tiledbvcf

You can store and query large VCF datasets by ingesting samples into a TileDB-VCF array, which enables scalable storage, incremental sample addition, and fast region-based queries for population genomics pipelines.

Yes, incremental sample addition is supported across cohorts. You can ingest single samples into an existing TileDB-VCF dataset without reprocessing the entire cohort, ensuring scalable storage and efficient updates.

Fast region-based queries are executed directly against the TileDB-VCF storage array, allowing you to rapidly filter and export genomic variants by specific genomic regions for clinical or population genomics analyses.

Yes, TileDB-VCF supports both local and cloud storage backends for genomics pipelines, allowing you to store and query variant datasets efficiently while configuring memory, region, and sample handling parameters.

You can prepare VCF data for GWAS-ready analyses by using TileDB-VCF to build population genomics datasets, compare allele frequencies across cohorts, and export queried variant regions efficiently.

Yes, a Python or CLI interface is required to use TileDB-VCF. You need a Python environment to install the package, create datasets, ingest samples, and run configurable genomic variant queries.