![]() Partitioning a large VCF file involves breaking it into a number of roughly equal-sized parts that canīe processed in parallel. For small files this is fine, but for very largeįiles it’s a good idea to partition them so the conversion runs faster. Pass with no need for intermediate temporary files. ![]() In the single file case, the input VCF is converted to the output Zarr file in a single sequential Processing multiple inputs is more work than a single file, since behind the scenes each input isĬonverted to a separate temporary Zarr file on disk, then these files are concatenated and rechunked > from sgkit.io.vcf import vcf_to_zarr > vcf_to_zarr (, "output.zarr" ) If there are multiple files, then pass a list: The sgkit.io.vcf.vcf_to_zarr() function can accept multiple files, and furthermore, each of theseįiles can be partitioned to enable parallel processing. load_dataset ( "output.zarr" ) > ds Dimensions: (alleles: 4, ploidy: 2, samples: 1, variants: 19910) Dimensions without coordinates: alleles, ploidy, samples, variants Data variables: call_genotype (variants, samples, ploidy) int8 dask.array call_genotype_mask (variants, samples, ploidy) bool dask.array call_genotype_phased (variants, samples) bool dask.array sample_id (samples) variant_allele (variants, alleles) object dask.array variant_contig (variants) int8 dask.array variant_id (variants) object dask.array variant_id_mask (variants) bool dask.array variant_position (variants) int32 dask.array Attributes: contigs: max_variant_allele_length: 48 max_variant_id_length: 1 > import sgkit as sg > from sgkit.io.vcf import vcf_to_zarr > vcf_to_zarr ( "CEUTrio.20.21.gatk3.4.g.vcf.bgz", "output.zarr" ) > ds = sg. ![]() To install sgkit with VCF support using pip (there is no conda package): VCF support is an “extra” feature within sgkit and requires additional Support for polyploid and mixed-ploidy genotypes. ![]() Input and output files can reside on local filesystems, Amazon S3, or Index, and each region is processed in parallel using Dask.Ĭontrol over Zarr chunk sizes allows VCFs with a large number of samples Large VCF files can be partitioned into regions using a Tabix (. Reads bgzip-compressed VCF and BCF files. The sgkit.io.vcf.vcf_to_zarr() function converts one or more VCF files to Zarr files stored in Example: converting 1000 genomes VCF to Zarr ![]()
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