MS4 - Multi-Scale Selector of Sequence Signatures: An alignment-free method for classification of biological sequences

International audienc

Tissue Resources for the Functional Annotation of Animal Genomes

In order to generate an atlas of the functional elements driving genome expression in domestic animals, the Functional Annotation of Animal Genome (FAANG) strategy was to sample many tissues from a few animals of different species, sexes, ages, and production stages. This article presents the collection of tissue samples for four species produced by two pilot projects, at INRAE (National Research Institute for Agriculture, Food and Environment) and the University of California, Davis. There were three mammals (cattle, goat, and pig) and one bird (chicken). It describes the metadata characterizing these reference sets (1) for animals with origin and selection history, physiological status, and environmental conditions; (2) for samples with collection site and tissue/cell processing; (3) for quality control; and (4) for storage and further distribution. Three sets are identified: set 1 comprises tissues for which collection can be standardized and for which representative aliquots can be easily distributed (liver, spleen, lung, heart, fat depot, skin, muscle, and peripheral blood mononuclear cells); set 2 comprises tissues requiring special protocols because of their cellular heterogeneity (brain, digestive tract, secretory organs, gonads and gametes, reproductive tract, immune tissues, cartilage); set 3 comprises specific cell preparations (immune cells, tracheal epithelial cells). Dedicated sampling protocols were established and uploaded in https://data.faang.org/protocol/samples. Specificities between mammals and chicken are described when relevant. A total of 73 different tissues or tissue sections were collected, and 21 are common to the four species. Having a common set of tissues will facilitate the transfer of knowledge within and between species and will contribute to decrease animal experimentation. Combining data on the same samples will facilitate data integration. Quality control was performed on some tissues with RNA extraction and RNA quality control. More than 5,000 samples have been stored with unique identifiers, and more than 4,000 were uploaded onto the Biosamples database, provided that standard ontologies were available to describe the sample. Many tissues have already been used to implement FAANG assays, with published results. All samples are available without restriction for further assays. The requesting procedure is described. Members of FAANG are encouraged to apply a range of molecular assays to characterize the functional status of collected samples and share their results, in line with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles

Des techniques mathematiques aux outils numeriques pour l'etude d'objets fractals

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

A deep neural network method for analyzing the CMS HGCal events

For the High Luminosity LHC, the CMS Collaboration made the ambitious choice of a high granularity design to replace the existing endcap calorimeters. The thousands of particles coming from the multiple interactions create showers in the calorimeters, depositing energy simultaneously in adjacent cells. The data are analog to 3D gray-scale image that should be properly reconstructed. In this paper, we will investigate how to localize and identify the thousands of showers in such events with a Deep Neural Network model. This problem is well-known in the Vision domain, it belongs to the challenging class Object Detection. Our project presents a lot of similarities with the ones treated in Industry but accumulates several technological challenges like the 3D treatment. We will present the Mask R-CNN model which has already proven its efficiency in Industry (for 2D images). To conclude we will present the first results of this challenge and how we plan to extend it to tackle 3D HGCAL data

Hybrid implementation of the VEGAS Monte-Carlo algorithm

This Conference focuses on the application of GPUs in High Energy Physics (HEP), expanding on the trend of previous workshops on the topic and pointing to establishing a recurrent series. All different applications of massively parallel computing in HEP will be addressed, from computational speed-ups in online and offline data selection and analysis to hard real-time applications in low-level triggering, to Monte Carlo simulations for lattice QCD. Both current activities and plans on foreseen experiments and projects will be discussed, together with perspectives on the evolution of the hardware and software

From Interactive to Immersive Molecular Dynamics

International audienceMolecular Dynamics simulations are nowadays routinely used to complement experimental studies and overcome some of their limitations. In particular, current experimental techniques do not allow to directly observe the full dynamics of a macromolecule at atomic detail. Molecular simulation engines provide time-dependent atomic positions, velocities and system energies according to biophysical models. Many molecular simulation engines can now compute a molecular dynamics trajectory of interesting biological systems in interactive time. This progress has lead to a new approach called Interactive Molecular Dynamics. It allows the user to control and visualise a molecular simulation in progress. We have developed a generic library, called MDDriver, in order to facilitate the implementation of such interactive simulations. It allows one to easily create a network connection between a molecular user interface and a physically-based simulation. We use this library in order to study a biomolecular system, simulated by various interaction-enabled molecular engines and models. We use a classical molecular visualisation tool and a haptic device to control the dynamic behavior of the molecule. This approach provides encouraging results for interacting with a biomolecule and understanding its dynamics. Starting from this initial success, we decided to use Virtual Reality (VR) functionalities more intensively, by designing a VR framework dedicated to immersive and interactive molecular simulations. This framework is based on MDDriver, on the visualisation toolkit VTK, and on the vtkVRPN library, which encapsulates the VRPN library into VTK

A VR framework for interacting with molecular simulations

International audienceMolecular Dynamics is nowadays routinely used to complement experimental studies and overcome some of their limitations. In particular, current experimental techniques do not allow to directly observe the full dynamics of a macromolecule at atomic detail. Molecular simulation provides time-dependent atomic positions, velocities and system energies according to biophysical models. Many molecular simulation engines can now compute a molecular dynamics trajectory of interesting biological systems in interactive time. This progress has lead to a new approach called interactive molecular dynamics. It allows to control and visualise a molecular simulation in progress. We have developed a generic library, called MDDriver, to facilitate the implementation of such interactive simulations. It allows to easily create a network between a molecular user interface and a physically-based simulation. We use this library in order to study an interesting biomolecular system, simulated by various interaction-enabled molecular engines and models. We use a classical molecular visualisation tool and a haptic device to control the dynamic behavior of the molecule. This approach provides encouraging results for interacting with a biomolecule and understanding its dynamics. Starting from this initial success, we decided to use VR functionalities more intensively, by designing a VR framework dedicated to immersive and interactive molecular simulations. This framework is based on MDDriver, on the visualisation toolkit VTK, and on the vtkVRPN library, which encapsulates the VRPN library into VTK

SIMoNe: Statistical Inference for MOdular NEtworks.

International audienceSUMMARY: The R package SIMoNe (Statistical Inference for MOdular NEtworks) enables inference of gene-regulatory networks based on partial correlation coefficients from microarray experiments. Modelling gene expression data with a Gaussian graphical model (hereafter GGM), the algorithm estimates non-zero entries of the concentration matrix, in a sparse and possibly high-dimensional setting. Its originality lies in the fact that it searches for a latent modular structure to drive the inference procedure through adaptive penalization of the concentration matrix. AVAILABILITY: Under the GNU General Public Licence at http://cran.r-project.org/web/packages/simone