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About

Project info, updates, and how to cite snpXplorer.

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Project info

snpXplorer is a web-server built with Flask and written in Python, HTML, and JavaScript. The source code is available on GitHub.

License: MIT License .

Cookie information: snpXplorer uses essential cookies to keep your session active and remember your selections while you use the website. These cookies are not used for analytics or marketing. The cookie notice preference is stored locally in your browser.

If you find snpXplorer useful, please cite: Tesi et al., 2021 .

PRS tool: Jordan.

Questions / collaborations: reach us.

Updates timeline

November 2025
snpXplorer v2 is live: more robust, OpenGWAS integration, new Haplotypes, Single-annotation and PRS tabs.
August 2023
Complete rewrite in Flask: major stability and speed improvements.
March 2022
Redesign while keeping the older Shiny implementation.
February 2022
Added sQTL to the Annotation section.
December 2021
Updated GWAS Catalog release to the latest available.
November 2021
Added major GWAS summary statistics and new annotation mode (up to 10,000 SNPs without GSEA).
August 2021
Switched to CADD v1.6 and improved support for rarer variants (1000G).
May 2021
snpXplorer was born.

What’s next

  • Improve Linkage for PRS and haplotypes
  • Increase number of SNPs for Annotation analysis
  • Enable upload of your own GWAS datasets
  • More support for structural variations
  • AI center

Stay tuned for amazing news!

Data & references

OpenGWAS
GWAS studies are based on OpenGWAS (regularly updated). Full dataset list: gwas.mrcieu.ac.uk.
Structural variants
Structural variants datasets based on our publication in Genome Research: link.
Linkage disequilibrium (LD)
LD information is derived from TOPMed, enabling LD-based proxy and regional context analyses.
LLM / semantic search
Trait discovery and deduplication are supported by MiniLM embeddings and SapBERT-style semantic similarity.
QTL & gene expression
eQTL/sQTL and gene-expression tracks are integrated from GTEx across >40 tissues.
Variant effects (CADD)
Functional effect predictions are annotated using CADD v1.7.
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