Assays & Analyses
Novel discoveries don't come from running a single assay. Unlock productivity with Pluto's flexible platform, where you can finally analyze all of your biological data in one place.
Don't see the technology you're looking for? Contact us to learn more about other techniques and pipelines available.
Store large, raw sequencing data files and transform them into biologically-meaningful results in your browser
Bulk RNA sequencing
Measure genome-wide gene expression with RNA-seq to detect individual & pathway-level changes in transcription.
Map global binding sites with ChIP-seq and visualize results for histones, transcription factors, or any other protein of interest.
Single cell RNA sequencing
Leverage scRNA-seq to hone in on cell type-specific gene expression profiles that change under different conditions.
Profile chromatin and identify loci with high signal-to-noise with this efficient epigenome method.
Learn more with a live demo
Use this run-on variant to map RNA Polymerase II active sites across the genome with single-base resolution.
An emerging immunotethering technology, CUT&Tag detects peaks even with low input & sequencing depth.
Detect the unique chromatin landscape & measure changes in accessibility across different samples.
Other high-throughput assays
Organize -omics and other data to easily search and compare biomarkers across experiments
- Methyl arrays
- Looking for something else? Contact Us
Low(er) throughput assays
Make the simplest experiments powerful with interactive, customizable figures and robust statistics
- Cytokine panels
- Pharmacokinetics, inhibition and toxicity
- Microscopy imaging
- Mesoscale discovery assays
- And more!
Run fast and flexible bioinformatics analyses with all parameters tracked along the way for end-to-end reproducibility. Examples include:
Summarize raw or normalized values for targets in different sample groups.
Compare genome-wide gene expression / binding in two groups for significant changes.
Run gene set enrichment (GSEA) & other algorithms for pathway-level biology.
Analyze data collected across multiple time-points, ages, doses, and more.
Experiment with principal components (PCA), UMAP, t-SNE algorithms for sample clustering.
Highlight representative images with captions & methology alonside their quantification.
Overlap gene lists, run survival analysis, and many more!
Take your analysis to the next level and answer the scientific questions you care about most.
Clustering and visualizing samples based on their differentially expressed genes, peaks, or other targets.
For epigenetics experiments, visualize genome-wide coverage using deepTools2 tornado plots / heatmaps and TSS profile plots.
Ready to get started?
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