vepyr 0.2.0

VEP Yielding Performant Results — Python interface for Ensembl VEP annotation in Rust

vepyr

vepyr (/ˈvaɪpər/) — VEP Yielding Performant Results — a blazing-fast Rust reimplementation of Ensembl's Variant Effect Predictor.

Setup with uv

1. Install uv.

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Clone the repository and enter it.

git clone [e-mail]:biodatageeks/vepyr.git
cd vepyr

3. Sync dependencies and build the package in place.

RUSTFLAGS="-C target-cpu=native" uv sync --reinstall-package vepyr

4. Run Python commands inside the managed environment.

uv run python -c "import vepyr; print(vepyr.__all__)"

5. Run the test suite.

uv run pytest

Quick start

The repository ships with small test fixtures so you can verify the full pipeline — build, annotate with indexed Parquet, and write VCF output — without downloading any external data.

1. Build a cache from a local Ensembl VEP cache directory

tests/data/ensembl_cache contains a tiny slice of the Ensembl VEP 115 offline cache (chr22). Convert it to the default indexed Parquet cache:

import vepyr

results = vepyr.build_cache(
    release=115,
    cache_dir="/tmp/vepyr_cache",
    cache_type="ensembl",
    local_cache="tests/data/ensembl_cache",  # skip download
)
for path, rows in results:
    print(f"{path}: {rows:,} rows")

2a. Annotate variants

A small 5-variant VCF for chr22 ships with the cache fixture:

import vepyr

cache_dir = "/tmp/vepyr_cache/115_GRCh38_ensembl"

lf = vepyr.annotate(
    vcf="tests/data/ensembl_cache/sample.vcf",
    cache_dir=cache_dir,
    check_existing=True,
    af=True,
    af_gnomadg=True,
    max_af=True,
)

df = lf.collect()
print(df.select("chrom", "start", "ref", "alt", "most_severe_consequence").head())

workers controls how many within-contig annotation pipelines run concurrently. workers=1 is the serial path; workers > 1 requires a tabix-indexed (bgzip + .tbi) input VCF.

df = vepyr.annotate(
    "input.vcf.gz",
    cache_dir,
    workers=4,
).collect()

build_cache() writes variation as chrN_warm.parquet and chrN_cold.parquet files, plus cold-position and variant-bloom indexes. Re-running build_cache() is idempotent by default; pass overwrite=True to rebuild existing cache outputs.

out = vepyr.annotate(
    "input.vcf.gz",
    cache_dir,
    workers=8,
    output_vcf="annotated.vcf",
)

2b. Write annotated VCF output

Instead of a LazyFrame, write results directly to a VCF file with CSQ in the INFO column — use .vcf.gz for bgzf compression or .vcf for plain text:

out_path = vepyr.annotate(
    vcf="tests/data/ensembl_cache/sample.vcf",
    cache_dir=cache_dir,
    check_existing=True,
    af=True,
    af_gnomadg=True,
    max_af=True,
    output_vcf="/tmp/annotated.vcf",  # or .vcf.gz for bgzf
)
print(f"Wrote annotated VCF to {out_path}")

3. Full --everything annotation (golden test data)

tests/data/golden has a pre-built chr1 cache, a 100-variant VCF, and a matching reference FASTA. Run a full --everything annotation:

import vepyr

lf = vepyr.annotate(
    vcf="tests/data/golden/input.vcf.gz",
    cache_dir="tests/data/golden/cache",
    everything=True,
    reference_fasta="tests/data/golden/reference.fa",
)

df = lf.collect()
print(f"{df.height} variants × {df.width} columns")
print(df.select("chrom", "start", "ref", "alt",
                "most_severe_consequence", "SYMBOL", "IMPACT").head(5))

Documentation

Build and serve the docs locally:

uv sync --extra docs
uv run mkdocs serve

Then open http://127.0.0.1:8000. Docs are auto-deployed to GitHub Pages on each tag push.

One-liner smoke test

Exercises cache build, indexed Parquet annotation, and VCF output:

uv run python -c "
import vepyr, tempfile, os
with tempfile.TemporaryDirectory() as d:
    r = vepyr.build_cache(115, d, cache_type='ensembl', local_cache='tests/data/ensembl_cache', show_progress=False)
    cache = os.path.join(d, '115_GRCh38_ensembl')
    print(f'build_cache : {len(r)} parquet files, {sum(n for _,n in r):,} rows')
    vcf = 'tests/data/ensembl_cache/sample.vcf'
    df1 = vepyr.annotate(vcf, cache, check_existing=True, af=True, max_af=True).collect()
    print(f'indexed     : {df1.height} variants × {df1.width} columns')
    out = os.path.join(d, 'annotated.vcf')
    vepyr.annotate(vcf, cache, check_existing=True, af=True, max_af=True, output_vcf=out, show_progress=False)
    print(f'vcf output  : {os.path.getsize(out):,} bytes')
    assert os.path.getsize(out) > 0, 'empty VCF'
lf = vepyr.annotate('tests/data/golden/input.vcf.gz', 'tests/data/golden/cache', everything=True, reference_fasta='tests/data/golden/reference.fa')
df = lf.collect()
print(f'everything  : {df.height} variants × {df.width} columns')
assert df.height > 0 and df.width > 80, 'smoke test failed'
print('smoke test passed')
"

