Spring 2025
Programming for Beginners using MATLAB
February 5 & 6, 2025
9 AM to 5 PM both days
G9.250A
This course is useful for individuals with an interest in learning the most elementary steps in software programming. The course will use MATLAB as the programming platform, but the coding elements taught are fully agnostic to the programming language. The goal of the course is not to teach MATLAB, but to break down for the novice the mystery of coding and to illustrate the basic thinking behind structuring a set of instructions to produce something intelligible. Students will learn how to write and read simple codes and how to evaluate the progression of a program sequence, both numerically as well as through graphical representations of intermediate and final results. As a final project, students will have a choice of programming a classic algorithm for data clustering or a classic algorithm for the simulation of biochemical reactions.
Day 1: Elementary set of commands (ops on arrays/matrices; loops; decisions), Programming interface including debugging scripts vs functions; variable name space, Benchmark test: ability to read a piece of code
Day 2: Plotting including dynamic plots, Random number generation Example problem: calculate pi using a randomized 'droplet fall' on circular area, Benchmark tests for programming: k-means or Gillespie algorithms
Prerequisites: Approximately 6 hours of pre-class video tutorials, background reading.
Registration closed.
For UTSW graduate students and PostDocs, academic credit (1 credit hour) is available.
Graduate Students: BME 5096-04 Special Topics - Programming for Beginners (with MATLAB)
PostDocs: PDRT 5095-01 SPECIAL TOPICS IN BIOINFORMATICS - Programming for Beginners using MATLAB
Course Director: Gaudenz Danuser, PhD
Instructors: Hanieh Mazloom-Farsibaf, PhD, Srinivas Kota, PhD
Single Cell Genomics
February 20 & 21, 2025
9 AM to 5 PM both days
G9.250A
This course covers the basics of single-cell technologies and computational analysis methods. We will provide overviews and key algorithms for single-cell RNA-Seq, single-cell ATAC-Seq, and multi-assay data, as well as spatial omics data. This course includes hands-on practice to perform analyses from raw data to quality control, clustering, visualization, trajectory inference, data integration, and cell annotation.
Prerequisites: Proficiency with R and Python.
Registration closed.
For UTSW graduate students and PostDocs, academic credit (1 credit hour) is available.
Graduate students: BME 5096-05 Special Topics: Single Cell Genomics
PostDocs: PDRT 5095-02 SPECIAL TOPICS IN BIOINFORMATICS - Single Cell Genomics
Course Director: Jeon Lee, PhD
Instructors: Jui Wan Loh, PhD, Jingxuan Chen, PhD, Shao-Po (Shawn) Huang
Introduction to Linux
February 24 & 25, 2025
9 AM to 5 PM both days
G9.102
Linux is a robust and versatile operating system favored by programmers and system administrators. Known for its stability and adaptability, it powers devices ranging from smartphones to supercomputers. Linux is particularly popular in academic and scientific fields due to its customizability and extensive suite of integrated tools. This two-day workshop welcomes beginners interested in learning Linux. It will introduce fundamental concepts to get you started on your Linux journey. This workshop lays the groundwork for anyone new to Linux. Those working in research, scientific computing, or computationally demanding fields will particularly benefit from its HPC emphasis.
Key Topics:
Linux philosophy and design principles
System navigation and startup
System configuration tools
Commonly used Linux applications
Command line operations and file management
Text editing and manipulation
User environment customization
Introduction to Bash shell scripting
Local security concepts
Hands-on Sessions:
Navigating the Shell: Master file/directory navigation and core shell commands.
File Manipulation: Become proficient in creating, moving, copying, and removing files and directories.
Pipes & Filters: Harness the power of combining commands for streamlined data processing.
Loops: Automate repetitive tasks for improved efficiency.
Scripting: Discover the potential of shell scripts to extend your Linux capabilities.
Prerequisites: Basic computer literacy. No prior Linux experience is required.
Registration closed.
There is no academic credit for this nanocourse. A certificate will be issued if requested after successful completion.
Course Director: Gaudenz Danuser, PhD
Instructors: TBA
Introduction to Python Software Development on GitHub
March 5 & 6, 2025
9 AM to 5 PM both days
G9.102
In today's world of scientific research and development, the ability to effectively collaborate and develop software as a team is essential. This two-day introductory course is designed specifically for graduate students and postdoctoral researchers seeking to enhance their software development skills in Python and embrace modern continuous integration practices on GitHub. Participants will gain hands-on experience in using Git, pre-commit hooks, unit testing, managing dependencies, and ultimately maintaining stable code through detailed environment requirements. The skills gained in this course will enable participants to work efficiently as part of a team, ensuring the development of high-quality and maintainable software.
Prerequisites: Introduction to Python nanocourse in Fall 2024 or basic literacy/pre-reading about python & GitHub.
Registration closed.
For UTSW graduate students and PostDocs, academic credit (1 credit hour) is available.
