tiledbvcf

To query and store VCFs fast at scale, use TileDB sparse arrays to ingest single-sample indexed VCF/BCF inputs and retrieve specific genomic regions without expensive re-merging. This enables high-performance retrieval for cohort and population genomics workflows.

Yes, you can add new samples to an existing genomic variant database incrementally. The TileDB-VCF approach supports scalable VCF/BCF ingestion, allowing you to add new participants later without rebuilding the entire dataset from scratch.

To extract variants for specific genomic regions and samples, use high-performance sparse-array querying to define target loci and select samples. You can retrieve genotypes and variant attributes, then export subsets back to VCF/BCF or tabular formats for downstream pipelines.

Ingesting VCF/BCF data into TileDB requires single-sample indexed VCF/BCF inputs with .tbi or .csi index files. It supports parallel ingestion and querying, and provides local or cloud-ready dataset URIs for flexible storage.

The best way to handle population genomics workflows without slow data re-merging is storing variants in TileDB sparse arrays. This method avoids expensive re-merging by enabling direct region-based queries and sample selection across the cohort, accelerating subset exports.

Yes, TileDB-VCF works with Python for querying genomic variant data. You can use the tiledbvcf Python API to read selected regions for chosen samples into a dataframe, supporting efficient variant querying and cohort analysis directly within your scripts.