alterlab-tiledbvcf

Efficient genomic variant data storage and querying is achieved by organizing VCF/BCF files into a TileDB-VCF dataset. This enables scalable ingestion, parallel region queries, and incremental sample additions without reprocessing existing data.

Yes, adding new samples to an existing VCF dataset does not require reprocessing. TileDB-VCF supports incremental sample addition through high-performance ingestion of single-sample VCF files with index support.

The best way to perform region queries across thousands of genomic samples is using parallel execution. TileDB-VCF facilitates fast region-based queries across large cohorts with memory-conscious configurations and cloud storage compatibility.

Yes, TileDB-VCF works with cloud storage for population genomics workflows. It provides flexible data export and interoperability, making it suitable for cohort studies, GWAS preparation, and scalable variant data management.

You can export VCF data for downstream analysis using transparent export features. TileDB-VCF supports both CLI and Python interfaces to extract and export queried variant data efficiently for further bioinformatics processing.

No, you do not strictly need Python as TileDB-VCF offers both CLI and Python interfaces. You can ingest VCF/BCF files and perform parallel region queries using either command-line tools or Python scripts.