About Nanocourses

2019 Spring/Summer Semester

Course Dates
Gene Expression and Regulation June 6th and 13th 2019
Deep Learning for Healthcare Genomics August 9th 2019
Computational Analysis September 2019

Upcoming Courses

Gene Expression and Regulation

June 6th and 13th 2019

9:00 am – 5:00 pm both days, Room NB2.100A

Are you interested in gene expression and regulation? This course is designed to cover NGS sequence analysis to determine: - gene expression (RNASeq) - gene regulation (ChIPSeq and ATACSeq).

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Deep Learning for Healthcare Genomics

August 9th 2019

9:00 am – 5:00 pm Room NB2.100A

This workshop teaches you how to apply deep learning to detect chromosome co-deletion and search for motifs in genomic sequences.

You’ll learn how to: - Understand the basics of convolutional neural networks (CNNs) and how they work - Apply CNNs to MRI scans of low-grade gliomas (LGGs) to determine 1p/19q chromosome co-deletion status - Use the DragoNN toolkit to simulate genomic data and to search for motifs - Upon completion, you’ll be able to: understand how CNNs work, evaluate MRI images using CNNs, and use real regulatory genomic data to research new motifs.

Course Size: 20 students


Computational Image Analysis

September 2019

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 bootcamp.

Time: This is a four-day course that runs from 9 a.m. to 5 p.m. each day

Class Location:

Training Topics:

Course Size: 15 students

Academic Credit: 2 credit hours special topics

This course is also part of the Computational and Systems Biology curriculum


Previous Nanocourses

Python II

April 23rd & 30th 2019

9:00 am – 5:00 pm both days, Room NB2.100A

This nanocourse builds upon the progress of Python I and continues to develop capabilities for for scientific computing and advanced data analysis, including basic machine learning.

Python is an open-source, fun, easy to learn, and powerful programming language. With deep community support and wide ranging deployment across many domains, Python is a worthy tool for projects large and small that any computational scientist should keep on hand.

Topics for this two day (all-day) course will include:

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Genomics Analysis, May 2019

May 1st & 2nd, 2019

9:00 am – 5:00 pm both days, Room NG3.202

Are you interested in genetic variation in cancer or inherited disease? Are you intested in looking for genetic risk loci? This course cover variate detection, annotation and visualization.

Topics covered will include:

Course Size: 15 students

Academic Credit: 1 credit hour special topics


Machine Learning I

February 28 & March 1 2019

9:00 am – 5:00 pm both days, Room NB2.100A (Feb. 28) and NG3.202 (Mar. 1)

Are you interested in machine learning? This course is an introductory course for students to learn the basics. Programming experience in Python is mandatory.

Course Size: 15 students

Academic Credit: 1 credit hour special topics


MATLAB for Scientific Data Exploration

February 26 & 27 2019

9:00 am – 5:00 pm both days, Room NB2.100A

The goal of the MATLAB nanocourse is to instruct the use of MATLAB as tool for scientific data management and exploration, while enabling the deciphering and navigation of complex data structures that are often generated by MATLAB-based software packages. This is NOT a MATLAB programming course, although for data exploration some minimal coding skills will be established.

What will be covered

Course Size: 15 students


Introduction to R for Beginners, Level II

January 2019

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.

Training Topics:

Course Size: 15 students

Academic Credit: 1 credit hour special topic


NCBI Workshops presented by NIH

March 28 2018

Location: NG3.112

NCBI offers a series of modular workshops on related set of NCBI resources. Each module consists of a 3-hour largely hands-on session emphasizing a different set of NCBI resources specifically designed for the UTSW community. Please bring your laptop computer.

Topics will include: - NCBI Tools for NIH Grantees and those wanting to be Grantees - NCBI Resources for Human Clinical/Phenotype Variation Research - NCBI Resources for Clinical Microbiology Research - NCBI Tools for NIH Grantees and those wanting to be Grantees

Course Size: Unlimited, no registration.

Academic Credit: Students who sign in for all sessions can receive 1 credit hour special topics


Introduction to Python I

8:00 am – 5:00 pm each day, Room NG3.202

This nanocourse will introduce Python for scientific computing. Python is an open-source, fun, easy to learn, and powerful programming language. With deep community support and wide ranging deployment across many domains, Python is a worthy tool for projects large and small that any computational scientist should keep on hand.

Topics for this two day (all-day) course will include:

The course will be interactive, with lectures followed by hands-on learning and exercises. No previous programming experience is necessary. Familiarity with basic programming/scripting concepts is helpful.


Introduction to R for Beginners, Level 1

9:00 am – 5:00 pm both days, Room NB2.100A

Do you want to be to do simple statistical analyses yourself? Do you find yourself spending time and effort generating the same plots and statistics for each project? 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 modeling, statistical tests, time series analysis, classification, clustering, etc.

This training is on two consecutive Fridays 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.

Training Topics:

Course Size: 15 students

Academic Credit: 1 credit hour special topics