Graduate students: BME 5096-06 Special Topics: Introduction to Python Software Development
PostDocs: PDRT 5095-03 SPECIAL TOPICS IN BIOINFORMATICS - Introduction to Python Software Development on GitHub
Course Director: Kevin Dean, PhD
Instructors: Conor McFadden
Multiplexed NGS Assays and Analysis: from FASTQ to fitness
March 10 & 11, 2025
9 AM to 5 PM both days
G9.102
Multiplexed assays of variant effects (MAVEs), e.g., deep mutational scanning, are increasingly becoming the standard from which to quantify phenotypic effects from genotypic perturbations. This includes protein mutagenesis, gene expression regulation, protein-protein interactions, and molecular evolution. In light of the expanding application of machine learning towards modeling these interactions, it is important to be able to design, implement, and analyze these experiments that can produce large volumes of high-quality data. In this course, students will be 1) given an overview of how to plan and implement a massively parallel reporter assay (MPRA), 2) shown how to understand the results of an Illumina based NGS run in the context of a deep mutational scanning experiment, and 3) build a pipeline to process these raw results into variant fitness and error estimates using either their own data, or provided examples. Students do not need to have an existing MAVE experiment or a strong background in coding.
Prerequisites: None; students do not need to have an existing MAVE experiment or a strong background in coding.
Registration closed.
For UTSW graduate students and PostDocs, academic credit (1 credit hour) is available.
Graduate students: BME 5096-07 Special Topics: Multiplexed NGS Assays & Analysis
PostDocs: PDRT 5095-04 SPECIAL TOPICS IN BIOINFORMATICS - Multiplexed NGS assays and analysis: from fastq to fitness
Course Director: Kimberly Reynolds, PhD
Instructors: Jerry Dinan, Philip M. Brown
Introduction to Computational Neuroscience
April 1 and 3, 2025
9 AM to 5 PM both days
G9.250A
This nanocourse provides an introduction to the computational neuroscience. Topics cover single-neuron models, neural circuit models, neural coding theories, and perceptual computations. We will go through the concept and math of these models followed by coding exercise.
Prerequisites: Calculus and linear algebra, MATLAB/Python.
Registration closed.
For UTSW graduate students and PostDocs, academic credit (1 credit hour) is available.
Graduate students: BME 5096-08 Special Topics: Introduction to Computational Neuroscience
PostDocs: PDRT 5095-05 SPECIAL TOPICS IN BIOINFORMATICS - Introduction to Computational Neuroscience
Course Director: Wenhao Zhang, PhD
Instructors: Eryn Sale, Micheal Myers Jr.
Neuroimaging & MRI: Processing & Analysis of Brain Data
April 4, 2025
9 AM to 5 PM
G9.250A
This nanocourse provides a comprehensive introduction to the processing and analysis of brain MRI data, with applications in neuroscience and biomedical research. Participants will explore key techniques for handling, preprocessing, and analyzing structural, diffusion weighted, and functional brain MRI datasets. The course will emphasize practical challenges in neuroimaging studies and demonstrate cutting-edge tools used in brain MRI research. Hands-on sessions will allow participants to work directly with brain MRI data, utilizing popular software like FSL, FreeSurfer, SPM, and ANTS.
Prerequisites: Literacy in computational image analysis and software.
Registration closed.
There is no academic credit for this nanocourse. A certificate will be issued if requested after successful completion.
Course Director: Jeon Lee, PhD
Instructors: Ahmed Shalaby, PhD, Krishna Kanth Chitta, MS
Introduction to Population Genetics
This course has been canceled.
This two-day course aims to introduce fundamental concepts of evolutionary theory and population genetics, including fitness, function, selection, and fixation. Topics covered will include Darwinian evolution, the Fisher-Wright model, Kimura's neutral drift theory, Muller's ratchet, the Haldane-Muller problem, and the Moran process.
Prerequisites: Basic math skills (at a high school level).
Theory of Variational Methods
April 16 and 18, 2025
10 AM to 2 PM both days
G9.102
This nanocourse provides the theoretical foundations for variational methods, which have a rich parallel history in the fields of statistical physics and statistical inference. We will cover the concept of the variational free energy, and show how the mean field theory of spin systems and expectation maximization algorithm are both instances of the same variational optimization framework. We then use this formalism to understand the workings of a variational autoencoder, a powerful class of generative machine learning models. The course will emphasize mathematical proofs and intuitive understanding of key concepts.
Prerequisites: Literacy in basic machine learning and math.
Registration closed.
There is no academic credit for this nanocourse. A certificate will be issued if requested after successful completion.
Course Director: Milo Lin, PhD
LLMs in Action: A Practical Introduction
April 22 & 23, 2025
9 AM to 5 PM both days
G9.102
This is a two-day intensive course exploring the world of Large Language Models (LLMs) in healthcare and biomedical research. Designed for beginners, this hands-on program covers LLM fundamentals, practical applications, ethical considerations, and the landscape of different LLM options. Participants will learn to leverage LLMs for a range of tasks (e.g., document analysis, assessment, clinical decision support). The course will delve into comparing various foundation models, discussing the pros and cons of open-source versus proprietary LLMs, and guiding participants in choosing the right tools for their needs. Through interactive sessions, attendees will develop the skills to effectively and responsibly use LLMs in their respective fields.
Prerequisites: Literacy in basics of machine learning.
Registration closed.
For UTSW graduate students and PostDocs, academic credit (1 credit hour) is available.
Graduate students: BME 5096-10 Special Topics: LLMs in Action - A practical introduction
PostDocs: PDRT 5095-07 SPECIAL TOPICS IN BIOINFORMATICS - LLMs in Action: A Practical Introduction
Course Director: Andrew Jamieson, PhD
Instructors: Michael Holcomb, MS, David Hein, MS, Ameer Hamza Shakur, PhD, Shinyoung Kang