data discovery

Shivom Announces Major Update to it’s Platform, Teamwork, Overhauled UX and Data Discovery Tool for Pharma.

data discovery

Shivom’s new release seeks to provide researchers and pharmaceutical companies with a major overhaul on how drug-discovery and development are conducted. The platform has been engineered to handle the complexities of gaining access through multi-layered consent and interrogating multi-omics data in the age of remote working during the coronavirus pandemic.

LONDON, UK – September 14, 2020 – Shivom Ventures Limited (“Shivom”), a leader in biotech and data-sharing solutions, today announced a major update to its platform, an end-to-end bioinformatics suite that accelerates drug discovery and development by delivering enriched insights through AI/ML technology. Building upon its earlier releases, Shivom’s v3.0 adds new features such as teamwork, dark mode, dynamic interrogation, revolutionary new interface and more. The most celebrated feature of v3.0 is the new Discover tool, allowing researchers to explore massive sets of genomic, phenotypic, multi-omic and other structured data generated by biobanks, academic institutions and consortiums from across the globe. 

Researchers are increasingly searching within large repositories generated by academic institutions, biobanks, consortium partners and even now becoming more common, technology companies for genomic and clinical data. However, access to restricted data is making it difficult for researchers to launch new clinical research because the organisations who are storing the dating are relying on legacy technologies to officiate access between data custodians and the clientele. With most databases and biobanks, the bona fide user must apply for access with a corresponding data access control body, provide an institution ID, and a long publication record. Usually, they will need their organization’s signing official to participate in the registration process, they are asked to submit a lengthy proposal detailing the data they want to use and may need to go through an Institutional Review Board. On average, a straightforward application and approval process takes 2–3 months; sometimes it has taken up to half a year. These legacy procedures can heavily impact the time it takes to initiate clinical research and even hinder a project from ever starting. 

Dr Axel Schumacher, Shivom Co-Founder & Chief Research Officer, said, “Data sharing technologies are still extremely underutilized and most data custodians are reluctant to share their data, demonstrating that the global healthcare community needs an elemental paradigm shift in how data is shared and utilized. We developed the technology to make quick, easy, and fine-grained data sharing and data analytics a reality. The Shivom platform enables researchers to build data cohorts, making large, or expensive-to-collect, datasets available to all. Data can be reused, reanalyzed, repurposed, and mined for further insight, saving organisations valuable time and resources, thereby shortening the time to discovery. The Shivom platform makes data easily findable & searchable, and ultimately actionable, as data can be analyzed on the bioinformatics marketplace, breaking down data silos, putting data in a larger, global context. But we did not stop there and improved accessibility by avoiding complicated access procedures, promoting maximum use of research data. With better reusability, data is not stored and forgotten, but reusable in many research projects.”

Facilitating data exploration and sharing is embedded within the core of Shivom’s new release as it’s aim is to empower next-generation scientists with next-generation tools in order to solve the most complex genetic diseases and viruses.

Shivom v3.0 Key Features & Pipelines

One of the key changes on the platform as part of the revamp includes the addition of dark theme UI. With just a click of a button, the researcher can change platform surface variant from standard light background to a black/dark grey which reduces luminance. This latest change is to make research safer by reducing eye strain, which is often the case with sitting carrying large computational analysis – allowing analysis to be carried for prolonged periods of time whilst minimising the risk of concentration lapse.

Another key feature includes the launch of the long-awaited ‘teamwork’ functionality. As the scientific research community across pharmaceutical, academia and industry increase in collaboration for scientific research breakthroughs, there has been an increase in the ability to allow research to be conducted across different organisations. This new feature allows account holders to add new users/members either from consortia or partners to curate discovery datasets, initiate & monitor analyses and view result output real-time. This allows researchers to focus on science and research instead of issues with regards to sharing data, integrating platforms (cloud storage and servers) and exchanging results.

The ‘projects’ feature has been updated for v3.0 for better usability. Previously, single users had the ability to monitor analysis and refer back to an old analysis run previously, which included both parameters and result output. The update partitions analysis into ‘in progress’, ‘completed’ and ‘failed’. This allows the user to multitask and monitor different ongoing analysis with ease without losing track, but also identify failed pipelines quickly in order to fix and rerun. These changes will allow for better analysis experiences for the researcher – which can become confusing and unwieldy especially when there are dozens of analyses being run simultaneously. Furthermore, for users that are part of the ‘teamwork’ group – will have the ability to see team members icons that are running other pipelines within the group.

Finally, there has been an additional analytical pipeline to the platform. A gene set analytical tool for GWAS summary statistics and raw genotype data has been added to the suite of GWAS pipelines including Meta-analysis, PRS and Genetic Correlations. Gene set enrichment analysis is used to identify gene set groups that are enriched in the GWAS results output in order to see whether there are any associations.

Ibrahim Farah, VP of Product said. ‘Today’s launch of v3.0 marks a huge milestone at Shivom and is one of our most thorough and comprehensive updates thus far. We have made several enhancements and additions across various parts of the platform, including user interaction and experience with dark mode theme, the ability to conduct research as a team or as part of a collaboration and finally new tools to interrogate genetic datasets for biological discoveries. We aim to ensure that all our customers have the best research experience when conducting large scale analysis by taking the technical burdens required to execute such complexities whilst bringing all the tools they need to quickly run analysis, interpret and share them.’

ABOUT SHIVOM

Shivom provides 1-click access to genomic data and insights. The end-to-end cloud operating system enables computation and analysis to Omics data, accelerating drug discovery and delivering novel insights to personalised medicine. Users are able to gain easy access to genomic data and the ability to run various bioinformatics & AI tools in one environment.

Headquartered in London, UK, Shivom’s mission is to build the world’s largest genetic marketplace. Shivom’s ecosystem of partners and customers spans 10 countries including two active European pilot projects with H2020.

Shivom’s Press Contact: nate@shivom.io

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