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Kari Lavikka @karilavikka.fi · 01/10/2026

The grammar itself uses JSON, but there is now also a Python package, which makes GenomeSpy much easier to use in Python notebooks and workflows. GenomeSpy: genomespy.app GitHub: github.com/genome-spy/

genomespy.app

GenomeSpy — Interactive Genomic Visualization for Web and Notebooks

A visualization grammar and GPU-accelerated rendering engine for genomic (and other) data

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Kari Lavikka @karilavikka.fi · 01/10/2026

GenomeSpy is an open-source toolkit for building interactive genome visualizations using a declarative grammar. A bit like ggplot2, but primarily for genome-aligned data with smooth interactivity. It also includes an application for exploring multi-sample genomic datasets, such as cancer cohorts.

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Kari Lavikka @karilavikka.fi · 01/10/2026

I made a bold move: I bumped the GenomeSpy version from v0.90.0 to … v1.0.0! 🎉 I also wrote a blog post about it, reflecting a bit on the development journey: genomespy.app/blog/posts/2...

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Kari Lavikka @karilavikka.fi · 26/08/2026

GenomeSpy used to be WebGL-first (or basically WebGL-only). Recent versions can also generate vector-graphics-editor-friendly SVGs, with automatic rasterization of dense layers. The R package uses this for SVG export. It's called MutGlyph: genomespy.app/MutGlyph/

genomespy.app

MutGlyph: Interactive Cancer Genomics Plots in R

Create interactive, zoomable oncoplots, rainfall plots, protein lollipop plots, and GISTIC copy-number landscapes in R with GenomeSpy.

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Kari Lavikka @karilavikka.fi · 26/08/2026

This was also a good opportunity to demonstrate how GenomeSpy (genomespy.app) can be embedded in an R package. The package generates visualization specifications and lets GenomeSpy do the rendering. And finally, I like my plots prettier.

genomespy.app

GenomeSpy

A visualization grammar and GPU-accelerated rendering engine for genomic (and other) data

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Kari Lavikka @karilavikka.fi · 26/08/2026

But why? There are a few reasons. Those huge oncoplot/oncoprint matrices look impressive, like Circos plots do, but they become barely readable once you throw enough data at them. Maybe interactivity helps. (PDFs are still static, though 🤔.)

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Kari Lavikka @karilavikka.fi · 26/08/2026

I made an R package that creates oncoplots, lollipop plots, rainfall plots, and GISTIC plots with basic interactivity such as tooltips and zooming. It's almost a drop-in replacement for the corresponding maftools' plotting functions.

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Kari Lavikka @karilavikka.fi · 04/08/2026

Let me know what you think and whether the visualization provides any new insights into purity/ploidy estimation!

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Kari Lavikka @karilavikka.fi · 04/08/2026

The visualization also projects the inferred integer copy numbers back into LogR and BAF space, making it easier to see how well a selected solution explains the observed data. The visualization is here: genomespy.app/docs/example...

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Kari Lavikka @karilavikka.fi · 04/08/2026

I implemented an interactive GenomeSpy visualization for this purpose. You can play with different samples from the simulated ASCAT example data and see how the choice of purity, ploidy, and other settings affects the estimated allele-specific copy numbers.

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Kari Lavikka @karilavikka.fi · 04/08/2026

When doing copy-number analysis, have you ever struggled to interpret ASCAT sunrise plots and similar purity/ploidy grids? Have you wanted to compare how different solutions affect the allele-specific copy-number fit and better understand the fitting procedure?

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Kari Lavikka @karilavikka.fi · 28/07/2026

These and many other examples are available in the documentation for you to play with: genomespy.app/docs/genomic... (3/3)

genomespy.app

Genomic Data Examples - GenomeSpy Docs

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Kari Lavikka @karilavikka.fi · 28/07/2026

A reference sequence translated into amino acids in all three reading frames on both strands, and a simple read-alignment viewer for BAM files. They also use some new data transformations I added to the visualization grammar. (2/3)

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Kari Lavikka @karilavikka.fi · 28/07/2026

I recently added a parameterizable arrow mark to GenomeSpy, my open-source toolkit for interactive genomic data visualization, and implemented a couple of fancy examples to showcase it. (1/3)

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Reposted by Kari Lavikka

Helena Klara Jambor @helenajambor.bsky.social · 21/01/2026

see you in 1 hour! #BioVis #DataVis meetup

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Kari Lavikka @karilavikka.fi · 06/08/2025

Congrats for the great work!

