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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cyril Matthey-Doret; Lyam Baudry; Axel Breuer; Rémi Montagne; +12 Authors

    Chromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genomes, including bacteria, viruses, yeasts and mammals. Our method does not require any prior training dataset and works well with default parameters on data generated with various protocols. Chromatin loops bridging distant loci within chromosomes can be detected by a variety of techniques such as Hi-C. Here the authors present Chromosight, an algorithm applied on mammalian, bacterial, viral and yeast genomes, able to detect various types of pattern in chromosome contact maps, including chromosomal loops.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Nature Communication...arrow_drop_down
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    Nature Communications
    Other literature type . Article . 2020 . Peer-reviewed
    License: CC BY
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    Europe PubMed Central
    Article . 2020 . Peer-reviewed
    Data sources: PubMed Central
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    Nature Communications
    Article . Preprint
    License: CC BY
    Data sources: UnpayWall
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    Nature Communications
    Article . 2020
    Data sources: DOAJ-Articles
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jon Ison; Hans Ienasescu; Piotr Jaroslaw Chmura; Emil Karol Rydza; +32 Authors

    Bioinformaticians and biologists rely increasingly upon workflows for the flexible utilization of the many life science tools that are needed to optimally convert data into knowledge. We outline a pan-European enterprise to provide a catalogue ( https://bio.tools ) of tools and databases that can be used in these workflows. bio.tools not only lists where to find resources, but also provides a wide variety of practical information. Contains fulltext : 208582.pdf (Publisher’s version ) (Open Access)

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    DOAJ-Articles
    Article . 2019
    Data sources: DOAJ-Articles
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    Bergen Open Research Archive - UiB
    Article . 2019 . Peer-reviewed
    License: CC BY
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    Genome Biology
    Article
    License: CC BY
    Data sources: UnpayWall
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    Radboud Repository
    Article . 2019
    Data sources: Radboud Repository
    DOAJ
    Article . 2019
    Data sources: DOAJ
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hanna Julienne; Huwenbo Shi; Bogdan Pasaniuc; Hugues Aschard;

    Abstract Motivation Multi-trait analyses using public summary statistics from genome-wide association studies (GWASs) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. Although methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses. Results We fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for variants of all effect sizes on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel. Availability and implementation The python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/. Supplementary information Supplementary data are available at Bioinformatics online.

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    Bioinformatics
    Article
    Data sources: UnpayWall
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    Europe PubMed Central
    Other literature type . 2019
    Data sources: PubMed Central
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    https://www.biorxiv.org/conten...
    Preprint
    License: CC BY NC ND
    Data sources: UnpayWall
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    Bioinformatics
    Article . 2019 . Peer-reviewed
    License: OUP Standard Publication Reuse
    Data sources: Crossref
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      Bioinformatics
      Article
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      Europe PubMed Central
      Other literature type . 2019
      Data sources: PubMed Central
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      https://www.biorxiv.org/conten...
      Preprint
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      Data sources: UnpayWall
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      Bioinformatics
      Article . 2019 . Peer-reviewed
      License: OUP Standard Publication Reuse
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thibord, Florian; Perret, Claire; Roux, Maguelonne; Suchon, Pierre; +4 Authors

    AbstractNext-generation sequencing is an increasingly popular and efficient approach to characterize the full set of microRNAs (miRNAs) present in human biosamples. MiRNAs’ detection and quantification still remain a challenge as they can undergo different post transcriptional modifications and might harbor genetic variations (polymiRs) that may impact on the alignment step. We present a novel algorithm, OPTIMIR, that incorporates biological knowledge on miRNA editing and genome-wide genotype data available in the processed samples to improve alignment accuracy.OPTIMIR was applied to 391 human plasma samples that had been typed with genome-wide genotyping arrays. OPTIMIR was able to detect genotyping errors, suggested the existence of novel miRNAs and highlighted the allelic imbalance expression of polymiRs in heterozygous carriers.OPTIMIR is written in python, and freely available on the GENMED website (http://www.genmed.fr/index.php/fr/) and on Github (github.com/FlorianThibord/OptimiR).

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    Europe PubMed Central
    Other literature type . 2019
    Data sources: PubMed Central
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    Authors: Antoine Godichon-Baggioni; Cathy Maugis-Rabusseau; Andrea Rau;

    arXiv - Mathematics - Statistics Theory; Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering composi- tional data (i.e., data made up of profiles, whose rows belong to the simplex) re- mains largely unexplored in cases where the observed value of an observation is equal or close to zero for one or more samples. This work is motivated by the analysis of two sets of compositional data, both focused on the categorization of profiles but arising from considerably different applications: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental con- ditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib’ bicycle sharing system in Paris, France. For both of these ap- plications, we focus on the use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension we propose called the Log Centered Log Ratio, in conjunction with the K -means algorithm. We use a nonasymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters present in the data. Finally, we illus- trate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq , on both the gene expression and bicycle sharing system data.

