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OpenCGA is an open-source platform that aims to provide a full stack solution for big data analysis and visualisation of genomic data. OpenCGA has been designed to provide a secure, high-performance and scalable solution. OpenCGA covers all aspects of genomic analysis: metadata database, authentication and security, variant normalisation and aggregation, variant annotation, highly scalable variant NoSQL storage, alignment and coverage, big data variant analysis, visualisation
OpenCGA is developed and maintained in the University of Cambridge and it is currently used by several big data projects such as GEL.
Main Features
In this section you will find a summary of the main features of OpenCGA.
Catalog Metadata
Catalog Data Models and Annotations
Catalog Database
Security
Authentication
Permissions
Alignment Storage
Variant Storage
Performance and scalability
Data Management
OpenCGA provides a framework for implementing big data variant storage engines which support: real-time queries, interactive complex data aggregations, full-text search, variant analysis, ... The framework takes care of several common operations such as variant normalisation, sample genotype aggregation, variant stats calculation, variant annotation, secondary indexing or in-memory cache. Two different engines are implemented for different use cases: MongoDB and HBase. A secondary index using Solr is nicely integrated with the two implementations.
Data Management
- Advanced variant normalisation tool implemented.
- Rich and efficient data models implemented.
- Dynamic
Query Engine
Aggregation and Stats
Big Data Analysis
Performance and scalability
- Storage engines have been implemented to provide real-time queries and interactive aggregations (faceted) even with thousands of whole genomes.
- Data mod
Clinical Analysis
OpenCGA aims to provide a full solution for Clinical Genomics analysis, this covers patient clinical data, interpretation algorithms and a pathogenic variant database.
Clinical Data
- Catalog is designed to store any clinical data model.
Clinical Interpretation Analysis
- Open a patient case study by creating a clinical analysis, this contains the patient and family data, the disease or phenotype to be analysed or the files among other information.
- Complete disease panel management implemented: create, update and delete disease panels. You can also import them automatically from PanelApp (GEL). Updated panels are versioned to keep track of existing analysis.
- Several rare disease interpretation analysis implemented such as TEAM or Tiering which is based on GEL RD Tiering tool (Cancer interpretation analysis coming soon). You can use one or more disease panels in the interpretation analysis.
- You can save more than one interpretation analysis result in the clinical analysis to create one or more clinical reports.
Pathogenic Variant Database
RESTful Web Services
OpenCGA implements more than 150 RESTful web services to allow users to manipulate and query Catalog metadata and data such as alignment, variants and pathogenic variants. REST web services are documented using Swagger, you can see OpenCGA Swagger documentation at http://bioinfo.hpc.cam.ac.uk/hgva/webservices/. To facilitate the usage all of these web services we have implemented different client libraries and a command line (see below in Usability).
REST web services can be grouped in different categories: Catalog, Alignment, Variant, Clinical and Admin.
Catalog
Alignment
- You can index BAM files to query reads and calculate coverage in BigWig format
- Query method to fetch alignments in GA4GH format, several filters implemented: region, mapping quality, number of mismatches, number of hits, properly paired, ...
Variant
Clinical
Admin
Usability
REST Clients
Command-line Interface (CLI)
Visualisation
OpenCGA web catalog
IVA
Genome Browser
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