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Multi-Omics Visualiser

Multi-omics combines study of the genome, microbiome, epigenome and all the other ‘omes’ to study biological big data, sequencing these different samples to find disease markers with more accuracy than one method alone. For this project, Small Multiples worked with the pioneering researchers at Iggy Get Out, who aim to study neurodegenerative diseases such as Motor Neurone Disease and Alzheimer’s from as many different angles as possible, using a whole body system approach - looking not just at genes in isolation, but at networks.

data visualisation web app development visual design

Sector
Business
Client
Iggy Get Out
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Together with Iggy Get Out, we developed a cutting-edge visualisation tool for multi-omics datasets that helps provide immediate and actionable insights to clinicians and patients. The new web-based tool integrates multiple omics datasets for each patient, enabling scientists and clinicians to seamlessly visualise the interactive datasets and compare large amounts of genomic information. The software was developed in a collaboration between researchers, data scientists, designers and developers.

Debilitating motor neurone diseases, such as amyotrophic lateral sclerosis (ALS) are inherited in 5 to 10 percent of cases, while the rest have no known cause. We created a visual analytical tool for geneticists to find the hidden pathways that are causing the disease in each patient. This tool is fast tracking the discovery of targeted diagnostics and therapeutics.

Our adaptation of an interactive map-like interface for filtering and inspecting data provides two advantages. Firstly, it gives an investigator the ability to seamless transit between a system-level macro view and an entity-level micro view, so the tool becomes an extension of the investigator’s thought process. Secondly, the simultaneity and the continuity preserves context throughout the session, this promotes efficiency and reduces error.

The patient/disease expression profile fingerprint is a unique way to enable visual comparison of different omic factors that could indicate disease markers within patients’ expression profiles. This could potentially also pave the way for machine-learning driven visual analysis of visualisation data

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