OpenCGA Catalog implements a high-performance metadata database to track all files metadata, samples, families, ...
OpenCGA implements authentication to control what data can be seen by users. Data such as Files, Samples, Families, .. can be shared in different way.
OpenCGA implements a high-performance and scalable variant NoSQL database to store and index thousands of whole genome VCF files. Performance observed show more than 2,000 whole genomes indexed a day.
Many variant operations have been implemented such as variant aggregation, stats calculation, variant annotation, export, ...
We have implemented the most advanced query engine and aggregation framework to query variants.
Indexing BAM files and calculating coverage is supported. You can efficiently query all these data through REST web services.
We have implemented a comprehensive REST API to work with Catalog and query Variants and Alignment data in a secure way. To facilitate using REST we have developed four client libraries developed in Java, Python, R and Javascript.
OpenCGA implements two different command lines, one for the users and one for the admin. Users can fully operate OpenCGA from the command line.
OpenCGA implkements most common analysis such as stats or GWAS among many other ones. We will keep adding more common analysis in each version.
Users can implement their own native analysis for OpenCGA by developing a plugin. These plugins can easily be installed and executed in OpenCGA.
OpenCGA can also execute any other external binary (C++, Python R, ...) by creating a simple wrapper that connect OpenCGA storage engine with the binary. We also provide some official external binaries supported such as Plink
You can store all you clinical data in our free data model solution in Catalog. You can define your clinical variables and annotate files, samples, individuals, families or cohort. Clinical Data is indexed automatically to provide a real-time queries and aggregations analysis.
Disease Panels are fully supported and versioned.
You can define different types of Clinical Analysis. We have implemented some automatic clinical interpretation algorithms for Rare Diseases (families) and Cancer. A Decision Support System has also been implemented in IVA.
OpenCGA takes advantage of the rich data models developed in OpenCB. We make an extensive use of Variant and Variant Annotation data models.
OpenCGA implements several analysis top of the Variant storage. These analysis can use different programming models – such as MapReduce – or different technologies such as Spark.
A Spark-based library has developed to provide extra analysis capabilities.
OpenCGA architecture was designed to be fully compatible with modern cloud architectures, this makes of OpenCGA extremely efficient and performance in cloud environments.
OpenCGA and Microsoft collaborated to test and validate HDInsight security and analysis performance.
Web based on IVA project at https://github.com/opencb/iva/tree/app/hgva
Server based on OpenCGA at https://github.com/opencb/opencga
IVA is a collaborative project that aims to integrate as many reference human studies as possible, you can contact us for feature request. If you want to contribute to the code you are more than welcome to contribute to IVA and OpenCGA
A University of Cambridge start-up was launched in 2019. Zetta provides official support and a number of different services.
This is was officially announced in later 2019, if you want to know more about this, please contact im411@cam.ac.uk
Ignacio Medina (HPCS, University of Cambridge)
Web based on IVA project at https://github.com/opencb/iva/tree/app/hgva
Server based on OpenCGA at https://github.com/opencb/opencga
IVA is a collaborative project that aims to integrate as many reference human studies as possible, you can contact us for feature request. If you want to contribute to the code you are more than welcome to contribute to IVA and OpenCGA