap2, Bowtie2, SAMtools, bedtools, breseq, IGV, SyRI, and GATK.

Instructor Bio: Daniel Deatherage earned his doctorate at The Ohio State University studying epigenetic effects of ovarian cancer. His postdoctoral work in Dr. Jeffrey Barrick’s lab has focused on using next generation sequencing to identify ultra rare mutations within evolving populations and diagnose synthetic biology construct failure modes. In general, he is interested in using next generation sequencing to answer novel questions that may not be answerable by other methods.

Preferred or Prerequisite Skills: None

Computer Requirement: Students must use their own laptops. TACC Account and UT EID are required. Please be sure you know both your UT EID and your TACC username when you come to class. To obtain a UT EID, go here. To sign up for a TACC account, go here.

Course Closes: June 9

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.