Biology subjects

Poulain, P.

Publications and source records attributed to Poulain, P..

6 recordsLinked to original sources

bioRxiv2026

Hi-C data from the filamentous fungus Podospora anserina and associated 3D models to visualize the spatial organization of its chromosomes

ObjectivesModel species are essential for fundamental research in biology. While a complete genomic sequence is a prerequisite for genetic studies, it is not enough on its own. Understanding the three-dimensional organization of the genome is also important, allowing researchers to gain a more realistic understanding of the mechanisms governing genome function. In the fungal model Podospora anserina, although the genomic sequence has been established for a long time, the three-dimensional organization remained unknown. Here we obtained the first Hi-C datasets and present associated 3D models, providing the research community with a valuable resource for better multi-omics data integration. Data descriptionHi-C experiments were performed in duplicate, using nuclei purified from wild-type fungal mycelium. Four FASTQ files were obtained (two per replicate) and used as inputs for the 3DGB workflow with four different output resolutions, to observe the genome organization of P. anserina at different levels of detail (50 kb, 20 kb, 10 kb, and 5 kb). In a context where researchers already have, for this species, a large amount of traditional omics data (ChIP-seq, RNA-seq, etc.), these 3D models are helpful for complementing the linear representation of the genome, which is traditionally used in bioinformatic analyses.

genomics↗

bioRxiv2025

Fast parameterization of Martini3 models for fragments and small molecules

Coarse-grained molecular dynamics simulations, such as those performed with the recently parametrized Martini 3 force field, simplify molecular models and enable the study of larger systems over longer timescales. With this new implementation, Martini 3 allows more bead types and sizes, becoming more amenable to study dynamical phenomena involving small molecules such as protein-ligand interactions and membrane permeation. However, while there were some solutions to automatically model small molecules using the previous iteration of Martini force field, there is no simple way to generate such molecules for Martini 3 yet. Here, we introduce Auto-MartiniM3, an advanced and updated version of the Auto-Martini program, designed to automate the coarse-graining of small molecules to be used with the Martini 3 force field. We validated our approach by modeling 81 small molecules from the Martini Database and comparing their structural and thermodynamic properties with ones obtained from models designed by Martini experts. Additionally, we assessed the behavior of Auto-MartiniM3-generated models by calculating solute translocation and free energy across lipid bilayers. We also evaluated more complex molecules such as caffeine by testing its binding to the adenosine A2A receptor. Finally, our results from deploying Auto-MartiniM3 on a large dataset of molecular fragments demonstrate that this program can become a tool of choice for fast high-throughput creation of coarse-grained models of small molecules, offering a good balance between automation and accuracy. Auto-MartiniM3 source code is freely available at https://github.com/Martini-Force-Field-Initiative/Automartini_M3

bioinformatics↗

bioRxiv2023

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD [fi]les in generalist data repositories, constituting the dark matter of MD -- data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 [fi]les and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on [fi]les produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identi[fi]ed systems with speci[fi]c molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

bioinformatics↗

bioRxiv2023

Visual integration of omics data to improve 3D models of fungal chromosomes

The functions of eukaryotic chromosomes and their spatial architecture in the nucleus are reciprocally dependent. Hi-C experiments are routinely used to study chromosome 3D organization by probing chromatin interactions. Standard representation of the data has relied on contact maps that show the frequency of interactions between parts of the genome. In parallel, it has become easier to build 3D models of the entire genome based on the same Hi-C data, and thus benefit from the methodology and visualization tools developed for structural biology. 3D modeling of entire genomes leverages the understanding of their spatial organization. However, this opportunity for original and insightful modeling is under exploited. In this paper, we show how seeing the spatial organization of chromosomes can bring new perspectives to Hi-C data analysis. We assembled state-of-the-art tools into a workflow that goes from Hi-C raw data to fully annotated 3D models and we re-analysed public Hi-C datasets available for three fungal species. Besides the well-described properties of the spatial organization of their chromosomes (Rabl conformation, hypercoiling and chromosome territories), our 3D models highlighted i) in Saccharomyces cerevisiae, the backbones of the cohesin anchor regions, which were aligned all along the chromosomes, ii) in Schizosaccharomyces pombe, the oscillations of the coiling of chromosome arms throughout the cell cycle and iii) in Neurospora crassa, the massive relocalization of histone marks in mutants of heterochromatin regulators. 3D modeling of the chromosomes brings new opportunities for visual integration. This holistic perspective supports intuition and lays the foundation for building new concepts.

genomics↗

bioRxiv2018

Pixel: a digital lab assistant to integrate biological data in multi-omics projects

BackgroundIn biology, high-throughput experimental technologies, also referred as "omics" technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project. MethodsThe Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies. ResultsThe Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.

bioinformatics↗

bioRxiv2017

PBxplore: A Tool To Analyze Local Protein Structure And Deformability With Protein Blocks

Proteins are highly dynamic macromolecules. A classical way to analyze their inner flexibility is to perform molecular dynamics simulations. In this context, we present the advantage to use small structural prototypes, namely the Protein Blocks (PBs). PBs give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, they allow analyzes of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is a suite of tools to analyze the dynamics and deformability of protein structures using PBs. It is able to process large amount of data such as those produced by molecular dynamics simulations. It produces various outputs with text and graphics, such as frequencies, entropy and information logo. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.

bioinformatics↗