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Computational genomics · Large language models · Reproducible research

Reproducible AI-Driven Approaches to Molecular and Evolutionary Genomics

We combine molecular genetics, computational genomics, and protein language models to investigate how biological sequences relate to function. Our work turns complex data into reproducible analyses and questions that can be tested.

Led by Rodolfo Aramayo, PhD
Associate Professor of Biology · Texas A&M University

Rodolfo Aramayo, PhD

Research you can explore

Ongoing research · MutScan

What do protein models tell us about variation?

Across 15 disease-associated proteins, we examine what model scores measure, how evidence sources overlap, and which conclusions need further biological support.

Explore the model audit →

Peer-reviewed · Computational genomics

Genomes as a basis for biological discovery

Genome assembly and comparative analysis of Myxococcus and Spiroplasma provide public resources for studying microbial function and evolution.

Read the genomics studies →

Released software · 2026

Reproducibility through manuscript preparation

One maintained LaTeX manuscript source produces four publication profiles. The versioned release pairs reusable code with build and dependency documentation.

Explore the software release →

From a biological question to the next test

  1. Frame the question

    Use biological mechanism and the literature to define a testable problem.

  2. Establish the evidence

    Examine experimental design, sequence quality, annotations, and reference data.

  3. Build and challenge

    Run reproducible analyses, compare baselines, and investigate failure cases.

  4. Define the next test

    Separate supported conclusions from hypotheses that need additional evidence.

Our research approach connects experimental genetics, computational analysis, and biological interpretation.

Rodolfo uses Claude and other LLM assistants in scientific software development, analysis, and writing, with checks against code, source data, and biological reasoning. See how this works in MutScan.

People & projects

A laboratory working across biological scales

Graduate and undergraduate researchers contribute distinct questions and methods to a shared program in proteins, genomes, and reproducible science.

Meet our researchers

Recent from the lab

  • Fall 2026
    Teaching scientific reasoning through practice

    Digital Biology and Molecular Cell Biology connect computational skills, biological mechanisms, and careful use of AI. Explore teaching & mentorship →

  • Manuscript Multi-Target LaTeX Template

    A public software release for reproducible manuscript preparation. Zenodo release · Code & documentation

  • Comparative genomics of Spiroplasma

    Our co-authored study examines toxin repertoires and metabolic capacities in three Drosophila-associated strains. Read the publication →