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Related Experiment Video

Updated: Jul 12, 2026

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

Eras of bioinformatics technologies from command-line interfaces to artificial intelligence (AI) chatbots.

Van Q Truong¹, Marylyn D Ritchie²

¹Institute for Biomedical Informatics, University of Pennsylvania, 3700 Hamilton Walk, 19104 Philadelphia, PA, United States.

Briefings in Bioinformatics

|July 10, 2026

Summary

Bioinformatics has evolved from command-line tools to a cornerstone of life sciences, driven by data growth and computational advances. The current artificial intelligence era presents new challenges in transparency and trust for researchers.

Keywords:

artificial intelligence (AI)bioinformatics eras large language models (LLMs)technology trends

Related Experiment Videos

Last Updated: Jul 12, 2026

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing

Published on: August 1, 2025

Area of Science:

Biomedical Informatics
Computational Biology
Life Sciences

Background:

Bioinformatics transformed from specialized tools to a central role in life sciences over 75 years.
Technological eras, including genomics and next-generation sequencing, spurred computational advances and data analysis challenges.

Purpose of the Study:

To trace the historical evolution of bioinformatics through distinct technological eras.
To contextualize the rise of artificial intelligence (AI) within bioinformatics history.
To provide a roadmap for navigating challenges in the AI-driven era of bioinformatics.

Main Methods:

Historical review of bioinformatics development.
Analysis of technological shifts driven by data growth and computational progress.
Examination of challenges and patterns across different bioinformatics eras.

Main Results:

Bioinformatics evolved through genomic, next-generation sequencing, and AI eras.
Scalability, accessibility, and reproducibility challenges were addressed by web servers, cloud platforms, and containerized workflows.
The AI era, featuring deep learning and large language models, is reshaping structural biology and data integration.

Conclusions:

Each technological era in bioinformatics lowers entry barriers but raises questions about transparency, trust, and rigor.
AI's integration into bioinformatics necessitates critical evaluation of its cultural, ethical, and technical implications.
A historical perspective is crucial for guiding the future of bioinformatics research and application.