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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 clinicians, and we installed OpenCGA, CellBase and IVA from OpenCB as data platform. 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. OpenCGA was able to index about 6,000 samples a day, variant annotation and cohort stats run in about 2 more days, in total all data was loaded, annotated and stats calculated in less than 2 weeks. 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 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. Data is divided in four different datasets depending on the genome assembly (GRCh37 or GRCh38) and the type of study (germline or somatic), this data has been organised in OpenCGA in three different Projects and four Studies:

ProjectStudy ID and NameSamplesVCF FilesVCF File TypeSamples/FileVariants
GRCh37 Germline


Rare Disease GRCh37

12,1425,329Multi sample2.28298,763,059
GRCh38 Germline


Rare Disease GRCh38

33,18016,591Multi sample2.00437,740,498


Cancer Germline GRCh38

9,1679,167Single sample1.00286,136,051
GRCh38 Somatic


Cancer Somatic GRCh38



OpenCGA Catalog stores all the metadata and clinical data of files, samples, individuals and cohorts. Rare Disease studies also include pedigree metadata by defining families. Also, a Clinical Analysis has been defined for each family. Several Variable Sets have been defined to store GEL custom data for all these entities.


For the Research environment we have used OpenCGA v1.4 using the new Hadoop Variant Storage that use Apache HBase as back-end because of the huge amount of data and analysis needed. We have also used CellBase v4.6 for the variant annotation. Finally we set up a IVA v1.0 web-based variant analysis tool.

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

NodeNodesCoresMemory (GB)Storage (TB)
Hadoop Master5282167.2 (6x1.2)
Hadoop Worker30282167.2 (6x1.2)
LSF Loading Queue 1012364Isilon storage

Genomic Data Load

In order to improve the loading performance we set up a small LSF queue of ten computing nodes. This configuration allows to load multiple files at the same time. We configured LSF to load up to 6 VCF files per node resulting in 60 files being loaded in HBase in parallel without any incidence, by doing this we observed a 50x in loading throughput. This resulted in an average of 125 VCF files loaded per hour in studies RD37 and RD38, about 2 files per minute. In the study CG38 the performance was 240 VCF files per hour or about 4 files per minute.

Rare Disease Loading Performance

The files from Rare Disease studies (RD38 & RD37) contain 2 samples per file on average. This results in larger files, increasing the loading time, compared with single-sample files. As mentioned above the loading performance was about 125 genomes per hour or 3,000 files per day. In terms of number of samples it is about 250 samples per hour or 6,000 samples a day.

The loading performance always depend on the number of variants and concurrent files being loaded, the performance was quite stable during the load and performance degradation was observed as can be seen here:

Concurrent files loaded60
Average files loaded per hour125.72
Load time per file00:28:38

Saturation Study

As part of the data loading process we decided to study the number of unique variants added in each batch of 1,000 samples. We generated this saturation plot for RD38:  

Cancer Loading Performance

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 file load is almost 2x faster. As mentioned above the loading performance was about 240 genomes per hour or 5,800 files per day. In terms of number of samples it is about 5,800 samples a day, which is consistent with Rare Disease studies.

Concurrent files loaded60
Average files loaded per hour242.05
Load time per file00:14:52

Analysis Benchmark

In this section we will outline the performance of some operations and common queries and clinical analysis. Please, for data loading performance see section Genomic Data Load above.


Variant Storage operations take care of leaving the data ready for the queries and analysis. There are two main operations to consider: Variant Annotation and Cohort Stats Calculation.

Variant Annotation

This process uses the CellBase server to generate a Variant Annotation for each unique variant in the database. This annotation is then used for filtering and analysis. The annotation throughput is 3.5M variants per hour. The process of annotating of the whole dataset took 17 days.

Cohort Stats Calculation

Cohort stats can be used for filtering variants in a similar way as the population frequencies. A set of cohorts where defined in each study.

  • ALL with all samples in the study
  • PARENTS with all parents in the study (only for Rare Disease studies)
  • UNAFF_PARENTS with all unaffected parents in the study (only for Rare Disease studies)

Precompute stats is a fast light operation that can run in less than 2 hours for each study, depending on the number of cohorts and variants.

Query and Aggregation Stats

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

FilterResultsTotal ResultsTime
consequence type = LoF + missense_variant



consequence type = LoF + missense_variant

biotype = protein_coding


panel with 200 genes


Clinical Analysis

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.

FilterResultsTotal ResultsTime

Segregation mode = biallelic

filter = PASS



Segregation mode = biallelic

filter = PASS


De novo variants

filter = PASS

consequence type = LoF + missense_variant

Compound Heterozygous

filter = PASS

biotype = protein_coding

consequence type = LoF + missense_variant

User Interfaces


IVA v1.0.3 has been installed to provide a friendly web-based analysis tool to browse and perform aggregation stats over all the variants. Also, users can execute interactive clinical interpretation analysis or browse the clinical data.   


We would like to thank Genomics England very much for their support and for trusting OpenCGA and the rest of OpenCB suite for this amazing release.

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