Fall 2022 Semester

Learn the basics of using UNIX from the command line. Introductory topics include the filesystem, the shell, and text files. The course will touch on 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.

ABOUT THE INSTRUCTOR: Benni is a Bioinformatics Consultant in the Center for Biomedical Research Support. Python, Bash, and huge computing clusters are some of his favorite things. In a previous life Benni studied pure math, differential geometry in particular.

This course provides a high-level introduction to concepts and best practices for next generation sequencing analysis (NGS). 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 hope this course will help you better understand what it takes to bridge the bench-scientist to bioinformatician divide.

ABOUT THE INSTRUCTOR: Anna Battenhouse is a research scientist in the lab of Dr. Edward Marcotte, is a Bioinformatics Consultant, and leads the Biomedical Research Support 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, and teaches the Introduction to NGS Tools course in the Big Data in Biology Summer School as well as several CBRS short courses.

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.

ABOUT THE INSTRUCTOR: Dhivya Arasappan has 11 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.

This 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 awk, grep and sed, as well as subtleties of UNIX stream and text manipulation.

PREREQUISITE: This is not an introductory course. Students must be comfortable performing basic tasks on the Linux command line. Suggested minimum background is completion of the “Intro to UNIX Command Line” short course and/or substantial command-line experience. Students can use their own laptops to access coding examples in a shared computing environment.

ABOUT THE INSTRUCTOR: Anna Battenhouse is a research scientist in the lab of Dr. Edward Marcotte as well as leading the Biomedical Research Support Facility in its mission to support the IT and computational needs of the UT Austin biological sciences community. She has extensive experience writing scripts to process Next Generation Sequencing data and to perform other functions.

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.

ABOUT THE INSTRUCTOR: Benni is a Bioinformatics Consultant in the Center for Biomedical Research Support. Python, Bash, and huge computing clusters are some of his favorite things. In a previous life Benni studied pure math, differential geometry in particular.

This course will introduce the fundamentals of programming in R. Will touch on base R and R Markdown, but will mostly focus on the Tidyverse ecosystem. Topics include data types, functions, coding etiquette, reading/writing files and basic data manipulation. 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 get comfortable working in an R environment.

ABOUT THE INSTRUCTOR: Rachael Cox is a PhD candidate in the Biochemistry PhD program. Previously, Rachael obtained a BS in Chemistry (Texas A&M, 2013) and spent several years designing and building continuous reaction systems at Eastman Chemical Company. Since then, she’s decided big molecules are much more interesting than small molecules, and now studies the evolution of protein complexes in Dr. Edward Marcotte’s lab. Rachael frequently works with multi-omics datasets using Bash, Python, and 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.

PREREQUISITE: 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.

ABOUT THE INSTRUCTOR: 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.

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.

ABOUT THE INSTRUCTOR: Benni is a Bioinformatics Consultant in the Center for Biomedical Research Support. Python, Bash, and huge computing clusters are some of his favorite things. In a previous life Benni studied pure math, differential geometry in particular.