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    figshare
    Other literature type . 2018
    License: CC BY
    Data sources: Datacite
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    Other literature type . 2018
    License: CC BY
    Data sources: Datacite
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    ProdInra
    External research report . 2018
    License: CC BY
    Data sources: ProdInra
    https://doi.org/10.48550/arxiv...
    Article . 2017
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
    Journal of Applied Statistics
    Article . 2018 . Peer-reviewed
    Data sources: Crossref
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    Authors: Schuemann, J.; McNamara, A.L.; Ramos-Méndez, J.; Perl, J.; +4 Authors

    International audience; The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Other literature type . 2019
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    Radiation Research
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    Oskar Bordeaux
    Article . 2018
    Data sources: Oskar Bordeaux
    Radiation Research
    Article . 2018 . Peer-reviewed
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      Radiation Research
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      Oskar Bordeaux
      Article . 2018
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      Radiation Research
      Article . 2018 . Peer-reviewed
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    Authors: Chaix, Estelle; Deléger, Louise; Bossy, Robert; Nédellec, Claire;

    Introduction Information on food microbial biodiversity is scattered across millions of scientific papers (2 million references in the PubMed bibliographic database in 2017). It is impossible to manually achieve an exhaustive analysis of these documents. Text-mining and knowledge engineering methods can assist the researcher in finding relevant information. Material & MethodsWe propose to study bacterial biodiversity using text-mining tools from the Alvis platform. First, we analyzed terms that designate Microbial and Habitat entities in text. Microorganism names were predicted using the NCBI taxonomy. Habitat entities were detected using the syntactic structure of the terms and the OntoBiotope ontology. This ontology has been specifically enriched for the recognition of food terms in text. In a second time, we predicted links between microorganisms and their habitats (labeled “Lives_in” relationships) using pattern and machine-learning based methods. The results of text-mining predictions are indexed and presented in a semantic search engine. Result The AlvisIR search engine for microbe literature gives online access to 1.2 million PubMed abstracts in 2015, among which 13% are specific to food. This tool makes it possible to use text-mining results to search for information on bacterial biodiversity. It covers all types of microbial habitats to help understand the origin of microbial presence in food. Significance This work presents the first semantic search engine dedicated to better understand microbial food biodiversity from text.

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    Other literature type . 2019
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    https://doi.org/10.1016/j.fm.2...
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    Authors: van Rijswijk, M; van Rijswijk, M; Beirnaert, C; Beirnaert, C; +50 Authors

    Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases. The meeting was funded by PhenoMeNal, European Commission's Horizon2020 programme, grant agreement number 654241 Sí

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    REPISALUD
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    Article . 2017
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    Apollo
    Article . 2018
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    Article . 2017
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    Other literature type . 2017
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    Article . 2017 . Peer-reviewed
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      Apollo
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      Apollo
      Other literature type . 2017
      License: CC BY
      Data sources: Apollo
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      F1000Research
      Article . 2017 . Peer-reviewed
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    Authors: Brysbaert, Guillaume; Lorgouilloux, Kevin; Vranken, Wim F; Lensink, Marc F;

    Motivation Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments. Results We combined centrality analyses with DynaMine flexibility predictions in a Cytoscape app called RINspector. The app performs centrality analyses and directly visualizes the results on a graph of predicted residue flexibility. In addition, the effect of mutations on local flexibility can be calculated. info:eu-repo/semantics/published SCOPUS: ar.j

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    Lille Open Archive
    Article . 2017
    Data sources: Lille Open Archive
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    DI-fusion
    Article . 2018 . Peer-reviewed
    Data sources: DI-fusion
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Lille Open Archive
      Article . 2017
      Data sources: Lille Open Archive
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DI-fusion
      Article . 2018 . Peer-reviewed
      Data sources: DI-fusion
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aite, Méziane; Chevallier, Marie; Frioux, Clémence; Trottier, Camille; +11 Authors

    Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway. Author summary Genome-scale metabolic models describe an organism’s metabolism. Building good models requires the integration of all relevant available information, obtained by exploring different data types and biological databases. This process is not straightforward and choices are made along the way, for example, which data is analyzed, with what tools. It matters that all reconstruction steps are well documented so that models can be fully exploited by the community. Having this metadata allows other researchers to reproduce, improve or reuse a model as a blueprint to create new ones. Sadly, this information is usually scattered and its proper distribution is the exception rather than the norm when using “à la carte” pipelines that combine main platforms and individual tools. We created a platform for “à la carte” metabolic model generation that responds to the need of transparency and data-connection in the field. It includes a battery of tools to exploit heterogeneous data through customizable pipelines. At each step, relevant information is stored, ensuring reproducibility and documentation of processes. Furthermore, exploration of models and metadata during the reconstruction process is facilitated through the automatic generation of local wikis. This view offers a user-friendly solution to iteratively explore genome-scale metabolic models produced with personalized pipelines and poorly interoperable tools. We highlight these benefits by building models for organisms with various input data. Among them, we show why the combination of heterogeneous information is necessary to elucidate specificities of Tisochrysis lutea, a eukaryotic microalga, for anti-oxidant production.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Cyril Matthey-Doret; Lyam Baudry; Axel Breuer; Rémi Montagne; +12 Authors

    Chromosomes of all species studied so far display a variety of higher-order organisational features, such as self-interacting domains or loops. These structures, which are often associated to biological functions, form distinct, visible patterns on genome-wide contact maps generated by chromosome conformation capture approaches such as Hi-C. Here we present Chromosight, an algorithm inspired from computer vision that can detect patterns in contact maps. Chromosight has greater sensitivity than existing methods on synthetic simulated data, while being faster and applicable to any type of genomes, including bacteria, viruses, yeasts and mammals. Our method does not require any prior training dataset and works well with default parameters on data generated with various protocols. Chromatin loops bridging distant loci within chromosomes can be detected by a variety of techniques such as Hi-C. Here the authors present Chromosight, an algorithm applied on mammalian, bacterial, viral and yeast genomes, able to detect various types of pattern in chromosome contact maps, including chromosomal loops.

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    Nature Communications
    Other literature type . Article . 2020 . Peer-reviewed
    License: CC BY
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    Europe PubMed Central
    Article . 2020 . Peer-reviewed
    Data sources: PubMed Central
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    Nature Communications
    Article . Preprint
    License: CC BY
    Data sources: UnpayWall
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    Nature Communications
    Article . 2020
    Data sources: DOAJ-Articles
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Jon Ison; Hans Ienasescu; Piotr Jaroslaw Chmura; Emil Karol Rydza; +32 Authors

    Bioinformaticians and biologists rely increasingly upon workflows for the flexible utilization of the many life science tools that are needed to optimally convert data into knowledge. We outline a pan-European enterprise to provide a catalogue ( https://bio.tools ) of tools and databases that can be used in these workflows. bio.tools not only lists where to find resources, but also provides a wide variety of practical information. Contains fulltext : 208582.pdf (Publisher’s version ) (Open Access)

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    DOAJ-Articles
    Article . 2019
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    Bergen Open Research Archive - UiB
    Article . 2019 . Peer-reviewed
    License: CC BY
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    Genome Biology
    Article
    License: CC BY
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    Radboud Repository
    Article . 2019
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    Article . 2019
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    Authors: Hanna Julienne; Huwenbo Shi; Bogdan Pasaniuc; Hugues Aschard;

    Abstract Motivation Multi-trait analyses using public summary statistics from genome-wide association studies (GWASs) are becoming increasingly popular. A constraint of multi-trait methods is that they require complete summary data for all traits. Although methods for the imputation of summary statistics exist, they lack precision for genetic variants with small effect size. This is benign for univariate analyses where only variants with large effect size are selected a posteriori. However, it can lead to strong p-value inflation in multi-trait testing. Here we present a new approach that improve the existing imputation methods and reach a precision suitable for multi-trait analyses. Results We fine-tuned parameters to obtain a very high accuracy imputation from summary statistics. We demonstrate this accuracy for variants of all effect sizes on real data of 28 GWAS. We implemented the resulting methodology in a python package specially designed to efficiently impute multiple GWAS in parallel. Availability and implementation The python package is available at: https://gitlab.pasteur.fr/statistical-genetics/raiss, its accompanying documentation is accessible here http://statistical-genetics.pages.pasteur.fr/raiss/. Supplementary information Supplementary data are available at Bioinformatics online.

