One of the goals of The 100,000 Genomes Project from Genomics England is to enable new medical research. Researchers will study how best to use genomics in healthcare and how best to interpret the data to help patients. The causes, diagnosis and treatment of disease will also be investigated. This is currently the largest national sequencing project of its kind in the world.

To achieve this goal Genomics England set up a Research environment for the researchers and installed OpenCGA, CellBase and IVA from OpenCB. We have loaded 64,078 whole genomes in OpenCGA, in total more than 1 billion unique variants have been indexed in the OpenCGA Variant Storage, and all metadata and clinical data for samples and patients have been loaded in OpenCGA Catalog. Variants were annotated using CellBase and IVA front-end was installed to analyse and visualise the data. Here you can find a full report of about the loading and analysis of the 64,078 genomes.

Genomic and Clinical Data

The variants and clinical data of 64,078 participant genomes been loaded and indexed in OpenCGA. In total they represent more than 30,000 VCF files compressed accounting for about 40TB of disk space. This data is divided in four different datasets depending on the genome assembly and the type of study (germline or somatic), this data has been organised in OpenCGA in three Projects and four Studies:

Each dataset is loaded in OpenCGA as a different Study, grouped in 3 different Projects, depending on the type of data (germline / somatic), and assembly (GRCh37 / GRCh38).

Platform

The Hadoop cluster consists of about 30 nodes running Hortonworks 2.6.5 (with HBase 1.1.2) and a LSF queue for loading all the VCF files, see this table for more detail:

OpenCGA 1.4.2 with the Hadoop Variant Storage has been used.

Loading Data

The data ingestion is executed in the LSF nodes, connected directly to the Hadoop platform. This configuration allows us to run multiple files at the same time.

Having 10 worker nodes in the queue, each of them loading up to 6 files at the same time, results in 60 files being loaded concurrently.

Multi sample files

The files from Rare Disease studies (RD38 & RD37) contain more than one sample per file. In average, 2 samples per file.

Single sample files

The files from Cancer Germline studies (CG38) contain one sample per file. Compared with the Rare Disease, these files are smaller in size, therefore, the load is slightly faster.

Query Performance

We would like to distinguish two types of queries: General and Clinical

General Queries

These queries are are only filtering by variant annotation and cohort stats. These queries only include aggregated data, not returning sample genotypes.

Clinical Queries

Clinical queries, or sample queries, enforces queries to return variants of a specific set of samples. These queries can use all the filters from the general queries. The result will include a ReportedEvent for each variant, which determines possible conditions associated to the variant.