Reference-free compression of high throughput sequencing data with a probabilistic de Bruijn graph.

BACKGROUND: 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. RESULTS: We present a novel reference-free method meant to compress data issued from high throughput sequencing technologies. Our approach, implemented in the software LEON, employs techniques derived from existing assembly principles. The method is based on a reference probabilistic de Bruijn Graph, built de novo from the set of reads and stored in a Bloom filter. Each read is encoded as a path in this graph, by memorizing an anchoring kmer and a list of bifurcations. The same probabilistic de Bruijn Graph is used to perform a lossy transformation of the quality scores, which allows to obtain higher compression rates without losing pertinent information for downstream analyses. CONCLUSIONS: LEON was run on various real sequencing datasets (whole genome, exome, RNA-seq or metagenomics). In all cases, LEON showed higher overall compression ratios than state-of-the-art compression software. On a C. elegans whole genome sequencing dataset, LEON divided the original file size by more than 20. LEON is an open source software, distributed under GNU affero GPL License, available for download at http://gatb.inria.fr/software/leon/.

GATB: Genome Assembly & Analysis Tool Box.

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 assembly and analysis to fasten the process of developing efficient software. The library is based on a recent optimized de-Bruijn graph implementation allowing complex genomes to be processed on desktop computers using fast algorithms with low memory footprints. AVAILABILITY AND IMPLEMENTATION: The GATB library is written in C++ and is available at the following Web site http://gatb.inria.fr under the A-GPL license. CONTACT: lavenier@irisa.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Patients with Metastatic Melanoma Receiving Anticancer Drugs: Changes in Overall Survival, 2010-2017.

Immune checkpoint inhibitors and targeted therapies have profoundly altered the management of several cancers over the past decade. Metastatic melanoma has been at the forefront of these changes. We provide here a nationwide overview and an assessment of changes in survival in France. We included 10,936 patients receiving a systemic treatment for metastatic cutaneous melanoma between 2010 and 2017 using the French National Health Insurance database (Système National des Données de Santé). Over the study period, there was a doubling of the number of new patients receiving a systemic treatment. Cytotoxic chemotherapy was progressively replaced by targeted therapy and immune checkpoint inhibitors. Patients having initiated a first-line treatment since June 2015 gained 46% overall survival compared with those initiating treatment before 2012. Overall survival at 24 months rose from 21% to 44%. We provide real-world evidence for the improvement of overall survival in the past decade among patients with metastatic melanoma. Although the characteristics of the patients treated can vary across periods, this type of exhaustive real-world data provides evidence from broader populations than those included in clinical trials.

French administrative health care database (SNDS): The value of its enrichment.

SNIIRAM/SNDS, the French administrative health care database, covers around 99% of the population. Its main limitation is the absence of clinical information and biological results. This report exposes the value of SNIIRAM/SNDS enrichment by external databases, and the linkage issues. It is illustrated by examples: the well-known population-based cohort CONSTANCES created to answer to epidemiological research questions with a specific interest on occupational and social factors, chronic diseases, and aging; the CANARI study, a regional-based study that collected Gleason score in all pathology laboratories in Brittany and then, linked pathology results to an ad hoc extraction from SNIIRAM database; the goal was to investigate the risk of high grade prostate cancer in patients treated by 5-alpha-reductase inhibitors for a symptomatic benign prostatic hyperplasia; the SACHA study, that identified and medically validated major bleeding event referred to emergency wards, then linked those clinical data to SNIIRAM; the goal was to minimize misclassification bias when estimating bleeding risk in patients who were prescribed antithrombotic drugs; the ISO-PSY study linked the SNIIRAM with the national cause of death registry (CépiDc) and the nationwide emergency department surveillance system (OSCOUR® network) to investigate the potential link between isotretinoin and suicidal risk; the EFEMERIS cohort that assesses drugs prescriptions in French pregnant women who delivered in the Haute-Garonne region; the EPI-GETB-AM study that derived a SNIIRAM/SNDS-based algorithm to identify venous thromboembolism and linked SNIIRAM/SNDS to the EPI-GETBO-III survey for validation. Another perspective of SNDS enrichment is clinical trials' data for medico-economic assessment, and extended follow-up without attrition bias. Linkage is not straightforward. Apart from regulatory approbation and authorized data center issues, which could be solved by the Health Data Hub Initiative, a multidisciplinary team with medical, pharmacological and methodological knowledge, as well as with technical skills is essential to handle the whole process.

A visual approach of care pathways from the French nationwide SNDS database - from population to individual records: the ePEPS toolbox.

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 Système National des Données de Santé is a great opportunity to carry out comprehensive health studies at the country level. However, using this database is far from being straightforward for nonexpert data scientists; so, dedicated tools needed to be developed. Our contribution is focused on data management and visualization tools able to query and cope with the complexity of care pathways of patients. The scope of the toolbox presented below goes from interactive tools for building groups of patients to timeline representations of individual patient healthcare trajectories.

From medico-administrative databases analysis to care trajectories analytics: an example with the French SNDS.

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 healthcare trajectories to provide a medical view of raw data. A data abstraction process enables the clinician to have an intuitive medical view of raw data and to design a study-specific view. This view is based on a generic model of care trajectory, that is a sequence of time stamped medical events for a given patient. This model is combined with tools to manipulate care trajectories efficiently.