March 3, 2017

New Research shows how Open Competition can be used to Drive Innovation

Cambridge, 9 May 2013 ... New research published in GigaScience shows how dynamic and open contests can be used to drive innovation, and develop new tools for life sciences research.

The research from Eagle, the specialist in bioinformatics service solutions, and the Pistoia Alliance, a not-for-profit, precompetitive alliance of companies and organisations engaged in lowering the barriers to innovation in life science R&D shows how using a dynamic leaderboard to monitor progress in a competition, actively stimulates participants to strive harder for success.

In 2012, the Pistoia Alliance launched its “Sequence Squeeze” competition to encourage the development of novel and enhanced data compression algorithms to improve the management of the large volumes of gene sequence data coming from NGS machines.

The Pistoia Alliance partnered with Eagle to organise the event, set up the supporting infrastructure, and manage the process of receiving and judging entries.

Nick Lynch from Pistoia Alliance, and co-author of the paper, said:  “The Pistoia Alliance is keen to encourage pre-competitive collaboration to drive innovation and so we were very pleased not only with the output from the Sequence Squeeze competition itself, but also how the leaderboard worked to foster constructive competition between our contributors. We hope that this model can be adopted more widely as this experiment clearly shows the benefits of such an approach.”

Richard Holland, Chief Business Officer of Eagle and first author of the paper, added: “We were very pleased to be involved in the Sequence Squeeze competition. While the life sciences industry has made huge steps in sequencing many new genomes, the sheer amount of data we are now producing has created incredible challenges. However, through initiatives such as this, we can develop effective tools to help manage the flood of data we are now creating.”

The end result of the contest was a set of brand new compression algorithms for next-generation sequencing data, all of which are fully open-source and available for the community to use and build upon with their own ideas. This open-source requirement ensures that everyone may benefit from this open innovation, and the data compression lessons learnt in the process can be shared with everyone.

The paper can be downloaded at

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