2015 SUMMER SCHOOL FOR BIG DATA IN BIOLOGY
The next session will take place on May 26-29, 2015. Below is a list of the courses that will be offered. Stay tuned for more details and to register!
- Bacterial genome assembly
- Biological network analyses
- Genome variant analysis
- Intro to RAD-seq
- Intro to RNA-seq pipelines
- Introduction to python
- Machine learning methods for gene expression analysis
- Protein modeling using Rosetta
- Proteomics and metabolomics
- Tag-based RNA-seq and functional analysis of gene expression data
The Center for Computational Biology and Bioinformatics (CCBB) at The University of Texas at Austin is proud to host an annual Summer School for Big Data in Biology. This event is organized in collaboration with the Genome Sequencing and Analysis Facility (GSAF) and the Center for Systems and Synthetic Biology (CSSB) at UT Austin and aims to:
- Provide an exciting intellectual environment in which researchers can pursue excellent training opportunities in "Big Data"
- Give participants hands-on experience in modern computational and bioinformatic approaches to biology using state-of-the-art high performance computing infrastructure
2014 SUMMER SCHOOL FOR BIG DATA IN BIOLOGY
The UT Summer School for Big Data in Biology in 2014 offered 11 intensive, hands-on four-day workshops across diverse topics. The course offerings were designed for students with all levels of experience, from those who are new to computational biology, bioinformatics, genomics, and proteomics all the way to advanced practitioners.
In general, each course meets for four half-days in either the mornings or afternoons, for a total of twelve hours. Participants have access to a course website where instructors post lectures, datasets, exercises, and other useful course information. There are no examinations or tests, and upon request, participants receive a certificate upon completion of each course. Academic credit is not issued.
All courses are held on the UT Campus in the GDC Building.
|MORNING 9 a.m. -12 p.m.||AFTERNOON 1:30 - 4:30|
For more information on course, click course title.
Summer School courses run for 4 x 3 hours, cost $175 per student and are taught by one or more instructors.
REGISTRATION for 2014:
Registration is open Feb. 1 – May 3, 2014
- General: $550 per course
- UT Austin Students: $175 per course
- UT Austin or UT System Faculty & Staff: $275 per course
- Non-UT Austin Students: $225 per course
- Groups: A 20% discount is available for groups of five or more (or groups registering for a total of five classes or more) from the same institution/agency: $440 per person per course. Please email CCBB Administration with an explanation.
All major credit cards accepted. We cannot accept IDT as payment at this time. Thank you.
Affiliations with UT will be confirmed by our staff. Non-UT students must send us a copy of their current student identification. Please email a copy of your ID to CCBB Administration.
*All registrants must have a UT EID to register. This is needed in order to set up computer lab access if necessary for course. Click here to obtain a UT EID.
- Time: 9:00 a.m. - 12:00 p.m.
- Location: GDC 4.302
- Instructor: Benni Goetz, MS (Bioinformatics Consultant, CCBB)
Using common bioinformatics tools effectively requires comfort with a UNIX command line, as well as basic programming skills. Taking advantage of the superclusters at TACC requires some basic understanding of what a computing cluster is. This course offers an introduction to UNIX, TACC, Python, and R basics, which are necessary to use bioinformatics tools on one of the world's most powerful computing systems. The goal of the course will not be making you a command-line Jedi; rather the focus will be on providing a conceptual framework (using real-world examples) to let you explore farther on your own.
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- Time: 1:30 p.m. - 4:30 p.m.
- Location: RLM 7.122
- Instructor: Dr. Oana Lungu (Postdoc, George Georgiou Lab)
This course is intended to give students a strong foundation and working knowledge for understanding computational protein design, a tool of growing importance in protein science. Computational protein design has grown in prominence as successful applications of this tool, such as the design of enzymes that carry out reactions not seen in nature, and the redesign of protein surfaces for enhanced stability, have been published. The course will introduce students to working with the Rosetta suite of software for macromolecular structure prediction and design, the premier software suite for computational protein design. Course topics will include visualizing and assessing protein structures; understanding key concepts of scoring and sampling in computational protein design; using various protein design strategies; loop modeling; and homology modeling.
Prerequisites: Knowledge of general biochemistry, and basic understanding of x-ray crystallography and NMR methods for protein structure determination are prerequisites for this course.
Computer requirements: Familiarity with using Linux or Unix terminals, Pymol, and some coding are strongly encouraged but not required.