Analyze biomedical data
without coding barriers
AI for biomedical analysis & research, with a built-in code environment.
AI for biomedical analysis & research, with a built-in code environment.
Built by Scientists from Nanyang Technology University, Singapore.
Backed by Google; AWS ; NTUitive
Introduction
Drylab lets bench scientists and bioinformaticians run QC, clustering, differential expression, and custom workflows, just by typing what they need.
Built-in code environment.
Fully reproducible.
Use Cases
See how both bench scientists and professional bioinformaticians use Drylab.
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Field-proven Use Cases
All Use Cases
Cell-Cell Communication analysis
Run Time: 5-7 mins
Streamlines analysis by automating ligand–receptor mapping, comparisons, and visualization into clear insights.
Pathway Enrichment analysis and Target Gene identification
Run Time: 5 mins
Quick check and identify potential hit gene based on specific expression profile across datasets, cell types, and conditions.
RNA-Seq and Differential Expression analysis
Run Time: 5-7 mins
Upload count matrices to perform automated DE testing across experimental conditions, generating gene lists, plotting for easy interpretation.
Single-cell RNA-seq analysis
Run Time: 10-30 mins
Automates key steps of scRNA-seq data processing: quality control, normalization, clustering, marker gene identification, cell type annotation and more with customizable code scripts.
Statistical analysis
Run Time: 3-7 minutes
Accepts raw, uncleaned laboratory or clinical data, automatically handles cleaning, suggests appropriate tests and delivers clear results with plots and reports.
Protocol Design
Run Time: 7 minutes
Quickly find and adapt methods from peer-reviewed literature or drop in your own protocol file to customize it for your available resources and experimental needs.
Field-proven Use Cases
All Use Cases
Cell-Cell Communication analysis
Run Time: 5-7 mins
Streamlines analysis by automating ligand–receptor mapping, comparisons, and visualization into clear insights.
Pathway Enrichment analysis and Target Gene identification
Run Time: 5 mins
Quick check and identify potential hit gene based on specific expression profile across datasets, cell types, and conditions.
RNA-Seq and Differential Expression analysis
Run Time: 5-7 mins
Upload count matrices to perform automated DE testing across experimental conditions, generating gene lists, plotting for easy interpretation.
Single-cell RNA-seq analysis
Run Time: 10-30 mins
Automates key steps of scRNA-seq data processing: quality control, normalization, clustering, marker gene identification, cell type annotation and more with customizable code scripts.
Statistical analysis
Run Time: 3-7 minutes
Accepts raw, uncleaned laboratory or clinical data, automatically handles cleaning, suggests appropriate tests and delivers clear results with plots and reports.
1
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0
Protocol Design
Run Time: 7 minutes
Quickly find and adapt methods from peer-reviewed literature or drop in your own protocol file to customize it for your available resources and experimental needs.
1
/
0
Field-proven Use Cases
All Use Cases
Cell-Cell Communication analysis
Run Time: 5-7 mins
Streamlines analysis by automating ligand–receptor mapping, comparisons, and visualization into clear insights.
Pathway Enrichment analysis and Target Gene identification
Run Time: 5 mins
Quick check and identify potential hit gene based on specific expression profile across datasets, cell types, and conditions.
RNA-Seq and Differential Expression analysis
Run Time: 5-7 mins
Upload count matrices to perform automated DE testing across experimental conditions, generating gene lists, plotting for easy interpretation.
Single-cell RNA-seq analysis
Run Time: 10-30 mins
Automates key steps of scRNA-seq data processing: quality control, normalization, clustering, marker gene identification, cell type annotation and more with customizable code scripts.
Statistical analysis
Run Time: 3-7 minutes
Accepts raw, uncleaned laboratory or clinical data, automatically handles cleaning, suggests appropriate tests and delivers clear results with plots and reports.
Protocol Design
Run Time: 7 minutes
Quickly find and adapt methods from peer-reviewed literature or drop in your own protocol file to customize it for your available resources and experimental needs.
Benchmark
Bioinformatics Analysis Performance
Protocol Design with Deep Research Performance
19% higher
17% higher
30%
Claude-4-sonnet
ChatGPT o4-mini
Drylab In-house Model
30
20
10
0
Graph (1) | Drylab achieved 30% accuracy— significantly higher than the 17% and 19% of ChatGPT o4-mini
and Anthropic’s Claude 4 sonnet on BixBench.
Bioinformatics Analysis Performance
Protocol Design with Deep Research Performance
37%
higher
56%
Google Gemini
Drylab In-house Model
60
40
20
0
Graph (2) | Drylab reached 56% step accuracy, outperforming Google Gemini’s 37% on BioProBench.
Bioinformatics Analysis Performance
Protocol Design with Deep Research Performance
37% higher
56%
Google Gemini
Drylab In-house Model
60
40
20
0
Graph (2) | Drylab reached 56% step accuracy, outperforming Google Gemini’s 37% on BioProBench.
Discussion
1.
Why 'Drylab'?
In science, a dry lab refers to the part of research that happens outside the wet bench-like analyzing data, designing experiments, and running simulations. It's half of the scientific workflow, and just as demanding.
At Drylab, we believe the dry side of science shouldn't be tedious. Our mission is to make it seamless—so scientists spend less time on manual work and more on discoveries.
2.
Who is Drylab for?
3.
What makes Drylab AI different from general AI tools like ChatGPT?
4.
How can I ensure that my lab's internal data and information remain secure?
5.
What kind of datasets can Drylab run?
Drylab supports multimodal datasets, including—but not limited to—genomics, transcriptomics, proteomics, epigenomics, metabolomics, imaging, spatial omics, and clinical or phenotypic data.
1.
Why 'Drylab'?
In science, a dry lab refers to the part of research that happens outside the wet bench-like analyzing data, designing experiments, and running simulations. It's half of the scientific workflow, and just as demanding.
At Drylab, we believe the dry side of science shouldn't be tedious. Our mission is to make it seamless—so scientists spend less time on manual work and more on discoveries.
2.
Who is Drylab for?
3.
What makes Drylab AI different from general AI tools like ChatGPT?
4.
How can I ensure that my lab's internal data and information remain secure?
5.
What kind of datasets can Drylab run?
Drylab supports multimodal datasets, including—but not limited to—genomics, transcriptomics, proteomics, epigenomics, metabolomics, imaging, spatial omics, and clinical or phenotypic data.
1.
Why 'Drylab'?
In science, a dry lab refers to the part of research that happens outside the wet bench-like analyzing data, designing experiments, and running simulations. It's half of the scientific workflow, and just as demanding.
At Drylab, we believe the dry side of science shouldn't be tedious. Our mission is to make it seamless—so scientists spend less time on manual work and more on discoveries.
2.
Who is Drylab for?
3.
What makes Drylab AI different from general AI tools like ChatGPT?
4.
How can I ensure that my lab's internal data and information remain secure?
5.
What kind of datasets can Drylab run?
Drylab supports multimodal datasets, including—but not limited to—genomics, transcriptomics, proteomics, epigenomics, metabolomics, imaging, spatial omics, and clinical or phenotypic data.