Compression of genomic data to facilitate precision medicine
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.