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Updated: May 9, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Using ChatGPT as a tool for training nonprogrammers to generate genomic sequence analysis code.

Haley A Delcher1, Enas S Alsatari1, Adeyeye I Haastrup1

  • 1Department of Pharmacology, University of South Alabama, Mobile, Alabama, USA.

Biochemistry and Molecular Biology Education : a Bimonthly Publication of the International Union of Biochemistry and Molecular Biology
|May 5, 2025
PubMed
Summary
This summary is machine-generated.

Biology students without programming skills can now analyze large Next-Generation Sequencing (NGS) datasets using ChatGPT. This AI tool empowers them to generate custom Python code, bridging the gap between biology and computer science for critical genomic data analysis.

Keywords:
active learningcomputational biologycomputers in research and teachingcurriculum design development and implementationgenomics proteomics bioinformaticsintegration of research into undergraduate teachingoriginal models for teaching and learning

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Analyzing large genomic datasets is challenging for biologists lacking programming expertise.
  • This often necessitates collaboration with computer scientists or extensive personal training.
  • Current limitations hinder independent genomic data analysis in biological research.

Purpose of the Study:

  • To demonstrate how ChatGPT can empower biology students with no programming background to analyze large Next-Generation Sequencing (NGS) datasets.
  • To integrate AI tools into undergraduate research curricula for practical bioinformatics skills development.
  • To bridge the gap between biological research and computational analysis capabilities.

Main Methods:

  • Implementing ChatGPT within a Course-based Undergraduate Research Experience (CURE) curriculum.
  • Utilizing students' existing biology knowledge as prompts for ChatGPT to generate Python code.
  • Directing ChatGPT to create programs capable of handling and analyzing large NGS datasets (>10 GB).

Main Results:

  • Students successfully generated Python programs using only biology prompts via ChatGPT.
  • These programs effectively analyzed Next-Generation Sequencing datasets exceeding 10 gigabytes.
  • The approach enabled students to perform comprehensive, publishable-quality data analysis.

Conclusions:

  • ChatGPT integration in education can equip biologists with essential programming skills for genomic data analysis.
  • AI tools like ChatGPT can bridge the interdisciplinary gap between biology and computer science.
  • ChatGPT offers a powerful new avenue for biological researchers to accelerate NGS data analysis and scientific discovery.