metadata

license: mit
tags:
  - dna
  - biology
  - genomics

Processed whole-genome alignment of 100 vertebrate species

For more information check out our paper and repository.

Source data:

MSA was downloaded from http://hgdownload.soe.ucsc.edu/goldenPath/hg38/multiz100way/
Human sequence was replaced with a newer reference: http://ftp.ensembl.org/pub/release-107/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.primary_assembly.fa.gz

Available MSAs:

89.zarr.zip contains human + 89 vertebrates (excluding 10 closest primates)
99.zarr.zip contains human + 99 vertebrates

Example usage:

from gpn.data import GenomeMSA

genome_msa = GenomeMSA(msa_path)
X = genome_msa.get_msa(chrom, start, end, strand="+", tokenize=False)

Coordinates:

hg38 assembly
chrom should be in ["1", "2", ..., "22", "X", "Y"]

Streaming (playing, few VEP queries)

Faster setup (no need to download and unzip)
Slower queries (depends on network connection)
Multiple dataloader workers don't seem to work
More CPU memory required to load: 10.41 GB
Recommended if you just want to do a few queries, e.g. VEP for a couple thousand variants

msa_path = "zip:///::https://huggingface.co/datasets/songlab/multiz100way/resolve/main/89.zarr.zip"

Local download (training, large-scale VEP)

Requires downloading (34GB) and unzipping (currently quite slow, will try to improve)

wget https://huggingface.co/datasets/songlab/multiz100way/resolve/main/89.zarr.zip
7z x 89.zarr.zip -o89.zarr  # can still take 5 hours with 32 cores, will try to streamline this in the future

Update: faster unzipping here, courtesy of lpigou
Much faster to query
Can have multiple dataloader workers
Virtually no CPU memory required to load
Recommended for training or VEP for millions of variants
```
msa_path = "89.zarr"
```