Integrating new approaches to atrial fibrillation management: the 6th AFNET/EHRA Consensus Conference.

There are major challenges ahead for clinicians treating patients with atrial fibrillation (AF). The population with AF is expected to expand considerably and yet, apart from anticoagulation, therapies used in AF have not been shown to consistently impact on mortality or reduce adverse cardiovascular events. New approaches to AF management, including the use of novel technologies and structured, integrated care, have the potential to enhance clinical phenotyping or result in better treatment selection and stratified therapy. Here, we report the outcomes of the 6th Consensus Conference of the Atrial Fibrillation Network (AFNET) and the European Heart Rhythm Association (EHRA), held at the European Society of Cardiology Heart House in Sophia Antipolis, France, 17-19 January 2017. Sixty-two global specialists in AF and 13 industry partners met to develop innovative solutions based on new approaches to screening and diagnosis, enhancing integration of AF care, developing clinical pathways for treating complex patients, improving stroke prevention strategies, and better patient selection for heart rate and rhythm control. Ultimately, these approaches can lead to better outcomes for patients with AF

Algorithms for Extracting Structured Motifs Using a Suffix Tree With an Application to Promoter and Regulatory Site Consensus Identification

This paper introduces two exact algorithms for extracting conserved structured motifs from a set of DNA sequences. Structured motifs may be described as an ordered collection of p # 1 "boxes" (each box corresponding to one part of the structured motif), p substitution rates (one for each box) and p - 1 intervals of distance (one for each pair of successive boxes in the collection). The contents of the boxes -- that is, the motifs themselves -- are unknown at the start of the algorithm. This is precisely what the algorithms are meant to find. A suffix tree is used for finding such motifs. The algorithms are efficient enough to be able to infer site consensi, such as, for instance, promoter sequences or regulatory sites, from a set of unaligned sequences corresponding to the non coding regions upstream from all genes of a genome. In particular, both algorithms time complexity scales linearly with N 2 n where n is the average length of the sequences and N their number. An application t..

Inférence de motifs structurés (algorithmes et outils appliqués à la détection de sites de fixation dans le séquences génomiques)

Au cours de ce travail, nous nous sommes particulièrement intéressés à un problème de biologie moléculaire: la détection de sites de fixation dans des séquences d'ADN. Ce problème, perçu sous un certain angle, trouve des solutions variées grâce aux travaux réalisés en algorithmique du texte. Après une présentation des spécificités de ces sites, nous passons en revue les représentations informatiques existantes utilisées pour les modéliser. Puis, nous faisons état des diffe rents travaux algorithmiques effectués dans le domaine de leur détection. La pertinence des principales approches est discutée. Nous essayons en particulier de présenter les différents aspects du compromis qui paraît inévitable entre sensibilité et complexité quand on traite un tel problème. Notre apport consiste ensuite à développer une nouvelle représentation pour les sites de fixation. Celle-ci prend en compte une caractéristique de certains d'entre eux: leur capacité à s'associer sous certaines contraintes. Nous introduisons la notion de modèle structuré, et développons plusieurs algorithmes combinatoires exacts de détection de tels modèles. Nous présentons ensuite l'outil que nous avons conçu à partir de ces algorithmes, dénommé SMILE. Nous ramenant au problème biologique qui a motivé ces travaux algorithmiques, nous appliquons cet outil à l'inférence de sites de fixation connus et inconnus dans des jeux de séquences nucléiques expérimentaux ou directement issus de génomes complets. Les résultats de ces expériences sont comparés avec ceux qu'obtiennent certains outils couramment utilisés sur les mêmes jeux de données, et leur pertinence biologique est discutée. Pour finir, nous jugeons l'apport des modèles structurés et esquissons plusieurs directions à explorer pour améliorer la détection de sites de fixation. Les représentations, algorithmes et outils développés dans cette thèse sont généraux, et peuvent donc être appliqués à l'extraction de tous types de signaux structurés et approchés, communs à plusieurs séquences. En particulier, ils peuvent d'ores et déjà être utilisés pour inférer des motifs dans des séquences protéiquesIn this work, we concentrated our interest on a problem in molecular biology: the detection of binding sites in DNA sequences. This problem, from a certain point of view, finds diverse solutions in text algorithmics. We first present the specificity of such sites, and look at the existing representations used to modelize them. We then review the existing algorithms aimed at detecting these sites, and try to evaluate the pertinence of the main approaches employed. In particular, we try to discuss the trade off that exists between sensibility and complexity when dealing with such a problem. Our work consists in developing a new representation for binding sites, which incorporates one of the main characteristics of some of them: their ability to appear associated in a more or less constrained manner. We introduce the notion of a structured model, develop several exact combinatorial algorithms to infer such models, and present the software, called SMILE, we made using these algorithms. Going back to the biological problem which motivated this work, we apply our tool to infer known and unknown binding sites in a few sets of DNA sequences. The results of these experiments are compared to those obtained by some of the most used software on the same sets of sequences. We then discuss of the biological pertinence of all these results. Finally, we try to place our work in the current context, and define several directions to explore to improve the inference of binding sites. The algorithms and tools we made are all generic, they can be applied to extract any kind of structured signals that are common to a set of sequences. In particular, they can already treat protein sequencesPARIS-EST Marne-la-Vallee-BU (774682101) / SudocSudocFranceF

Infering regulatory elements from a whole genome. An application to the analysis of genome of \itemize{Helicobacter Pylori} σ80\sigma_{80} family of promoter signals

International audienc

Co-lethality studied as an asset against viral drug escape: the HIV protease case

International audienceBackground: Co-lethality, or synthetic lethality is the documented genetic situation where two, separately non-lethal mutations, become lethal when combined in one genome. Each mutation is called a "synthetic lethal" (SL) or a co-lethal. Like invariant positions, SL sets (SL linked couples) are choice targets for drug design against fast-escaping RNA viruses: mutational viral escape by loss of affinity to the drug may induce (synthetic) lethality.Results: From an amino acid sequence alignment of the HIV protease, we detected the potential SL couples, potential SL sets, and invariant positions. From the 3D structure of the same protein we focused on the ones that were close to each other and accessible on the protein surface, to possibly bind putative drugs. We aligned 24,155 HIV protease amino acid sequences and identified 290 potential SL couples and 25 invariant positions. After applying the distance and accessibility filter, three candidate drug design targets of respectively 7 (under the flap), 4 (in the cantilever) and 5 (in the fulcrum) amino acid positions were found.Conclusions: These three replication-critical targets, located outside of the active site, are key to our anti-escape strategy. Indeed, biological evidence shows that 2/3 of those target positions perform essential biological functions. Their mutational variations to escape antiviral medication could be lethal, thus limiting the apparition of drug-resistant strains

RISOTTO: Fast Extraction of Motifs with Mismatches

We present in this paper an exact algorithm for motif extraction. Efficiency is achieved by means of an improvement in the algorithm and data structures that applies to the whole class of motif inference algorithms based on suffix trees. An average case complexity analysis shows a gain over the best known exact algorithm for motif extraction, when applied to extract long motifs. A full implementation was developed and made available online. Experimental results show that the proposed algorithm is more than two times faster than the best known exact algorithm for motif extraction, confirming in this way the theoretical results obtained