Why we offer nanocourses
Advances in biomedical technologies necessitate data-intensive assays and sophisticated computational infrastructure for data storage, processing, presentation and interpretation. Progress in disease diagnoses and treatments now depend on correlative analyses of multi-modal data. The Bioinformatics Core Facility’s mission is to raise awareness and competence in data analysis issues. The Bioinformatics Nanocourse Series helps to fulfill this mission.
The basic nanocourse format was introduced by Bentley and Stanford at Harvard Medical School as a format that is “…well suited for: rapid course development; educating those with limited time to devote to formal classroom experiences; and enhancing the existing curriculum with integrative, supplemental, or novel course topics.” We have adapted their model to the bioinformatics and UTSW contexts. Topics will be chosen each year as a reflection of BICF client needs, new or trending topics in the field, and feedback from across campus.
What to expect
Each nanocourse runs for one to two days, eight hours per day, and combines didactic lectures on topical content with hands-on workshop sessions facilitated by experienced bioinformatics faculty, industry experts and BICF personnel. Participation in the workshop sessions is limited in order to provide personalized training for each participant. Due to the small class size, participation is competitive. Registration dates will be announced in advance and registrants will be admitted based on instructor-determined match between course content and participant level of experience and research need. For example, an individual who registers for a new R users course but who has some R experience may be asked to attend the novice R users course instead.
Participants will be asked to bring their own laptop and may be required to download some materials in advance. Lunch is not provided but is available at nearby campus locations. Prior to the course participants other than faculty will be asked to submit a supervisor endorsement form, acknowledging PI support for the individual to be away from their lab for those specific dates and the entire duration of the course.
Who should attend
The nanocourse series is open to all students, postdocs, research assistants, faculty and other personnel at UT Southwestern. We will also consider registrations from individuals at other institutions on a limited basis. Acceptance into nanocourses is competitive and decisions will be based on answers to questions on the registration form. Academic credit may be available to students through the Graduate School and Postdoctoral Affairs offices.
How to register
Please see the Upcoming Nanocourses section below for more information. To be added to our email notification list, please contact Rebekah Craig.
Introduction to R for Beginners, Level II
Two consecutive Thursdays, January 11th & 18th, 2018
9:00 am – 5:00 pm both days, Room NB2.100A
Have you already taken the R for Beginner 1 and now want to build up your skills? Do you want to create interactive plots or perform complicated genomics analsysis with Bioconductor? Do you understand dataframes, matries and vectors but need more practice on more sophisticaled analysis? In this continuation of the R Beginner 1 Nanocourse, we will review what you learned in R Beginner Level 1 and demonstrate how to merge and search excel sheets, create small scripts for repetitive tasks, generate interactive plots and use bioinformatics packages from Bioconductor. Students will also have a chance to present their own data challenges and come up with analysis strategies.
You will need to bring a laptop computer with the latest version of R-Studio installed. If you already have R-studio, please make sure you have the latest version of R installed.
Course Size: 15 students
Academic Credit: 1 credit hour special topics
Registration for this course is closed. Please contact Rebekah Craig if you would like to be notified of future training opportunities.
2018 Calendar Year
For the remainder of the 2018 calendar year the below topics will be offered as nanocourses. Full details and course descriptions will be announced soon.
Please contact Rebekah Craig if you would like to be notified when course registration opens.
February 27th & 28th: Python I
March 8th & 9th: Machine Learning I
March 28th & 29th: NCBI Workshop presented by NIH
May 23rd & 24th: Human Variation Bootcamp
June 8th & 15th: Gene Expression and Regulation
July 12th & 19th: Introduction to R for Beginners, Level 1
September: Computational Image Analysis
October: Python I
December: Machine Learning I
January 31 - February 1, 2017, 9:00 a.m. - 5:00 p.m. both days
Have you already used R, but want to learn how to create simple scripts, perform statistical analysis with NGS data like RNASeq? Do you want to create interactive figures for your next paper? R is a freely available language and programming environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc.
This is a two-day training on January 31 - February from 9am to 5pm. You will need to bring a laptop computer with the latest version of R-Studio installed. If you already have R-studio, please make sure you have the latest version of R installed.
- Review of R Concepts
- Introduction to R Scripting
- Basic RNASeq Analysis with R
- Introduction to Web Interfaces with Shiny
Thursday & Friday, April 27 - 28, 2017
9:00 a.m. - 5:00 p.m. both days, Room BL3.212
Do you want to know how to identify genetic biomarkers? Genome-wide association studies (GWAS) use statistical methods to identify trait-associated variants by identifying differences in allele frequency in binary traits represented in two populations (case vs control) or differences in a quantitative trait within a population, such as height.
This class will cover the good, the bad, and the ugly of GWAS analysis, including:
- Study design
- Confounding factors including ethnicity and genetic linkage
- Population structure
- How to determine relatedness between individuals
- How to perform this analysis for your own research
This is a hands-on workshop, which includes working in a Linux environment.
Two consecutive Fridays, July 21st & 28th, 2017
9:00 a.m. - 5:00 p.m. both days, Room NL6.125
Have you already used R, but want to learn how to create simple scripts, perform statistical analysis with NGS data like RNASeq? Do you want to create interactive figures for your next paper? R is a freely available language and programming environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. If you have already taken Intro to R for New Users, there will be an overlap of material with this class.
- Review of R
- Basic RNASeq Analysis with R
- Writing R Scripts
- Building an R Web Application
A four-day course, September 14th - 15th & 18th - 19th, 2017
9:00 a.m. - 5:00 p.m. each day, Room NG3.202
This nanocourse offers an introduction to state-of-the-art computer vision methods to convert image data into quantitative information. The four-day intensive course covers image analysis fundamentals using theory lectures and hands-on computer exercises using popular image analysis programs such as ImageJ, CellProfiler and Matlab. Biomedical scientists will gain the background to (1) search for and evaluate existing image analysis software, and (2) start devising their own image analysis pipeline/software. The course will also include and "image analysis therapy" session where the class can brainstorm about each other's image analysis problems.
The course is open to any interested person at UTSW, provided they utilize imaging and are interested in computational image analysis for their research. Some background in mathematics and programming is a plus, e.g., completion of the Mathematical Foundations of Quantitative Biology course and the Matlab boot camp.
- Image Enhancement & Filtering
- Object Detection & Tracking
- Morphological Operators
- Machine Learning Approaches
This course is also part of the Computational and Systems Biology curriculum.