GenomeSpy

GenomeSpy provides building blocks for tailored, interactive genomic visualizations. Integrate them into web applications or explore your data in Python notebooks such as Jupyter and marimo, or in Observable. GPU-accelerated WebGL rendering supports smooth zooming and panning through large datasets.

A declarative grammar lets you describe your data, choose graphical marks, and combine views. GenomeSpy builds upon the concepts originally introduced in The Grammar of Graphics and later implemented in ggplot2 and Vega-Lite.

Originally developed to explore large sample collections in cancer genomics research, GenomeSpy has since been used in several publications. Try the interactive genome browser below, or browse more examples.

Resources

Documentation (JSON / JavaScript)
Documentation (Python)
Playground
Observable notebooks
GitHub
Questions and feedback
A sixty-second lightning talk presented at VIZBI 2021
The award-winning BioVis @ ISMB 2020 poster and talk
MSc thesis

Publications

S. Holmström et al., "FUSE: Data-driven FUnctional SEgmentation of DNA Methylation Data," Bioinformatics, Apr 2026, doi: 10.1093/bioinformatics/btag201.
G. Micoli et al., "Decoding the Genomic and Functional Landscape of Emerging Subtypes in Ovarian Cancer", Cancer Discovery, Jul 2025, doi: 10.1158/2159-8290.CD-25-0652.
K. Lavikka et al., "Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit," GigaScience, Aug 2024, doi: 10.1093/gigascience/giae040.
A. Lahtinen et al., “Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma,” Cancer Cell, May 2023, doi: 10.1016/j.ccell.2023.04.017.
W. Senkowski et al., “A platform for efficient establishment and drug-response profiling of high-grade serous ovarian cancer organoids,” Dev Cell, May 2023, doi: 10.1016/j.devcel.2023.04.012.

Abstract example: Using rect and text marks to specify a labeled bar chart.
Abstract example: Using rect and text marks to make a labeled heatmap. The labels are automatically scaled to fit the cells. Try to zoom in and out!
A scatter plot with one and a half million points decorated with some annotations visualizes a miserably failed t-SNE attempt.
A Manhattan plot for Genome-Wide Association Study (GWAS).
A multiple sequence alignment that loads data from a FASTA file and displays it as a scrollable heatmap and sequence logo.
A structural variation visualization that uses the link mark to show arcs connecting the breakpoints. It also includes segmented copy-number data.
GC content of the human genome: One dataset, two visual representations. The data are loaded lazily from a BigWig file and the scale domains are autoscaled to accommodate the region.
Using lazy data loading, data transformations, and multiple layers to visualize the GENCODE gene annotation stored in a hierarchical GFF3 file.
An Observable notebook that describes how to replicate ASCAT's copy-number segmentation visualization. The visualization is interactive and thoroughly commented.
Exploring a sample collection with the GenomeSpy App. The visualization shows several cell-line samples with segmented copy numbers, loss of heterozygosity, and SNPs and indels.
GenomeSpy in action: Lahtinen, A., Lavikka, K., et al. (2023) Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma.
SegmentModel Spy. Visualizes GATK's copy-number segment models together with read and allelic counts. This is an example of using GenomeSpy as a visualization library in a special-purpose web application.

GenomeSpy was originally developed in The Systems Biology of Drug Resistance in Cancer group at the University of Helsinki.

This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant agreement No. 965193 (DECIDER) and No. 847912 (RESCUER), the Sigrid Jusélius Foundation, the Cancer Foundation Finland, and Orion Research Foundation.

The Building Blocks

Resources

Publications