"Wet lab" and "dry lab" describe two ways of doing biology. In a wet lab you handle samples, reagents and instruments; in a dry lab you work with data, models and code. Modern life science needs both, and the scientists who are most in demand understand how each side shapes the other. This guide explains the difference, the skills each requires and how to decide where to start.
What happens in a wet lab
A wet lab is a laboratory where experiments are performed on biological material: DNA, RNA, proteins, cells, microorganisms or tissues. The work is called "wet" because it involves liquids: buffers, media, reagents and samples.
Typical wet-lab techniques include:
- Nucleic-acid work: DNA and RNA isolation, PCR, quantitative PCR (qPCR), gel electrophoresis and cloning.
- Protein work: expression, purification, SDS-PAGE, Western blotting and enzyme assays.
- Cell and tissue culture: maintaining mammalian, microbial or plant cells under sterile conditions.
- Microbiology: isolation, staining, identification and growth measurements.
- Analytical methods: spectrophotometry, chromatography and immunoassays such as ELISA.
Good wet-lab practice is about accuracy, sterility, safety and documentation. A single pipetting error, a contaminated culture or a missing control can invalidate weeks of work.
What happens in a dry lab
A dry lab is where biological questions are answered computationally. Bioinformaticians and computational biologists analyse sequencing data, model protein structures, screen molecules, build statistical and machine-learning models, and develop the software and pipelines that make this possible.
Typical dry-lab work includes:
- Genomics and transcriptomics: quality control, alignment, variant calling, RNA-seq and single-cell analysis.
- Structural biology and drug discovery: structure prediction, molecular docking, molecular dynamics and QSAR.
- Statistics and machine learning: experimental design, hypothesis testing and predictive models.
- Engineering: pipelines, cloud computing, databases and scientific software.
Dry-lab work is "wet" in a different way: it is only as good as the data and the assumptions behind it. Choosing the wrong statistical test, ignoring batch effects or leaking information between training and test sets produces confident but wrong answers.
The difference in one table
| Wet lab | Dry lab | |
|---|---|---|
| Material | Samples, reagents, instruments | Data, code, models |
| Core skills | Technique, sterility, safety, documentation | Programming, statistics, reproducibility |
| Typical errors | Contamination, pipetting, missing controls | Wrong test, leakage, batch confounding |
| Output | Measurements, materials, observations | Analyses, predictions, software |
| Where | A laboratory | Anywhere with a computer |
Why the best scientists understand both
Most modern projects cross the boundary. A gene-expression study starts with RNA isolation at the bench and ends with differential-expression analysis on a computer. A drug-discovery project moves between docking predictions and binding assays. Decisions on one side change what is possible on the other:
- Experimental design decides whether an analysis can separate the effect of interest from batch effects. A bioinformatician who understands the bench can spot a confounded design before samples are processed.
- Data quality depends on wet-lab choices such as RNA integrity, library preparation and sequencing depth. A bench scientist who can read a quality-control report can fix problems at the source.
- Validation often needs both: a computational prediction is confirmed by an experiment, and an unexpected experimental result is explained by re-analysing the data.
People who can speak both languages are rare, which is why "wet + dry" skills are valued in research institutes, biotech companies, diagnostics laboratories and CROs.
Which should you learn first?
It depends on your background and goals.
- If you are a biology or biotechnology student with little coding experience, strengthen your bench skills and start learning the command line and Python or R in parallel. Our Bioinformatics Foundations course assumes no programming.
- If you already work in a lab, learn to analyse your own data. NGS Data Analysis with Galaxy teaches sequencing analysis without coding, and Biostatistics & Experimental Design covers the statistics behind sound experiments.
- If you come from computer science or engineering, learn enough biology to ask the right questions. Molecular & Cell Biology Essentials is designed for exactly that.
- If you want both, a combined programme is efficient. The Bench-to-Bioinformatics Programme takes you from RNA isolation and qPCR at the bench to RNA-seq analysis on the computer.
Getting hands-on
Reading about techniques is not the same as performing them. For wet-lab skills, choose training where every participant works at the bench, not demonstrations watched from a distance. For dry-lab skills, choose courses that use real public datasets and end with a project you can show. Both kinds of evidence, a laboratory notebook and a code repository, carry weight with supervisors and employers.
You can also explore computational methods today, for free, in the Nayancode Workbench, which runs analyses privately in your browser.
Written by the Vivofold team for research and education. Found an error? Tell us and we will correct it.