varcode
Variant annotation in Python
Decision gist · record as of 2026-08-14
Yes. Varcode is actively maintained, has low install friction, carries a permissive license, and fills a specific niche in genomic variant annotation. It is well-suited for researchers and bioinformaticians working with variant data who need reliable effect prediction. Requires Python ≥3.9 and a one-time setup of reference genome data via pyensembl; no known security vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires reference genome data installed via pyensembl (e.g., `pyensembl install --release 75 76`) before variant effects can be computed.
- Low install friction with a pure-Python wheel.
- Active maintenance as of 2026-07-09 with recent release (36 days old).
License · maintenance · safety
permissive license (permissive) — Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
last release 2026-07-09 (36 days) · last repo commit 2026-07-09 · 87 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 96,701 downloads/mo, #13,199 on PyPI
Alternatives
Verify before relying
pip install varcode
pyensembl install --release 75
import varcode
variants = varcode.load_maf("variants.maf")
effects = variants.effects()
print(effects[0])- Whether pyensembl reference data download and setup is straightforward for first-time users
- Performance characteristics with large variant sets (e.g., whole-genome or multi-sample VCFs)
- Completeness of effect annotation coverage across all variant types and edge cases
What it is and what it does
Varcode is a Python library for working with genomic variant data and predicting the impact of those variants on protein sequences. It loads variants from standard formats like MAF files, organizes them by gene, and computes detailed mutation effects—such as amino acid substitutions, frameshifts, premature stops, and splice disruptions—on each affected transcript. The library represents effects as typed objects (e.g., `Substitution`, `FrameShift`, `PrematureStop`) that expose the mutant protein sequence, the transcript involved, and the gene context.
The package is built on numpy, pandas, and pyensembl for reference genome lookups. It handles complex scenarios like multi-outcome effects (where a variant's impact is ambiguous without additional information), structural variants, and germline-aware annotation. Users typically load variant collections, filter or group by gene, call `.effects()` to compute annotations, and inspect the resulting effect objects for downstream analysis—such as identifying driver mutations, predicting immunogenicity, or prioritizing variants for experimental validation.
Use it for
- Load TCGA or other cancer variant datasets and predict which mutations cause protein-truncating effects like premature stops or frameshifts.
- Annotate somatic variants in a tumor sample to identify which genes are affected and classify each variant's functional impact.
- Group variants by gene and compute effects across all transcripts to understand isoform-specific consequences of the same mutation.
- Filter variant collections for high-priority effects (e.g., nonsense mutations) to prioritize candidates for experimental validation or clinical reporting.
- Analyze multi-sample VCF files with per-sample genotype information to classify variants as somatic or germline and compute sample-aware effects.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Varcode is actively maintained, has low install friction, carries a permissive license, and fills a specific niche in genomic variant annotation. It is well-suited for researchers and bioinformaticians working with variant data who need reliable effect prediction. Requires Python ≥3.9 and a one-time setup of reference genome data via pyensembl; no known security vulnerabilities.
Install
varcode on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance as of 2026-07-09 with recent release (36 days old). Depends on numpy, pandas, and pyensembl; requires reference genome data installed separately via pyensembl.
Requires reference genome data installed via pyensembl (e.g., `pyensembl install --release 75 76`) before variant effects can be computed.
License in practice
Licensed under Apache Software License (permissive), allowing commercial and private use with minimal restrictions.
Quickstart
pip install varcode
pyensembl install --release 75
import varcode
variants = varcode.load_maf("variants.maf")
effects = variants.effects()
print(effects[0])Verify before relying
- Whether pyensembl reference data download and setup is straightforward for first-time users
- Performance characteristics with large variant sets (e.g., whole-genome or multi-sample VCFs)
- Completeness of effect annotation coverage across all variant types and edge cases
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesnumpypandaspyensemblmemoized_propertyserializablesercol |
| Maintenance | Actively maintained 36 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 96,701 / month, #13,199 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonTopic :: Scientific/Engineering :: Bio-Informatics |
Evidence: varcode-7.0.0-py3-none-any.whl
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