Will AI replace Bioinformatics Scientists?

Many of your tasks are within reach of AI. The role will change, so it pays to act now.

Task exposure to today's AI

57/100

High exposure

This is the occupation's typical task mix. Your own score can be up to 30 points higher or lower depending on how you work: routine share, people contact, judgment and how much you already use AI.

Get your personal score

Will AI replace Bioinformatics Scientists?

Not entirely. Today's AI tools can take on about 57% of the typical work in this occupation, mostly: communicate research results through conference presentations, scientific publications, or project reports. The parts that stay human include: Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.; Develop new software applications or customize existing applications to meet specific scientific project needs.; Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies..

People who learn to use AI for the automatable parts keep the edge. The 3-phase plan below shows where to start.

Which tasks AI can take on

The largest tasks in this occupation, their share of the working week, and how much of each today's AI tools can do.

TaskAutomatable

Communicate research results through conference presentations, scientific publications, or project reports. 70%

Develop new software applications or customize existing applications to meet specific scientific project needs.AI already used 62%

Compile data for use in activities, such as gene expression profiling, genome annotation, or structural bioinformatics. 60%

Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes. 55%

Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies.AI already used 53%

Create novel computational approaches and analytical tools as required by research goals. 47%

+ 14 more tasks (41% of the week) in the full test.

Your human edge

Analyze large molecular datasets, such as raw microarray data, genomic sequence data, or proteomics data, for clinical or basic research purposes.
Develop new software applications or customize existing applications to meet specific scientific project needs.
Consult with researchers to analyze problems, recommend technology-based solutions, or determine computational strategies.

AI tools to try

ChatGPT / Claude for literature and writingAI research tools (e.g. Elicit, Consensus, Semantic Scholar)Copilot for data analysis code (Python, R)

Related roles

A 3-phase AI learning plan

Phase 1

Faster literature and writing

Literature search and summaries with AI, always checked against the papers.
Drafts of methods, reports and grant text.
Reference management and formatting.

Project

Use AI to screen 40 papers on one pathway, verify every claim against the source PDFs, and draft a 2-page methods and background section with formatted references in under 8 hours.

Phase 2

AI for data analysis

Analysis code in Python or R written with AI help.
Visualisation and statistics explained step by step.
Reproducibility: version code and prompts.

Project

Rebuild a past RNA-seq or proteomics analysis as an AI-assisted, version-controlled Python or R pipeline with logged prompts, and compare runtime and figure quality against your original.

Phase 3

AI as a research method

Classification, extraction or modelling with AI on your data.
Validate against human labels or ground truth.
Ethics, data protection and disclosure of AI use.

Project

Use an LLM to extract phenotype and variant fields from 200 de-identified public records, score precision and recall against your own manual labels, and write an AI-use disclosure statement.

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