Am I a life scientist or an IT data manager? That’s the question many researchers are asking themselves in today’s data-driven life sciences organizations.
Whether it is a bench scientist analyzing a genomic sequence or an M.D. exploring biomarkers and a patient’s genomic variants to develop a personalized treatment, researchers are spending a great amount of time searching for, accessing, manipulating, analyzing, and visualizing data.
Organizations supporting such research efforts are trying to make it easier to perform these tasks without the user needing extensive IT expertise and skills. This mission is not easy.
Focus on the data
Modern life sciences data analysis requirements are vastly different than they were just a handful of years ago.
In the past, once data was created, it was stored, analyzed soon after, and then archived to tape or another long-term medium. Today, not only is more data is being generated, but also the need to re-analyze that data means that it must be retained where it can be easily accessed for longer periods.
Additionally, today’s research is much more collaborative and multi-disciplinary. As a result, organizations must provide an easy way for researchers to access data, ensure that results are reproducible, and provide transparency to ensure best practices are used and that procedures adhere to regulatory mandates.
More analytics and collaboration represent areas where The Galaxy Project (also known as just Galaxy) can help. Galaxy is a scientific workflow, data integration, and data and analysis persistence and publishing platform designed to help make computational biology accessible to research scientists that do not have computer programming experience.
Galaxy is generally used as a general bioinformatics workflow management system that automatically tracks and manages data while providing support for capturing the context and intent of computational methods.
Organizations have several ways to make use of Galaxy. They include:
Free public instance: The Galaxy Main instance is available as a free public service at UseGalaxy.org. This is the Galaxy Project’s primary production Galaxy instance and is useful for sharing or publishing data and methods with colleagues for routine analysis or with the larger scientific community for publications.
Anyone can use the public servers, with or without an account. (With an account, data quotas are increased and full functionality across sessions opens up, such as naming, saving, sharing, and publishing Galaxy-defined objects).
Publicly available instances: Many other Galaxy servers besides Main have been made publicly available by the Galaxy community. Specifically, a number of institutions have installed Galaxy and have made those installations either accessible to individual researchers or open to certain organizations or communities.
For example, the Centre de Bioinformatique de Bordeaux offers a general purpose Galaxy instance that includes EMBOSS (a software analysis package for molecular biology) and fibronectin (diversity analysis of synthetic libraries of a Fibronectin domain). Biomina offers a general purpose Galaxy instance that includes most standard tools for DNA/RNA sequencing, plus extra tools for panel resequencing, variant annotation, and some tools for Illumina SNP array analysis.
A list of the publically available installations of Galaxy can be found here.
Do-it-yourself: Organizations also have the choice of deploying their own Galaxy installations. There are two options: an organization can install a local instance of Galaxy (more information on setting up a local instance of Galaxy can be found here), or Galaxy can be deployed to the cloud. The Galaxy Project supports CloudMan, a software package that provides a common interface to different cloud infrastructures.
How it works
Architecturally, Galaxy is a modular python-based web application that provides a data abstracting layer to integrate with various storage platforms. This allows researchers to access data on a variety of storage back-ends like standard direct attached storage, S3 object-based cloud storage, storage management systems like iRODs (the Integrated Rule-Oriented Data System), or a distributed file system.
For example, a Galaxy implementation might use object-based storage such as that provided by Dell EMC Elastic Cloud Storage (ECS). ECS is a software-defined, cloud-scale, object storage platform that combines that cost advantages of commodity infrastructure with the reliability, availability, and serviceability of traditional storage arrays.
With ECS, any organization can deliver scalable and simple public cloud services with the reliability and control of a private-cloud infrastructure.
ECS provides comprehensive protocol support, like S3 or Swift, for unstructured workloads on a single, cloud-scale storage platform. This would allow the user of a Galaxy implementation to easily access data stored on such cloud storage platforms.
With ECS, organizations can easily manage a globally distributed storage infrastructure under a single global namespace with anywhere access to content. ECS features a flexible software-defined architecture that is layered to promote limitless scalability. Each layer is completely abstracted and independently scalable with high availability and no single points of failure.
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