- Core Facilities
- Contact Us
We are accepting registrations for Fall term, 2018 (see descriptions below):
Introduction to Unix/Linux and Command-Line Data Analysis (Instructor: Matthew Peterson)
To sign up for workshop or get other information, email email@example.com.
Genotyping By Sequencing I and Genotyping By Sequencing II (Instructor: Andrew Black)
In collaboration with the Departments of Statistics and Molecular and Cellular Biology, the CGRB offers a number of workshops and classes available to both internal and external faculty, staff, postdocs, and students. Generally these are 1- or 2-credit sized (3 or 6 weeks), and are usually offered in academic terms according to the schedule illustrated below. Note that many require some familiarity with either the Unix/Linux command-line or R: if you are unsure about you abilities for any of these, see their descriptions below or feel free to contact the trainers with questions.
Most of these utilize our Advanced Cyberinfrastructure Teaching Facility.
(Note: this schedule is in development, and represents our best estimate for upcoming offerings.)
This module introduces the natural environment of bioinformatics: the Linux command line. Material will cover logging into remote machines, filesystem organization and file manipulation, and installing and using software (including examples such as HMMER, BLAST, and MUSCLE). Finally, we introduce the CGRB research infrastructure (including submitting batch jobs) and concepts for data analysis on the command line with tools such as grep and wc.
The Linux command-line environment has long been used for analyzing text-based and scientific data, and there are a large number of tools pre-installed for data analysis. These can be chained together to form powerful pipelines. Material will cover these and related tools (including grep, sort, awk, sed, etc.) driven by examples of biological data in a problem-solving context that introduces programmatic thinking. This module also covers regular expressions, a useful syntax for matching and substituting string and sequence data.
This module introduces programming concepts, driven by examples of biological data analysis, in the Python programming language. Topics covered will include variables and data types (including strings, integers and floats, dictionaries and lists), control flow (loops, conditionals, and some boolean logic), variable scope and its proper use, basic usage of regular expressions, functions, file input and output, and interacting with the larger Unix/Linux environment. Prior experience with the Unix/Linux command-line is recommended (previously or simultaneously taking Intro to Unix/Linux satisfies).
Part II of the Python series expands on basic programming topics and explores a common concept in modern software development called Object Oriented design, driven again by examples of biological data analysis. Although we will not cover the subtopics of inheritance or public/private variables, we will discuss the use of objects (and their blueprints: classes) in encapsulating functionality into easily used blocks of code that more closely match the biological concepts at hand. Other topics in this area include APIs and syntactic sugar. Finally, we’ll use these ideas to explore creating and using packages such as the BioPython package. Prior experience with the Unix/Linux command-line is recommended (previously or simultaneously taking Intro to Unix/Linux satisfies).
The R programming language is widely used for the analysis of statistical data sets. This course introduces the language from a computer science perspective, covering topics such as basic data types (e.g. integers, numerics, characters, vectors, lists, matrices, and data frames), importing and manipulating data (in particular, vector and data-frame indexing), control flow (loops, conditionals, and functions), and good practices for producing readable, reusable, and efficient R code. We'll also explore functional programming concepts and the powerful data manipulation and visualization packages dplyr and tidyr, and ggplot2.
This is an online version of Data Programming in R. Participants work through readings, videos, and exercises at their own pace, with guidance from an instructor as needed.
This module covers the analysis of data generated by genotyping-by-sequencing (GBS), a restriction-enzyme approach allowing the deep sequencing of many individuals or samples at select regions of the genome. GBS I covers applications of GBS for non-model organisms, focusing on reference-free analysis with the Stacks pipeline. Prior experience with the Unix/Linux command-line and some familiarity with R are recommended (previously or simultaneously taking Intro to Unix/Linux and Data Programming in R satisfies).
This followup to GBS I covers applications of GBS for model organisms, focusing on reference-guided alignment and analysis with the Stacks pipeline. Other topics may include case studies and examples of GBS application. Prior experience with the Unix/Linux command-line and R are recommended (previously or simultaneously taking Intro to Unix/Linux and Data Programming in R satisfies).
The first in a pair on analyzing RNA-seq data covers the development of de-novo transcriptome assemblies. This includes data cleaning and preparation, comparing methods of assembly, filtering of contigs and assessing the quality of output. Other topics include variant detection and annotation. Prior experience with the Unix/Linux command-line is recommended (previously or simultaneously taking Intro to Unix/Linux satisfies).
This second in the pair on analyzing RNA-seq data covers the analysis of differential expression. Topics include data preparation, read mapping, region identification and statistical analysis with R and Bioconductor. Prior experience with the Unix/Linux command-line and R are recommended (previously or simultaneously taking Intro to Unix/Linux and Data Programming in R satisfies).
Description coming soon.
Description coming soon.