ArticleGigaScience2025
Analysis-ready VCF at Biobank scale using Zarr.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mass spectrometry-based human spatial omics: fundamentals, innovations, and applications.Journal of biomedical science · 2026Pooled it
- Revisiting mutational resistance to ampicillin and cefotaxime in Haemophilus influenzae.Genome medicine · 2024Pooled it
- What has population genomics told us about the dynamics of selection and plant adaptation?Molecular biology and evolution · 2026Review
- Tracing the evolutionary histories of ultra-rare variants using variational dating of large ancestral recombination graphs.bioRxiv : the preprint server for biology · 2026Article
- A Pandemic-Scale Ancestral Recombination Graph for SARS-CoV-2.bioRxiv : the preprint server for biology · 2025Article
- A benchmark study of compression software for human short-read sequence data.Scientific reports · 2025Article
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Authors and funding
16 authors.
Funding
Abstract
backgroundVariant Call Format (VCF) is the standard file format for interchanging genetic variation data and associated quality control metrics. The usual row-wise encoding of the VCF data model (either as text or packed binary) emphasizes efficient retrieval of all data for a given variant, but accessing data on a field or sample basis is inefficient. The Biobank-scale datasets currently available consist of hundreds of thousands of whole genomes and hundreds of terabytes of compressed VCF. Row-wise data storage is fundamentally unsuitable and a more scalable approach is needed.
resultsZarr is a format for storing multidimensional data that is widely used across the sciences, and is ideally suited to massively parallel processing. We present the VCF Zarr specification, an encoding of the VCF data model using Zarr, along with fundamental software infrastructure for efficient and reliable conversion at scale. We show how this format is far more efficient than standard VCF-based approaches, and competitive with specialized methods for storing genotype data in terms of compression ratios and single-threaded calculation performance. We present case studies on subsets of 3 large human datasets (Genomics England: $n$=78,195; Our Future Health: $n$=651,050; All of Us: $n$=245,394) along with whole genome datasets for Norway Spruce ($n$=1,063) and SARS-CoV-2 ($n$=4,484,157). We demonstrate the potential for VCF Zarr to enable a new generation of high-performance and cost-effective applications via illustrative examples using cloud computing and GPUs.
conclusionsLarge row-encoded VCF files are a major bottleneck for current research, and storing and processing these files incurs a substantial cost. The VCF Zarr specification, building on widely used, open-source technologies, has the potential to greatly reduce these costs, and may enable a diverse ecosystem of next-generation tools for analysing genetic variation data directly from cloud-based object stores, while maintaining compatibility with existing file-oriented workflows.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.