Inverting the model of genomics data sharing with the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL)

Abstract

The traditional model of genomic data analysis - downloading data from centralized warehouses for analysis with local computing resources - is increasingly unsustainable. Not only are transfers slow and cost prohibitive, but this approach also leads to redundant and siloed compute infrastructure that makes it difficult to ensure security and compliance of protected data. The NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL; https://anvilproject.org) inverts this model, providing a unified cloud computing environment for data storage, management, and analysis. AnVIL eliminates the need for data movement, allows for active threat detection and monitoring, and provides scalable, shared computing resources that can be acquired by researchers as needed. This presents many new opportunities for collaboration and data sharing that will ultimately lead to scientific discoveries at scales not previously possible.

A complete reference genome improves analysis of human genetic variation

“…To investigate how the T2T-CHM13 assembly impacts short-read variant calling, we realigned and reprocessed all 3,202 samples from the recently expanded 1KGP cohort [28] using the NHGRI AnVIL Platform [39]. In this collection, each sample is sequenced to at least 30x coverage using paired-end Illumina sequencing, with samples from 26 diverse populations across 5 major continental superpopulations ( Fig.…”

GA4GH: International policies and standards for data sharing across genomic research and healthcare

“…GA4GH Driver Projects and other partners are beginning to implement cloud-based workflows built on GA4GH standards that allow scientists to share, access, and interrogate data stored at disparate sites around the globe. Some concrete examples of this access pattern include (1) the Data Coordination Platform of the Human Cell Atlas, an internationally federated compute environment for analyzing single-cell data; (2) Genomics England’s secure Research Environment for approved investigators to access the 100,000 Genomes Project dataset; (3) the NHGRI Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) 30 and the Gen3 Data Commons, which provide cloud-based spaces for scientists to work with large-scale genomic and genomic-related datasets and shared tools; and (4) H3ABioNet, a bioinformatics platform that serves data from the Human Heredity and Health in Africa (H3Africa) network to researchers across the continent and provides containerized workflows for analysis of the data.…”

recount3: summaries and queries for large-scale RNA-seq expression and splicing

“…The contents of the resource and its user interfaces are illustrated in Figure 1a. In addition to SciServer-Compute, we have made recount3 available from AnVIL (Schatz et al, 2021), the genomic data science cloud platform from NHGRI. In summary, we created an RNA-seq processing framework and resulting resource to facilitate reanalysis of hundreds of thousands of RNA-seq samples.…”