Introduction to Orvos¶

Overview¶

Orvos is a platform for storing, organizing, processing, and sharing genomic and other biomedical big data. The platform is designed to make it easier for bioinformaticians to develop analysis, developers to create web applications with genomic data and IT administers to manage large-scale compute and storage resources for genomic data. The platform is designed to run on top of "cloud operating systems" such as Amazon Web Services and OpenStack. Currently, there are implementations that work on AWS and Xen+Debian/Ubuntu.

Project¶

The core technology has been under development at Harvard Medical School for many years. We are now in the process of refactoring original code, refactoring the APIs, and developing significant new capabilities. (See the Orvos Project History and the Backlog for more information on the roadmap.)

Why Orvos¶

A set of relatively low-level compute and data management functions are consistent across a wide range of analysis pipelines and applications that are being built for genomic data. Unfortunately, every organization working with these data have been forced to build their own custom systems for these low level functions. At the same time, there are proprietary platforms emerging that seek to solve these same problems. Orvos was created to provide and common solution that could be used across a wide range of applications and would be free and open source.

Benefits¶

The Orvos platform seeks to solve a set of common problems that face informaticians and IT Organizations:

Benefits to informaticians:

• Make authoring analyses and constructing pipelines in any language as efficient as possible
• Provide an environment that can run open source and commercial tools (e.g. Galaxy, GATK, etc.)
• Enable deep provenance and reproducability across all pipelines
• Provide a way to flexibly organize data and ensure data integrity
• Make queries of variant and other compact genome data very high-performance
• Create a simple way to run distributed batch processing jobs
• Enable the secure sharing of data sets from small to very large
• Provide a set of common APIs that enable application and pipeline portability across systems
• Offer a reference environment for implementation of standards
• Standardize file format translation

Benefits to IT organizations:

• Low total cost of ownership
• Eliminate unnecessary data duplication
• Ability to create private, on-premise clouds
• Self-service provision of resources
• Ability to utilize low-cost off the shelf hardware
• Easy-to-manage horizontally scaling architecture
• Straight-forward browser-based administration
• Provide facilities for hybrid (public and private) clouds
• Ensure full compliance with security and regulatory standards
• Support data sets from 10s of TB to exabytes

Functional Capabilities¶

Functionally, Orvos has two major sets of capabilities: (a) data management and (b) compute management.

Data Management¶

The data manage services are designed to handle all of the challenges associated with storing and organizing large omic data sets. The heart of theses services is the Data Manager, which brokers data storage. The data management system is designed to handle the following needs:

• Store files (e.g. BAM, FASTQ, VCF, etc.) in a reliable way
• Store metadata about files for a wide variety of organizational schema
• Create collections (sets of files) that can be used in analyses
• Ensure files are not unnecessary duplicated
• Maintain provenance on files (be able to identify their origin)
• Secure access to files
• Translate files between formats
• Make it easy to access files
• Leverage large arrays of distributed commodity drives for reliable storage

Compute Management¶

The compute management services are designed to handle the challenges associated with creating and running pipelines as large scale distributed processing jobs.

• Enable a common way to represent pipelines (JSON)
• Support the use of any pipeline creation tool
• Keep all pipeline code in a GIT repository
• Run pipelines as distributed computations using MapReduce
• Easily retrieve and store data from pipelines in the data management system
• Store a record of every pipeline that is run
• Eliminate the need to re-run pipeline jobs that have already been run
• Make it possible to easily and reliably re-run and verify any past pipeline
• Create a straightforward way to author web applications that use underlying data and pipelines
• Enable easy sharing of pipelines and applications between systems
• Be able to run distributed computations across clusters in different data centers to access very large data sets

The compute management system also includes a sub-component for doing tertiary analysis. This component has not been built yet, but we envision that it will provide an in-memory database for very high-performance queries of a compact representation of a genome that includes variants and other relevant data needed for tertiary analysis. (Learn more about the compact genome here.)

Virtual Machines¶

Orvos envisions an environment where informaticians receive access to virtual machines (VMs) on a private or public cloud. Using this approach eliminates the need for managing separate physical servers for different projects significantly increasing the utilization of underlying hardware resources. It also gives informaticians a great deal of freedom in choosing everything from their operating system to their tools. With virtual machines, each informatician or project team can have full isolation, security, and privacy for their work.

The Orvos platform provides shared common services that can be used from within a virtual machine. All of the Orvos services are accessible through APIs.

APIs and SDKs¶

Orvos is designed so all of the data management and compute management services can be accessed through a set of a consistent APIs and interfaces. Most of the functionality is represented in a set of REST APIs. Some components use native interfaces (e.g. GIT). Since most informaticians are unfamiliar with the REST syntax, their is a roadmap to develop SDKs for specific languages (Python, Perl, Ruby, R, and Java). There is also a command line interface to the system.

Dashboard¶

In addition to the APIs we are building a browser-based UI for the representing key information in the system and to provide visual tools for common activities that are well represented through tools.

Related Articles¶

Technical Architecture
Key Components