Source	Added fields	Count
VCF CSQ fixed base fields	Allele, Consequence, IMPACT, SYMBOL, Gene, etc.	18
--everything --hgvs flag-derived fields, de-duplicated against VCF base	includes frequency, MANE, UniProt, HGVS offset, regulatory, etc.	59
VEP option-set implication: frequency/pubmed flags enable check_existing	CLIN_SIG, SOMATIC, PHENO	3
--merged	REFSEQ_MATCH, SOURCE, REFSEQ_OFFSET	3
--flag_pick_allele_gene	PICK	1
BAM-edited cache auto-enables --use_transcript_ref + bam_edited	GIVEN_REF, USED_REF, BAM_EDIT	3
Total		87

#	Field	Breakdown bucket
1	Allele	VCF CSQ fixed base
2	Consequence	VCF CSQ fixed base
3	IMPACT	VCF CSQ fixed base
4	SYMBOL	VCF CSQ fixed base
5	Gene	VCF CSQ fixed base
6	Feature_type	VCF CSQ fixed base
7	Feature	VCF CSQ fixed base
8	BIOTYPE	VCF CSQ fixed base
9	EXON	VCF CSQ fixed base
10	INTRON	VCF CSQ fixed base
11	HGVSc	VCF CSQ fixed base
12	HGVSp	VCF CSQ fixed base
13	cDNA_position	VCF CSQ fixed base
14	CDS_position	VCF CSQ fixed base
15	Protein_position	VCF CSQ fixed base
16	Amino_acids	VCF CSQ fixed base
17	Codons	VCF CSQ fixed base
18	Existing_variation	VCF CSQ fixed base
19	DISTANCE	Default / --everything flag-derived
20	STRAND	Default / --everything flag-derived
21	FLAGS	Default / --everything flag-derived
22	PICK	--flag_pick_allele_gene
23	VARIANT_CLASS	--everything
24	SYMBOL_SOURCE	--everything
25	HGNC_ID	--everything
26	CANONICAL	--everything
27	MANE	--everything
28	MANE_SELECT	--everything
29	MANE_PLUS_CLINICAL	--everything
30	TSL	--everything
31	APPRIS	--everything
32	CCDS	--everything
33	ENSP	--everything
34	SWISSPROT	--everything
35	TREMBL	--everything
36	UNIPARC	--everything
37	UNIPROT_ISOFORM	--everything
38	REFSEQ_MATCH	--merged
39	SOURCE	--merged
40	REFSEQ_OFFSET	--merged
41	GIVEN_REF	BAM-edited cache / --use_transcript_ref
42	USED_REF	BAM-edited cache / --use_transcript_ref
43	BAM_EDIT	BAM-edited cache
44	GENE_PHENO	--everything
45	SIFT	--everything
46	PolyPhen	--everything
47	DOMAINS	--everything
48	miRNA	--everything
49	HGVS_OFFSET	--everything --hgvs
50	AF	--everything
51	AFR_AF	--everything
52	AMR_AF	--everything
53	EAS_AF	--everything
54	EUR_AF	--everything
55	SAS_AF	--everything
56	gnomADe_AF	--everything
57	gnomADe_AFR_AF	--everything
58	gnomADe_AMR_AF	--everything
59	gnomADe_ASJ_AF	--everything
60	gnomADe_EAS_AF	--everything
61	gnomADe_FIN_AF	--everything
62	gnomADe_MID_AF	--everything
63	gnomADe_NFE_AF	--everything
64	gnomADe_REMAINING_AF	--everything
65	gnomADe_SAS_AF	--everything
66	gnomADg_AF	--everything
67	gnomADg_AFR_AF	--everything
68	gnomADg_AMI_AF	--everything
69	gnomADg_AMR_AF	--everything
70	gnomADg_ASJ_AF	--everything
71	gnomADg_EAS_AF	--everything
72	gnomADg_FIN_AF	--everything
73	gnomADg_MID_AF	--everything
74	gnomADg_NFE_AF	--everything
75	gnomADg_REMAINING_AF	--everything
76	gnomADg_SAS_AF	--everything
77	MAX_AF	--everything
78	MAX_AF_POPS	--everything
79	CLIN_SIG	implied check_existing
80	SOMATIC	implied check_existing
81	PHENO	implied check_existing
82	PUBMED	--everything
83	MOTIF_NAME	--everything
84	MOTIF_POS	--everything
85	HIGH_INF_POS	--everything
86	MOTIF_SCORE_CHANGE	--everything
87	TRANSCRIPTION_FACTORS	--everything