Seedlabs

PanelDiff: a many-panel gene overlap comparer for genomics labs

A tool for genomics labs to compare which genes are shared or unique across different testing panels.

A tool for clinical and research genomics that compares 6–20 gene panels in one quasi-proportional Venn diagram and exports shared/unique gene lists, replacing manual spreadsheet diffs for panel versioning and validation documentation.

Computer ScienceData Visualization and Analytics
Materials Science Information SystemsClinical genomics and molecular diagnostics

Concept

A panel-comparison tool for genomics: select or upload six to twenty gene panels (lab-developed panels, commercial panels, curated panels such as PanelApp's), and get a single quasi-proportional Venn diagram where each region's area reflects the number of genes shared by that exact combination of panels, plus exportable shared/unique gene lists per region. The wedge is validation and versioning documentation: when a diagnostic lab updates a panel from v2 to v3 or switches vendors, it must document exactly what changed — today that means error-prone pairwise spreadsheet diffs. The tool also flags any region clamped at the minimum drawable radius and prints true gene counts, so small unique-gene sets are never visually inflated.

Why now

nVenn2 [0] was built for multi-set overlap analysis in bioinformatics and now makes diagrams with more than nine sets feasible where the previous version could not, with runtime growing with the number of non-empty regions rather than set count — and gene panels are exactly the sparse case where most intersections are empty. It is deployed today as R and Python packages with editable appearance, so a product wrapper around it is realistic now. The published benchmarks used 40 independent diagrams per configuration over universes of 20–50 elements, matching small-panel sizes, and the disclosed 2% minimum-radius constraint is turned into an explicit clamp flag with true counts rather than hidden distortion.

Written by GLM · 11 Oct 2026

AI assessment

Backed by 1 paper92

A highly focused utility tool that solves a specific, painful compliance bottleneck in clinical genomics using a proven visualization algorithm.

Evidence strength
5/5
The idea directly implements the nVenn2 algorithm's specific capability to handle sparse, multi-set overlaps, matching the research's benchmarks and constraints perfectly.
Market pull
4/5
Clinical labs have strict regulatory requirements for panel validation and versioning, making a tool that automates this documentation highly valuable.
Novelty & moat
4/5
While other genomics tools exist in the catalog, this addresses a distinct visualization and documentation gap rather than a data migration or auditing problem.
Feasibility
5/5
The core engine (nVenn2) already exists as R and Python packages, meaning the MVP is essentially a UI/UX wrapper around existing code.
Wedge clarity
5/5
The focus on 'validation and versioning documentation' is a sharp, high-urgency entry point that targets a specific regulatory pain point.
Simplicity / focus
5/5
The product is a single-purpose tool with one clear function: comparing gene lists and exporting the results.

Scored by Gemma 4 · 11 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.

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Who benefits

  • Genomics Englandorganization

    Its PanelApp resource curates hundreds of expert gene panels; comparing overlap across many panels at once would speed curation and redundancy review.

  • Ambry Geneticscompany

    Its hereditary cancer and cardiology panels are updated frequently; automated shared/unique gene exports support version-change documentation in validation workflows.

  • LabCorpcompany

    Its clinical molecular labs must document panel changes under CAP/CLIA expectations; the tool converts manual spreadsheet diffs into a reproducible report.

  • Broad Instituteorganization

    Its genomics programs compare gene lists across many conditions; legible >9-set Venns make multi-condition comparisons practical where they previously were not.

Research it builds on

  1. nVenn2: faster, simpler generalized quasi-proportional Venn diagrams
    Samuel Pis-Vigil, María González-Pereira, Magda R. Hamczyk et al. · 2026 · 1 citations
    All ideas from this paper →
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feasibility