Fall 2024 Semester

Course Closes: September 18

Modality: In-person

Description:

This is the 1st part of the Introduction to Mass Spectrometry course series. We will cover basic concepts of mass spectrometry, reading mass spectra, mass spec terminology including resolution, exact vs average mass, and mass accuracy, and also cover charge states and charge envelopes.

Instructor Bio:

Andrew is the lab manager and a Research Associate of the Biological Mass Spectrometry Facility. He earned his B.S. in Chemical and Biological Engineering from the University of Colorado Boulder and his M.S. in Pharmaceutical Sciences from the University of Colorado Anschutz Medical School. Andrew has over 5 years of experience in the pharmaceutical industry working as a formulation development and mass spectrometry scientist.

Course Closes: September 25

Modality: In-person

Description:

This is the 2nd part of the Introduction to Mass Spectrometry course series. We will go over the various components of a mass spectrometer including the ionization source, common types of mass analyzer, and the detector.

Instructor Bio:

Aarti Bashyal is a Research Associate of the Biological Mass Spectrometry Facility. She earned a PhD in analytical chemistry under the supervision of Professor Jennifer Brodbelt at The University of Texas at Austin. Her primary research focused on characterizing protein post-translational modifications using ultraviolet photodissociation mass spectrometry. She has over 7 years of hands-on experience in mass spectrometry-based proteomics and is adept in troubleshooting both liquid chromatography and mass spectrometry instruments.

Course Closes: October 2

Modality: In-person

Description:

This is the 3rd part of the Introduction to Mass Spectrometry course series. We will cover tandem mass spectrometry, aka MS/MS, and the primary modes of fragmentation for breaking apart molecules. Fragmentation of peptides will be covered in detail.

Instructor Bio:

Dr. Person is the Director of the Biological Mass Spectrometry Facility and Adjunct Associate Professor in the College of Pharmacy. She has 30 years experience with analytical techniques starting with her Ph.D. at the University of Chicago in physical chemistry. Her 27 years of working in proteomics has included novel research into post-translational modifications and antibody sequencing. She has been director of the facility since 2003, with a focus on acquisition of high-end instrumentation and facilitating research labs in the use of proteomics and metabolomics.

Preferred or Prerequisite Skills:

Introduction to Mass Spectrometry and Mass Spectrometry Instrumentation.

Course Closes: October 1

Modality: Hybrid, but in-person encouraged

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: 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.

Course Closes: October 4

Modality: Hybrid, but in-person encouraged

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:

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.

Course Closes: October 8

Modality: Hybrid, but in-person encouraged

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: 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.

Course Closes: October 14

Modality: Hybrid (In-person or Zoom)

Description:

This course provides a high-level introduction to concepts and best practices for Next Generation Sequencing (NGS) analysis. 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 aim to help you better understand what it takes to bridge the bench-scientist to bioinformatician divide.

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.

Preferred or Prerequisite Skills:

Basic familiarity with DNA and RNA.

Course Closes: October 18

Modality: Hybrid (In-person or Zoom)

Description:

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.

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.

Course Closes: October 25

Modality: In-person

Description:

Mice (and rats) with genetically engineered conditional knockout alleles can be used to dissect a gene’s role in specific cell types and at precise time points in an animal’s development/life. This class will outline recombination systems commonly used and describe the testing of conditional and recombinase strains. It will also describe breeding strategies for producing mice with a tissue-specific mutation and cover detecting and avoiding problems when using recombinase systems.

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 before joining the Texas A&M Institute for Genomic Medicine (TIGM). He led TIGM’s knockout mouse production 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.

Preferred or Prerequisite Skills:

No prerequisites, although an understanding of basic genetics is presumed.

Course Closes: November 4

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

Description:

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.

Preferred or Prerequisite Skills:

Prior exposure to programming language concepts is helpful but not required.

Students can use their own laptops to access coding examples in a shared computing environment.

Course Closes: November 6

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

Description:

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.

Preferred or Prerequisite Skills:

Completion of the Introduction to UNIX CBRS course or equivalent command line experience.

Students can use their own laptops to access coding examples in a shared computing environment.

Course Closes: November 11

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

Description:

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 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.

Preferred or Prerequisite Skills:

This is not an introductory course. Attendees must be comfortable performing basic tasks on the Linux command line. Suggested background is completion of the CBRS Introduction to UNIX and Intermediate UNIX courses, or substantial command-line experience.

Students can use their own laptops to access coding examples in a shared computing environment.

Course Closes: November 12

Modality: In-person

Description:

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.

Instructor Bio:

Alexandra Lukasiewicz is a current Cell and Molecular Biology PhD student in the lab of Dr. Lydia Contreras, with a degree focus 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:

Students are expected to bring their own laptop and are able to connect to the UT WiFi network. As an introductory course, no prior knowledge of R programming is required.

Course Closes: November 24

Modality: Hybrid, but in-person encouraged

Description:

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.

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.

Preferred or Prerequisite Skills:

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.