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LABORATORY INFORMATICS
related variant that explains pathogenesis and disease symptoms, that finding may stay buried in the clinician’s notes. ‘In some cases they can submit or publish their findings but, even if that happens, it can take as long as 12 to 18 months for peer review and publication,’ says Medina. ‘Clinicians really need to be able to share their findings – with all of the patient data-related regulations in place – across hospitals. With the new federation feature, XetaBase will finally address that need to make findings available within minutes, not months.’ XetaBase is cloud-hosted and this simplifies data management and scalability, with a huge emphasis on making data secure and, effectively, available in real time. ‘You may have several gigabytes of
genotypic and other contextual data and metadata per patient,’ explains Medina. ‘The server for our platforms runs in the cloud and so this fact allows customers to easily scale to their needs, [supporting] tens or hundreds of thousands of patients in some cases, while we take care of and provide all the services that they need for the platform.’ Importantly, the OpenCB platform is built on a fileless infrastructure. ‘Other solutions rely on a file-based system, but
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“We take all of the billions of mutations from large datasets and put them into one index on the system to enable incredibly fast analyses”
then how can you easily search across, say, 20,000 files to look for a disease- related variant that matches that of your patient?’ he asks. ‘In OpenCB, in contrast, all of the genomic variant data is aggregated in one indexed database. The largest example we have is Genomics England – for which there are about 140,000 whole genomes in one single installation, accounting for about 300 terabytes of data,’ says Medina. ‘And this fileless system means that, despite this massive volume and breadth of data, we can scan the whole database within minutes or scan any patient or the entire family in a few seconds.’ In fact, the OpenCB architecture makes it possible to include hundreds of different pieces of information relevant to each genetic variant and still query the whole platform. ‘One analogy we can use to help explain this is Google,’ says Medina. ‘When
you search for something on Google, the system doesn’t search through the one trillion pages of content individually. Rather, Google has every page indexed so, when you query Google, you query that index and it takes just milliseconds. ‘We have done something similar with OpenCB. We take all of the billions of mutations from large datasets and put them into one index on the system to enable incredibly fast analyses.’ And, of course, this is critically relevant whether the query is for insight into one patient, such as searching for patients with the same mutation, but also for the disease researcher who might be querying different variants across all of the different samples, Medina adds. The ultimate vision is for a platform such as OpenCB and XetaBase to help reduce drug development times, increase the speed of disease diagnosis and aid decision-making for patients. ‘My goal for the next five years is to demonstrate we can have a significant impact on research and healthcare, and realistically help to reduce drug development times by potentially years,’ says Medina. ‘We also want to enable researchers to communicate their findings in a secure way, so that they can reanalyse data and ensure no patient is forgotten.’
Summer 2022 Scientific Computing World 23
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