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generatR apps accepted onto competitive ICURe programme

Our recent application to the NxNw ICURe programme has been accepted, which will fund Gerard Quinn, the Early Career Researcher for 3 months to validate our genomic application idea in the market. In which we were one of 4 teams in the UK chosen by Cancer Research UK.

“Innovation-to-Commercialisation of University Research” (ICURe) is a 4-month online customer discovery programme designed using lean start-up methodology and funded by Innovate UK. 

It trains, funds and supports teams led by university early-career researchers to determine whether there is a market for products or services that utilise their research, science or technology. Each successful team will receive up to £30,000 of funding to ‘get out of the lab’ and validate their commercially-promising ideas in the marketplace.

For many years our research group struggled with one-off analysis and integration of increasing volumes of genomics data we were generating and amassing from a variety of different sources. We were increasingly reliant on the expertise and time of one or two members of the group with data analysis expertise, carrying out one-off bespoke, but routine analysis using a disconnected mixture of commercial and open source software packages.

Problem: This rapidly became the bottleneck in progressing the research goals our entire team, which was very inefficient and frustrating for members of our group and our collaborators greatly reducing the pace and number of research goals that could be progressed!

Idea: We decided to try and come up with some easy to use data analysis solutions that would allow data-user with limited computational expertise to analyse, interpret and share the results of their own experiments.

Solution: To address this unmet need, we developed a series customisable genomic data analysis generatR applications. These apps work seamlessly with existing genomic workflows, empowering data-users to carry out complex analysis at the click of a button, freeing up valuable data analysts time to work on other projects.

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