Learn computational biology by building something real.
A growing training pathway for students who want hands-on experience with biomedical data, coding, reproducible research, multi-omics, and AI-assisted scientific workflows.
Placeholder page · training modules being builtYou do not need to know everything before you start.
Students may arrive from biology, biochemistry, computer science, mathematics, or another STEM field. The goal is to build enough shared vocabulary and technical skill to contribute to a real research question.
Biology-first student
Learn Linux, Git, scripting, data structures, statistics, and reproducible analysis while applying your biological knowledge.
Computing-first student
Learn how biomedical experiments are designed, what metadata mean, how biological evidence is evaluated, and why context matters.
Already mixed?
Move quickly into a defined project involving real data, benchmarking, workflow development, visualization, or scientific software.
Starter pathway
Research basics
Questions, hypotheses, controls, metadata, and reading a scientific paper.
Computing basics
Linux shell, files, Git/GitHub, environments, and reproducibility.
Python or R
Data frames, visualization, statistics, and scientific scripting.
Biomedical data
RNA-seq, genomics, proteomics, clinical data, and common analysis patterns.
Mini-project
Answer one defined question and produce a figure, analysis, workflow, or software contribution.
Possible project tracks
Cancer multi-omics
Connect DNA, RNA, protein, and phenotype to investigate tumor biology and treatment response.
Bioinformatics workflows
Build and test reproducible workflows using Nextflow, containers, Git, and HPC resources.
Machine learning & AI
Benchmark models, evaluate features, test LLM-assisted workflows, and learn how to validate computational claims.
Proteomics
Analyze quantitative protein data, perform pathway analysis, visualize results, and connect them to biological questions.
Scientific software
Turn repeated analysis tasks into reusable scripts, packages, dashboards, or research tools.
Data visualization
Learn to communicate complex biological results clearly through figures, reports, and interactive outputs.
Resources coming next
Before Day 1
Accounts, software setup, GitHub, basic command line, and a short readiness checklist.
Linux + Git quick start
A practical introduction focused only on what you need to begin research.
Python / R mini-lessons
Short exercises using biomedical-style data rather than generic programming examples.
Responsible AI for research
How to use AI tools productively while checking sources, evidence, reproducibility, and scientific validity.
Mini-project library
Small starter projects that can grow into posters, manuscripts, software, or longer research experiences.
How to present your work
Figures, posters, abstracts, GitHub documentation, scientific writing, and communicating to mixed audiences.
Arkansas INBRE student opportunities
Interested in a summer research experience?
Students interested in computational biology, cancer research, data science, or AI applications in medicine can use this training hub to prepare for project-based research opportunities.
Interested in joining a computational project?
Send a short introduction with your major, year in school, any coding or biology experience, what you want to learn, and one research area that interests you. Prior programming experience is helpful but not required for every project.
Introduce yourself