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    Bioinformatics
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    Europe PubMed Central
    Other literature type . 2019
    Data sources: PubMed Central
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    https://www.biorxiv.org/conten...
    Preprint
    License: CC BY NC ND
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    Bioinformatics
    Article . 2019 . Peer-reviewed
    License: OUP Standard Publication Reuse
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      Bioinformatics
      Article . 2019 . Peer-reviewed
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    Authors: Thibord, Florian; Perret, Claire; Roux, Maguelonne; Suchon, Pierre; +4 Authors

    AbstractNext-generation sequencing is an increasingly popular and efficient approach to characterize the full set of microRNAs (miRNAs) present in human biosamples. MiRNAs’ detection and quantification still remain a challenge as they can undergo different post transcriptional modifications and might harbor genetic variations (polymiRs) that may impact on the alignment step. We present a novel algorithm, OPTIMIR, that incorporates biological knowledge on miRNA editing and genome-wide genotype data available in the processed samples to improve alignment accuracy.OPTIMIR was applied to 391 human plasma samples that had been typed with genome-wide genotyping arrays. OPTIMIR was able to detect genotyping errors, suggested the existence of novel miRNAs and highlighted the allelic imbalance expression of polymiRs in heterozygous carriers.OPTIMIR is written in python, and freely available on the GENMED website (http://www.genmed.fr/index.php/fr/) and on Github (github.com/FlorianThibord/OptimiR).

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    Europe PubMed Central
    Other literature type . 2019
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    Authors: Antoine Godichon-Baggioni; Cathy Maugis-Rabusseau; Andrea Rau;

    arXiv - Mathematics - Statistics Theory; Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering composi- tional data (i.e., data made up of profiles, whose rows belong to the simplex) re- mains largely unexplored in cases where the observed value of an observation is equal or close to zero for one or more samples. This work is motivated by the analysis of two sets of compositional data, both focused on the categorization of profiles but arising from considerably different applications: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental con- ditions; and (2) finding patterns in the usage of stations over the course of one week in the Velib’ bicycle sharing system in Paris, France. For both of these ap- plications, we focus on the use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension we propose called the Log Centered Log Ratio, in conjunction with the K -means algorithm. We use a nonasymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters present in the data. Finally, we illus- trate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq , on both the gene expression and bicycle sharing system data.

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    ProdInra
    External research report . 2018
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    https://doi.org/10.48550/arxiv...
    Article . 2017
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
    Journal of Applied Statistics
    Article . 2018 . Peer-reviewed
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    Authors: Schuemann, J.; McNamara, A.L.; Ramos-Méndez, J.; Perl, J.; +4 Authors

    International audience; The TOPAS Monte Carlo (MC) system is used in radiation therapy and medical imaging research, having played a significant role in making Monte Carlo simulations widely available for proton therapy related research. While TOPAS provides detailed simulations of patient scale properties, the fundamental unit of the biological response to radiation is a cell. Thus, our goal was to develop TOPAS-nBio, an extension of TOPAS dedicated to advance understanding of radiobiological effects at the (sub-)cellular, (i.e., the cellular and sub-cellular) scale. TOPAS-nBio was designed as a set of open source classes that extends TOPAS to model radiobiological experiments. TOPAS-nBio is based on and extends Geant4-DNA, which extends the Geant4 toolkit, the basis of TOPAS, to include very low-energy interactions of particles down to vibrational energies, explicitly simulates every particle interaction (i.e., without using condensed histories) and propagates radiolysis products. To further facilitate the use of TOPAS-nBio, a graphical user interface was developed. TOPAS-nBio offers full track-structure Monte Carlo simulations, integration of chemical reactions within the first millisecond, an extensive catalogue of specialized cell geometries as well as sub-cellular structures such as DNA and mitochondria, and interfaces to mechanistic models of DNA repair kinetics. We compared TOPAS-nBio simulations to measured and published data of energy deposition patterns and chemical reaction rates (G values). Our simulations agreed well within the experimental uncertainties. Additionally, we expanded the chemical reactions and species provided in Geant4-DNA and developed a new method based on independent reaction times (IRT), including a total of 72 reactions classified into 6 types between neutral and charged species. Chemical stage simulations using IRT were a factor of 145 faster than with step-by-step tracking. Finally, we applied the geometric/chemical modeling to obtain initial yields of double-strand breaks (DSBs) in DNA fibers for proton irradiations of 3 and 50 MeV and compared the effect of including chemical reactions on the number and complexity of DSB induction. Over half of the DSBs were found to include chemical reactions with approximately 5% of DSBs caused only by chemical reactions. In conclusion, the TOPAS-nBio extension to the TOPAS MC application offers access to accurate and detailed multiscale simulations, from a macroscopic description of the radiation field to microscopic description of biological outcome for selected cells. TOPAS-nBio offers detailed physics and chemistry simulations of radiobiological experiments on cells simulating the initially induced damage and links to models of DNA repair kinetics.

