Registration for this event is now closed.

Dates TBD – postponed indefinitely due to the federal government shutdown.

Cloud computing for bioinformatics and data science. In this workshop, bioinformatics experts from Delaware INBRE and CTR will guide participants through a cloud-based “Consensus Pathway Analysis Workshop*. Participants will gain basic cloud computing skills as they complete hands-on trainings in differential expression analysis, pathway analysis, a meta- analysis, and more. These skills are relevant to biological interpretation of many omics approaches including, RNA-Seq, proteomics, and epigenomics. Participants will walk through one module per week at their own pace. No prerequisite knowledge is required.

The following workshops have materials available online.  Click on the names to access them.  Workshop descriptions are available in the next section.

• Practical AI/ML for Computational Biology & Chemistry – Complete with instructional videos, this workshop is cloud-ready and takes less than $10 to run on AWS, though the materials are available entirely for free.  This workshop is part of the NIGMS Sandbox, a collection of cloud-based learning modules from institutions across the US designed to teach bioinformatics and data science for biomedical and biological resources.
• Python & Bash Boot Camp – An introduction to the command line (Bash) and programming (Python).
• FAIR Data Practices for Omics Analysis – An exploration of the FAIR (Findable, Accessible, Interoperable, Reusable) Data Principles using omics data.

The following workshops have materials available upon request.  Send an email to workshop@bioinformatics.udel.edu for access and files.  Workshop descriptions are available in the next section.

• High Performance Computing & the Linux Command Line Boot Camp – Introduces participants to UD’s high-performance computing (HPC) cluster Biomix.  Features instructional videos.
• R for Bio Data Analysis Boot Camp – Introduces participants to R for data analysis using demographic and biomedical datasets.  Features a blog-style, interactive document (R markdown).  No prior R knowledge required (introductory materials included as part of bundle).

Many CBCB affiliated faculty and students have created open source learning materials:

• Applied Python Programming for Life Scientists: From Fundamentals to Algorithmic Thinking and Data-Driven Discovery (Dr. Ryan Moore) – A beginner-friendly Python textbook for life scientists and biologists. Learn Python programming, build problem-solving skills, develop algorithmic thinking, and apply data-driven methods to research. 
• Transformers Neural Network Workshop (Logan Hallee, PhD Candidate) – Learn about transformers, neural networks, and machine learning.  If you are new to machine learning, you can supplement the workshop with Logan’s Guide to Machine Learning.

There are also a number of reliable, (mostly) free learning resources from other programs:

• NIGMS Sandbox – Aims to teach students, researchers, and clinicians, among others, how to utilize the power of cloud technology for the benefit of life sciences applications and research. Here we present 26 cloud learning modules that represent unique use cases or scientific workflows. Types of data used across the modules include, but are not limited to, genomics, methylomics, transcriptomics, proteomics, and medical imaging data across formats such as FASTA/FASTQ, SAM, BAM, CSV, PNG, and DICOM. Learning modules cover areas from introductory material to single-omics approaches, multi-omics techniques, single-cell analysis, metagenomics, and AI/ML imaging applications.
• The Carpentries – Teaches foundational coding and data science skills to researchers worldwide.  All lessons are available for free.  Data Carpentry features a number of field-specific lessons, ranging from astronomy to ecology.

Materials available upon request | Previously offered: July 2025; January 2025; June 2024

R is a statistical programming language used broadly for data analysis and visualization, including of biological and biomedical datasets. In this workshop, you will learn the basics of importing data and libraries, manipulating data tables, running common statistical tests/analyses, and creating visualizations using common data science packages like ggplot2 and dplyr.  Some basic knowledge of R is encouraged, but not required.

Previously offered: January 2025

Led by Dr. Kristina Holton, an alumna of the Bioinformatics Data Science PhD Program at UD, the workshop will cover the concepts behind single cell approaches and participants will gain hands-on experience analyzing real-world datasets using common analysis pipelines in R.

Materials available upon request | Previously offered: June 2024, June 2023

Biomix provides access to high performance computing (HPC) for Delaware life science research, allowing users to perform analyses that may be impossible or troublesome to run on a personal computer. In this workshop, you will learn to log on to Biomix, navigate using the command line, use the job scheduler SLURM, and run the popular bioinformatics tool BLAST on Biomix.

**Now an NIGMS Sandbox Module!**  This cloud-enabled module is ready to be run anytime, anywhere, and features instructional videos throughout.

Click here for the Sandbox module | Previously offered: June 2022

This workshop will provide participants with a conceptual understanding of various AI/ML approaches, practical applications of AI/ML in computational biology and chemistry, and hands-on exercises that emphasize the importance of data preparation and readiness for AI/ML. We will give participants opportunity with first-hand experiences on the issues in dealing with data that is not well-prepared. We will cover various data formats, processing and wrangling techniques to get the data into a form where it can be utilized by AI/ML algorithms. We will also teach different visualization techniques to better understand the data at hand. Basic Python programming is recommended but not required.

Click here for materials | Previously offered: May 2022

The boot camp covers Bash and Python skills and topics critical to understanding and performing modern data analysis.  While originally meant to prepare participants for the “Practical AI/ML for Computational Biology and Chemistry” Workshop, the materials are designed to be a stand-alone introduction to python and the command line.

Click here for materials | Previously offered: April 2022

This workshop will teach Findable, Accessible, Interoperable, and Reusable data practices while applying them to some steps of omics analysis. Participants will learn strategies and tools for keeping data FAIR through hands-on exercises. Some knowledge of using the unix/linux command line and Git is required.

Previously offered: May 2021; June 2021

The workshop will cover best practices in bioinformatics analysis of amplicon-based microbial community data – using a 16S rRNA data set. Participants will learn to retrieve sequence data from the NCBI Sequence Read Archive (SRA), and will then use a BASH command-line environment on the Biomix computational cluster to perform sequence analysis. R will be used to perform downstream analysis and visualization of those data. Compositional statistical methods will be introduced and used throughout.