Training in computational and bioinformatics approaches to biological problems is an important part of the CBRS mission. Each semester, we offer a variety of short courses each semester in diverse topics for learning computational approaches to solving biological problems. Courses are $50.00. All meet for one day, lasting between two to four hours per course.
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● Introduction to PyMol
This course will provide an introduction to the popular 3D molecular visualization software PyMOL. Participants will be led on a hands-on tour of the use and features of PyMOL. Topics that will be covered include the PyMOL layout, mouse controls, visualization presets, taking measurements, representations and selections, scenes and shows, creating movies, and alignments.
ABOUT THE INSTRUCTOR: Dr. Art Monzingo is Director of the Macromolecular Crystallography Facility and has over 30 years experience studying protein molecules, primarily using X-ray crystallography. He has experience as a software developer, as well.
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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.
Students in the course will learn what a cluster is and how to use the world-class clusters available at the Texas Advanced Computing Center (TACC). The course will discuss the basic architecture of the Lonestar 5 and Stampede 2 computing clusters, and how they compare to a regular computer. It will touch on job launchers and job scheduling, and how to submit your own jobs to TACC. Custom tools by the Bioinformatics Consulting Group for job submission will be emphasized. Basic comfort at the UNIX command line is a prerequisite.
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 take to bridge the bench-scientist to bioinformatician divide.
ABOUT THE INSTRUCTOR: Anna Battenhouse is a research scientist in the labs of Drs. Edward Marcotte and Vishy Iyer, 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.
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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 both R and python will be presented. Students should bring their own laptops to the course. Installation of either R (with ggplot2 and pheatmap) or Python (with matplotlib, seaborn, plotline, and pandas).
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 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. 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 8 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.
Please visit our Spring 2019 archive here for past courses.