Erwan Drezen

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Data volumes generated by next-generation sequencing (NGS) technologies is now a major concern for both data storage and transmission. This triggered the need for more efficient methods than general purpose compression tools, such as the widely used gzip method. We present a novel reference-free method meant to compress data issued from high throughput(More)
MOTIVATION Efficient and fast next-generation sequencing (NGS) algorithms are essential to analyze the terabytes of data generated by the NGS machines. A serious bottleneck can be the design of such algorithms, as they require sophisticated data structures and advanced hardware implementation. RESULTS We propose an open-source library dedicated to genome(More)
Background. Large scale metagenomic projects aim to extract biodiversity knowledge between different environmental conditions. Current methods for comparing microbial communities face important limitations. Those based on taxonomical or functional assignation rely on a small subset of the sequences that can be associated to known organisms. On the other(More)
Medico-administrative data like SNDS (Système National de Données de Santé) are not collected initially for epidemiological purposes. Moreover, the data model and the tools proposed to SNDS users make their in-depth exploitation difficult. We propose a data model, called the ePEPS model, based on health care trajectories to provide a medical view of raw(More)
Secondary use of medical and administrative databases has become a powerful tool for epidemiological studies. In that respect, the recent access opening of French nationwide health record database or SNDS (Système National des Données de Santé) is a great opportunity to carry out comprehensive health studies at the country level. However, using this(More)
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