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Kari Lavikka @karilavikka.fi · 23/06/2025

Parquet and Arrow are great columnar formats, but the abysmal performance of TextDecoder on Chrome (or maybe V8) completely kills any speed benefits when files contain lots of short, unique strings. Even #JavaScript CSV parsers are faster. It’s fine if you don’t need to access the string columns 🤷

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Kari Lavikka @karilavikka.fi · 17/06/2025

Selections can also drive filtering and aggregation, though the code is still entirely CPU-based for now. I’d like to explore columnar and GPU-based approaches in the future. (2/2)

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Kari Lavikka @karilavikka.fi · 17/06/2025

It’s been a bit quiet on the GenomeSpy front, but I finally had time to implement interval selections and other fancy features. Vega-Lite–style conditional encodings are compiled into shader code, with selection tests running on the GPU—making interactions snappy. (1/2)

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Kari Lavikka @karilavikka.fi · 30/04/2025

I've typically used R (with tidyverse) for analyses, but recently I've been familiarizing myself with Python. Pandas feels quite awkward to me, at least compared to dplyr. But @pola.rs is great! It works wonderfully with Parquet files too. ❤️

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Kari Lavikka @karilavikka.fi · 03/03/2025

Thanks for your kind words! lesson learned! 😅 I'm glad you found my work interesting!

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Kari Lavikka @karilavikka.fi · 03/03/2025

However, I didn’t realize that "Jellyfish," the tool that generates these plots, is not. This is embarrassing.

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Kari Lavikka @karilavikka.fi · 03/03/2025

Dang. My bad. 😬 I'm the first author of this paper and should have ensured the tool's name was unique. The article is about "Jellyfish plots," which depict tumor evolution. We named them that because they resemble jellyfish with their bells and tentacles—and "jellyfish plot" was unique.

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Kari Lavikka @karilavikka.fi · 26/02/2025

Finally, I'm grateful to all the collaborators and supervisors on this and the previous tumor evolution papers and the whole tumor evolution team! (7/7)

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Kari Lavikka @karilavikka.fi · 26/02/2025

Jellyfish automates the drawing process and generates visually pleasing plots with ease. Based on the data used in the tumor evolution paper, we've now made auto-generated, interactive Jellyfish plots available at hautaniemilab.github.io/jellyfish/ (4/7)

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Kari Lavikka @karilavikka.fi · 26/02/2025

The Jellyfish visualization design was initially published in our paper on tumor evolution in ovarian high-grade serous carcinoma (Lahtinen, Lavikka, Virtanen, et al., 2023, www.sciencedirect.com/science/arti...). However, in that paper, all Jellyfish plots were drawn manually. (3/7)

sciencedirect.com

Evolutionary states and trajectories characterized by distinct pathways stratify patients with ovarian high grade serous carcinoma

Ovarian high-grade serous carcinoma (HGSC) is typically diagnosed at an advanced stage, with multiple genetically heterogeneous clones existing in the…

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Kari Lavikka @karilavikka.fi · 26/02/2025

Metastases in solid tumors consist of heterogeneous subclonal mixtures that evolve across space and time. Jellyfish integrates tumor phylogeny and subclonal compositions of spatiotemporal samples into a unified plot, making subclonal dynamics easier to interpret. #CancerResearch (2/7)

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Kari Lavikka @karilavikka.fi · 07/02/2025

Thrilled to announce that my R package 'jellyfisher' is now available on CRAN! It's my first package submission to CRAN, and a related paper will (hopefully) be published soon. Check it out: cran.r-project.org/web/packages....

cran.r-project.org

jellyfisher: Visualize Spatiotemporal Tumor Evolution with Jellyfish Plots

Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. This approach provides an intuitive way to analyze t...

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Kari Lavikka @karilavikka.fi · 07/12/2024

It's not weird. It's relaxing. I've been doing the same.

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Reposted by Kari Lavikka

Helena Klara Jambor @helenajambor.bsky.social · 22/08/2024

🚨 next #BioVis #Dataviz virtual meetup: Will Stahl-Timmins (The #BMJ) is joining us on Sept 18! "A picture of health: Visualisations in medical publishing" Details & Link: biovis.net/2024/meetup/

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Kari Lavikka @karilavikka.fi · 06/08/2024

My first first-author paper is now published: doi.org/10.1093/giga.... GenomeSpy is a grammar-based visualization toolkit for genomic data, powered by efficient WebGL rendering. Check the paper for examples on how we use it to explore a large ovarian high-grade serous carcinoma dataset.

academic.oup.com

Deciphering cancer genomes with GenomeSpy: a grammar-based visualization toolkit

AbstractBackground. Visualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remai

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