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About Nanocourses

Courses Spring/Summer 2020

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Data Science for Biologists Module Course Number Dates
Introduction to R for Beginners, Level I MB5114 & PDRT5139 January 2020
Introduction to R for Beginners, Level II MB5115 & PDRT5141 January 2020
Accessing Public Data No academic credit February 2020
MATLAB for Scientific Data Exploration MB5113 & PDRT 5166 February 2020
NGS Sequence Analysis Module Expected Dates
Genomics Analysis Spring/Summer 2020
Gene Expression Analysis Spring/Summer 2020
Single Cell Analysis Spring/Summer 2020

Upcoming Courses

Introduction to R for Beginners, Level I and II

January 10th, 17th, 24th, 31st, 2020

  • Jan. 10, 17; 9am to 5pm; NB2.100A
  • Jan. 24; 8am-3pm; NB2.100A
  • Jan. 31; 8am-5pm; NG3.202 (Note: different room)

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.

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:

  • Introduction to R
  • Importing and Cleaning Data with Tidyverse
  • Manipulating and Merging Data
  • Data Visualization with GG Plot
  • Statistical Tests
  • Linear Modeling
  • Scripting and Markdown
  • Bioconductor and Public data (TCGA)

Course Size: 15 students

Academic Credit: 1 credit hour for each course level


Accessing Public Data

February 14th, 2020 (One day only)

9am to 5pm; E2.305 (Library conference room, will require badge entry)

For many types of sequencing analyses, we need access to public data stored in various databases and repositories. This workshop will discuss types of genomic reference data available through public databases such as Ensembl, NCBI, UCSC, ENCODE, and TCGA and step through how to find and download this data. The workshop will also explore how to find and download publicly available experimental data, such as data (FASTQ files and count matrices) from published papers, using GEO and the SRA repositories. While most of the workshop will access data using a web browser, downloading data from the SRA will require beginner knowledge of the command-line interface and TCGA analysis will require beginner R.

Training Topics:

  • TCGA Data Access
  • ENCODE and the UCSC Browswer

Course Size: 15 students

Academic Credit: Not available


MATLAB for Scientific Data Exploration

February 27 & 28, 2020

9:00 am – 5:00 pm, 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

  • Overview of MATLAB as a data exploration tool (user interface, documentation, applications).
  • Navigation of regular data using matrices.
  • Navigation of irregular data in dynamic structures.
  • Design and implementation of data structures for irregular, complex information bases.

Course Size: 15 students

Academic Credit: 1 credit hour


Previous Nanocourses

Gene Expression and Regulation

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).


Deep Learning for Healthcare Genomics

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.


Computational Image Analysis

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:

  • Image Enhancement & Filtering
  • Segmentation
  • Object Detection & Tracking
  • Colocalization
  • Morphological Operators
  • Machine Learning Approaches

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


Python II

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:

  • Numerical arrays/matrices (numpy/scipy)
  • Data structures/DataFrames (pandas)
  • Plotting and Visualizing data (matplotlib, seaborn, bokeh, scikit-learn)
  • Reading and Writing Data Files

Genomics Analysis, May 2019

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:

  • Short Read Alignments, Reference Genomes and Sequencing Technologies.
  • Variation Detection Tools
  • Somatic and Germline Variant Priorization in Cancer, Medelian and Complex Diseases
  • Population Genetics to Identify Risk Loci
  • Immune Profiling using Sequence Data

Machine Learning I

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


NCBI Workshops presented by NIH

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


Introduction to Python I

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:

  • Basic install, setup, and IDEs
  • Basic Syntax
  • Conditional statements, loops, functions
  • Modules, classes, scripting, debugging
  • Numerical arrays/matrices (numpy/scipy)
  • Data structures (pandas)

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.