--- license: mit tags: - dna - biology - genomics --- # Processed whole-genome alignment of 100 vertebrate species For more information check out our [paper](https://doi.org/10.1101/2023.10.10.561776) and [repository](https://github.com/songlab-cal/gpn). 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: ```python 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 - ```python 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) ```bash 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](https://huggingface.co/datasets/lpigou/89.zarr), courtesy of [lpigou](https://huggingface.co/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 - ```python msa_path = "89.zarr" ```