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Exploring Tools for Data Analysis and AI Applications in Biosciences and Genomics
Fall 2025 Workshop: AI for Bioinformatics: Practical Skills for the Modern Researcher
This workshop provides graduate students in public universities with the necessary skills and tools to analyze biological data using high-performance computing resources.
Participants will acquire hands-on experience with industry-standard command-line tools (CLI) for DNA and RNA sequencing analysis, sequence manipulation and alignment, and pipeline management for automating complex workflows. They will also learn about differential expression analysis for identifying genes with altered expression levels, data visualization techniques for effectively presenting results, and the basics of artificial intelligence (AI) and machine learning (ML) in bioinformatics.
Upon completion of this workshop, graduates will be capable of using these powerful tools and methods to address real-world biological challenges and make significant contributions to bioinformatics research.
Required Skills
| Skill | Description |
|---|---|
| Basic understanding of biology | This workshop assumes a basic understanding of biological concepts, such as DNA, RNA, genes, and genomes. |
| Familiarity with the command line (optional, but helpful) | While not required, familiarity with the command line will help navigate the tools covered in the workshop. |
| Enthusiasm for learning new computational skills | A strong interest in learning new computational skills is essential for success in this workshop. |
Workshop Program
- Time: Thursdays @2PM (please register through the [U of A Data Science Institute DataLab website] or [click on this link to fill the form])
- Where: Science & Engineering Library Room 212.
- Zoom link: TBA
All sessions are recorded and uploaded to the University of Arizona's DataLab YouTube channel, where you can also find the other DataLab series: Natural Language Processing (NLP), Generative AI, NextGen Geospatial.
| Date | Session Title | Session Content | Material Link/Recording |
|---|---|---|---|
| 09/04 | AI as Your Research Assistant | Learn how to use publicly available AI as your research assistant: brainstorming an experiment, furthering your hypotheses, aiding with literature reviews, identifying potential insights, and helping with code and computation. This session will focus on using ChatGPT (or your favourite AI) as writing partners and productivity aids — no programming required. You’ll leave with practical prompt templates and strategies to save time and communicate more clearly. | TBA |
| 09/11 | How AI Sees Biological Data | This session introduces you to how AI models process biological data like images, protein structures, and sequences. We will discuss tools such as AlphaFold for structure prediction, Cellpose for cell segmentation, DeepLabCut for motion tracking, and DeepVariant for sequence alignments. | TBA |
| 09/18 | Applying Language Models for Biological Research | This session focuses on using AI language models to understand and process scientific language — from parsing papers and protocols to generating PubMed queries or cleaning metadata. You'll learn how to use AI to extract insights from documents and automate literature searches. | TBA |
| 09/25 | Debugging, Optimizing, and Translating Code With AI | Learn how to use AI to troubleshoot bioinformatics workflows, translate between R, Python, and Bash, and refactor scripts for clarity and reproducibility. Whether it's fixing a broken Nextflow command or understanding a confusing error, AI can help. Bring your own examples or use ours — this session is built on real-world frustrations. | TBA |
| 10/02 | Using AI to Interpret Analyses and Results | You’ve run the pipeline — now what? This session focuses on showing how AI can be used in order to make sense of the data, figures or reports you have generated, and asking the correct questions to follow up the results. | TBA |
References:
- A Bioinformatics Wiki. C. Lizarraga. Data Science Institute. UArizona.
- Artificial Intelligence and Machine Learning in Bioinformatics.
- A survey of best practices for RNA-seq data analysis. Conesa, A., Madrigal, P., Tarazona, S. et al. A survey of best practices for RNA-seq data analysis. Genome Biol 17, 13 (2016). https://doi.org/10.1186/s13059-016-0881-8.
- awesome-bioinformatics
- awesome-biological-visualizations
- From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, Del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella KV, Altshuler D, Gabriel S, DePristo MA. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;43(1110):11.10.1-11.10.33. doi: 10.1002/0471250953.bi1110s43. PMID: 25431634; PMCID: PMC4243306.
- Genome Browser
- RNA-seq and Differential Expression. High Performance Research Computing. Texas A&M University.
- TeSS (Training eSupport System).
Updated: 07/18/2025 (M. Cosi)
UArizona Data Lab, Data Science Institute, University of Arizona.