license: mit
tags:
- dna
- variant-effect-prediction
- biology
- genomics
Human variants
A curated set of variants from three sources: ClinVar, COSMIC, OMIM and gnomAD. Predictions for methods benchmarked in GPN-MSA paper can be downloaded from here. Functional annotations can be downloaded from here.
For more information check out our paper and repository.
Data sources
ClinVar: Missense variants considered "Pathogenic" by human labelers.
COSMIC: Somatic missense variants with a frequency at least 0.1% in cancer samples (whole-genome and whole-exome sequencing only).
OMIM: Regulatory variants considered "Pathogenic" by human labelers, curated in this paper.
gnomAD: All common variants (MAF > 5%) as well as an equally-sized subset of rare variants (MAC=1). Only autosomes are included.
Usage
from datasets import load_dataset
dataset = load_dataset("songlab/human_variants", split="test")
Subset - ClinVar Pathogenic vs. gnomAD common (missense) (can specify num_proc to speed up):
dataset = dataset.filter(lambda v: v["source"]=="ClinVar" or (v["label"]=="Common" and "missense" in v["consequence"]))
Subset - COSMIC frequent vs. gnomAD common (missense):
dataset = dataset.filter(lambda v: v["source"]=="COSMIC" or (v["label"]=="Common" and "missense" in v["consequence"]))
Subset - OMIM Pathogenic vs. gnomAD common (regulatory):
cs = ["5_prime_UTR", "upstream_gene", "intergenic", "3_prime_UTR", "non_coding_transcript_exon"]
dataset = dataset.filter(lambda v: v["source"]=="OMIM" or (v["label"]=="Common" and "missense" not in v["consequence"] and any([c in v["consequence"] for c in cs])))
Subset - gnomAD rare vs. gnomAD common:
dataset = dataset.filter(lambda v: v["source"]=="gnomAD")