The Center for Computational Biology and Bioinformatics at The University of Texas at Austin is hosting the 3rd Annual Big Data in Biology Summer School May 23–26, 2016.
The 2016 Summer School offers eleven intensive courses that span general programming, high throughput DNA and RNA sequencing analysis, proteomics, and computational modeling. These courses provides a unique hands-on opportunity to acquire valuable skills directly from experts in the field. Each course will meet for three hours a day for four days (either in the morning or in the afternoon) for a total of twelve hours.
Great introductory courses:
- Introduction to Core Next Generation Sequencing (NGS) Tools
- Introduction to Proteomics
- Introduction to Python
- Introduction to RNA-seq
- Bash Beyond Basics
- Genome Variant Analysis
- Machine Learning Methods for Gene Expression Profiling Analysis
- Medical Genomics
- Metagenomic Analysis of Microbial Communities
- Computational Modeling to Study Evolution in Action
- Protein Modeling Using Rosetta
New in 2016:
Bash Beyond Basics: This course will focus on being more productive in the Bash shell. We will learn about regular expressions, Unix utilities like cut/sort/join, awk, advanced piping, process substitution, string manipulation, and Bash scripting. Learn to love the command line and increase your productivity with rapid manipulation of bioinformatic data!
Metagenomic Analysis of Microbial Communities: This course surveys the Python software ecosystem and familiarizes participants with cutting-edge data science tools. Topics include interactive computing basics; data preprocessing and cleaning; exploratory data analysis and visualization; and machine learning and predictive modeling.
Clinical Genomics: This course will introduce a selection of genomics methodologies in a clinical and medical context. We will cover genomics data processing and interpretation, quantitative genetics, association between variants and clinical outcomes, cancer genomics, and the ethics/regulatory considerations of developing medical genomics tools for clinicians. The course will have an optional lab component where participants will have the opportunity to explore datasets and learn basic genomics and clinical data analysis.
Computational Modeling to Study Evolution in Action: This course is about the study of evolution using computational model systems. We will use two different systems for digital evolution: Avida and “Markov Gate Networks” exploring many different possibilities of using computational systems for evolution research. Participants will gain a hands-on introduction to the Avida Digital Evolution Research Platform, a popular artificial life system for biological research and the Markov Gate Network modeling framework to study questions pertaining to neuro-evolution, behavior, and artificial intelligence.
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