How to Use AI Agents in Biology: A Practical Guide
How to give a scientific AI agent the right context, audit its decisions, preserve provenance, and tell whether its conclusions are supported.
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Resources
Bioinformatics, explained from first principles
Practical guides to the concepts behind biological data analysis, plus reproducible workflows, infrastructure, validation studies, and product notes from Pipette.bio.
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The newest practical guides, research notes, and product writing from the Pipette.bio team.
Genomic Selection Explained for Plant Breeding
How genome-wide markers and historical field trials can help breeding programs decide what to test, cross, and advance next.
Read the guideTrack 01 · New series
Bioinformatics fundamentals
Focused, practical explanations of the concepts researchers meet in real analyses—written to answer one question clearly and connect it to the right workflow.
Genomic Selection Explained for Plant Breeding
Training populations, GEBVs, realistic validation, genotype-by-environment interaction, hybrid prediction, and breeding decisions.
→Seurat vs Scanpy: Which Should You Use for Single-Cell RNA-seq Analysis?
How the R and Python single-cell ecosystems differ in workflow design, scale, integration, machine learning, and statistical analysis.
→GSEA vs GO Enrichment: What’s the Difference?
How ranked-list GSEA differs from GO over-representation analysis—and when each method answers the better biological question.
→TPM vs FPKM vs Counts: What’s the Difference in RNA-seq?
How sequencing depth, gene length, and the downstream question determine which expression measure to use.
→Track 02
Infrastructure & engineering
Architecture, planning systems, workflow orchestration, scientific context, and the operational problems behind reproducible biological analysis.
AI Bioinformatics Agent vs Workflow Manager: What’s the Difference?
Compare AI bioinformatics agents with Nextflow, Snakemake, WDL, CWL, and Galaxy—and learn when each model fits the work.
Read the comparisonHow to Use AI Agents in Biology: A Practical Guide
How biologists can specify an analysis, review consequential decisions, verify scientific integrity, and recognize warning signs in agent-generated work.
→The Skill Graph: A Literature-Derived Map of Bioinformatics
How a directed knowledge graph derived from the literature supports Pipette’s planning agent and can be queried through MCP.
→The Bioinformatics Hiring Crisis Is Unsolvable
An argument for scaling analytical capacity through better infrastructure rather than relying on hiring alone.
→Why We Built Pipette (Even Though Claude Code Exists)
Where general coding assistants help, where bioinformatics asks for more structure, and why Pipette takes a domain-specific approach.
→Agentic AI for Biology: What Scientific AI Agents Can Actually Do
An evidence-based guide to how scientific AI agents plan, use tools, execute bioinformatics workflows, fail, and remain accountable to human review.
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Scientific workflows & validation
Dataset reanalysis, workflow selection, method coverage, and practical guides grounded in concrete biological analysis.
Reproducing a Rice Salt-Stress RNA-Seq Study in 90 Minutes
A reanalysis of raw RNA-seq data from a published salt-stress study, including the automated workflow and reproduced findings.
Read the reanalysisPlant Bioinformatics & Agricultural Genomics with Pipette
A practical overview of genomic prediction, QTL mapping, plant GWAS, imputation, and population-structure workflows.
Read the workflow guideLooking for analysis evidence?Browse datasets, workflows, outputs, and downloadable reports.
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Product notes
What changed, and why
Release notes and product decisions covering the workspace, planning, analysis capabilities, recovery, and reporting.
Literature Search, Smarter Reports, and More Reliable Bioinformatics
Pipette.bio can now search and summarize scientific papers, answer questions about completed reports, support more specialized genomics workflows, and handle long-running analyses more reliably.
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