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    Radiation Research
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    Oskar Bordeaux
    Article . 2018
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    Radiation Research
    Article . 2018 . Peer-reviewed
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      Radiation Research
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      Oskar Bordeaux
      Article . 2018
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      Radiation Research
      Article . 2018 . Peer-reviewed
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    Authors: Chaix, Estelle; Deléger, Louise; Bossy, Robert; Nédellec, Claire;

    Introduction Information on food microbial biodiversity is scattered across millions of scientific papers (2 million references in the PubMed bibliographic database in 2017). It is impossible to manually achieve an exhaustive analysis of these documents. Text-mining and knowledge engineering methods can assist the researcher in finding relevant information. Material & MethodsWe propose to study bacterial biodiversity using text-mining tools from the Alvis platform. First, we analyzed terms that designate Microbial and Habitat entities in text. Microorganism names were predicted using the NCBI taxonomy. Habitat entities were detected using the syntactic structure of the terms and the OntoBiotope ontology. This ontology has been specifically enriched for the recognition of food terms in text. In a second time, we predicted links between microorganisms and their habitats (labeled “Lives_in” relationships) using pattern and machine-learning based methods. The results of text-mining predictions are indexed and presented in a semantic search engine. Result The AlvisIR search engine for microbe literature gives online access to 1.2 million PubMed abstracts in 2015, among which 13% are specific to food. This tool makes it possible to use text-mining results to search for information on bacterial biodiversity. It covers all types of microbial habitats to help understand the origin of microbial presence in food. Significance This work presents the first semantic search engine dedicated to better understand microbial food biodiversity from text.

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    https://doi.org/10.1016/j.fm.2...
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    Authors: van Rijswijk, M; van Rijswijk, M; Beirnaert, C; Beirnaert, C; +50 Authors

    Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases. The meeting was funded by PhenoMeNal, European Commission's Horizon2020 programme, grant agreement number 654241 Sí

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    F1000Research
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    REPISALUD
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    F1000Research
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    Apollo
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    F1000Research
    Article . 2017 . Peer-reviewed
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      Apollo
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      F1000Research
      Article . 2017 . Peer-reviewed
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    Authors: Brysbaert, Guillaume; Lorgouilloux, Kevin; Vranken, Wim F; Lensink, Marc F;

    Motivation Protein function is directly related to amino acid residue composition and the dynamics of these residues. Centrality analyses based on residue interaction networks permit to identify key residues in a protein that are important for its fold or function. Such central residues and their environment constitute suitable targets for mutagenesis experiments. Predicted flexibility and changes in flexibility upon mutation provide valuable additional information for the design of such experiments. Results We combined centrality analyses with DynaMine flexibility predictions in a Cytoscape app called RINspector. The app performs centrality analyses and directly visualizes the results on a graph of predicted residue flexibility. In addition, the effect of mutations on local flexibility can be calculated. info:eu-repo/semantics/published SCOPUS: ar.j

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    Lille Open Archive
    Article . 2017
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    DI-fusion
    Article . 2018 . Peer-reviewed
    Data sources: DI-fusion
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      Lille Open Archive
      Article . 2017
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      DI-fusion
      Article . 2018 . Peer-reviewed
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    Authors: Aite, Méziane; Chevallier, Marie; Frioux, Clémence; Trottier, Camille; +11 Authors

    Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway. Author summary Genome-scale metabolic models describe an organism’s metabolism. Building good models requires the integration of all relevant available information, obtained by exploring different data types and biological databases. This process is not straightforward and choices are made along the way, for example, which data is analyzed, with what tools. It matters that all reconstruction steps are well documented so that models can be fully exploited by the community. Having this metadata allows other researchers to reproduce, improve or reuse a model as a blueprint to create new ones. Sadly, this information is usually scattered and its proper distribution is the exception rather than the norm when using “à la carte” pipelines that combine main platforms and individual tools. We created a platform for “à la carte” metabolic model generation that responds to the need of transparency and data-connection in the field. It includes a battery of tools to exploit heterogeneous data through customizable pipelines. At each step, relevant information is stored, ensuring reproducibility and documentation of processes. Furthermore, exploration of models and metadata during the reconstruction process is facilitated through the automatic generation of local wikis. This view offers a user-friendly solution to iteratively explore genome-scale metabolic models produced with personalized pipelines and poorly interoperable tools. We highlight these benefits by building models for organisms with various input data. Among them, we show why the combination of heterogeneous information is necessary to elucidate specificities of Tisochrysis lutea, a eukaryotic microalga, for anti-oxidant production.

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