cOmpRession of Genomic dAta to facilitate precision MedIcine

ended

Healthcare is a worldwide challenge. To fight diseases, medicated treatments are common. However, statistics show that 3 out of 5 treatments are currently inadequate. Personalised Medicine is one of the fundamental discipline to fix those inefficient treatments. Based on DNA, genomic sequencing is performed to achieve a personalised diagnostic for the patient. Unfortunately, this type of medicine encounters some limitations. One of them is the astronomical amount of numerical genomic data that it generates. With these volumes, transferring, storing, archiving and analysing the data become an issue. To counteract these issues, ENANCIO has developed an algorithm, called Lena, to compress genomic data and improve the entire genomic data workflow. ENANCIO aims to provide a solution to improve the transfer and storage of those data, but also to improve speed and precision of data analysis. There is an urgent need to break down the barriers that exist by giving a genomic data compression solution which considers the entire genomic data workflow. Currently, no compression solution adresses the entire dataflow. Such a technology is a new challenge. Thanks to artificial intelligent value smoothing technology, ENANCIO aims to bring the only one technology improving the entire value chain of the genomic data, from the improvement of the compression rate and compression time, allowing a faster e-transfer and a lighter storage, to the improvement of the time and precision of analysis. Genomic data market has shown a fast raise and will reach 2 billion € in 2025. ENANCIO addresses two categories of customers, private and academic sequencing platforms who manage thousands giga bytes of data. During ORIGAMI project, ENANCIO aims to study the feasibility of the algorithm to fit with the latest sequencers on the market, NovaSeq platform from Illumina (90% of market share). This new platform will be the standard of tomorrow, justifying the need of compatibility with LENA algorithm.