scholarly journals GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Gwenaëlle G. Lemoine ◽  
Marie-Pier Scott-Boyer ◽  
Bathilde Ambroise ◽  
Olivier Périn ◽  
Arnaud Droit
Keyword(s):  
Muscle Development ◽  
R Package ◽  
Topological Information ◽  
Old Patients ◽  
Network Modules ◽  
Extended Analysis ◽  
Networks Analysis ◽  
Underlying Processes

Abstract Background Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. Results Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. Conclusion GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

scholarly journals GWENA: Gene Co-expression Networks Analysis and Extended Modules Characterization in a Single Bioconductor Package

2020 ◽  
Author(s):  
Gwenaëlle Lemoine ◽  
Marie-Pier Scott-Boyer ◽  
Bathilde Ambroise ◽  
Olivier Perin ◽  
Arnaud Droit
Keyword(s):  
Muscle Development ◽  
R Package ◽  
Gene Set Enrichment ◽  
Topological Information ◽  
Old Patients ◽  
Network Modules ◽  
Extended Analysis ◽  
Networks Analysis ◽  
Underlying Processes

Abstract Background: Network-based analysis of gene expression through co-expression networks can be used to investigate modular interactions occurring between genes toward different biological functions. An extended description of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline.Results: Here we present GWENA, a new R package that integrates gene co-expression network construction and a whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performances, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. We successfully prioritized a gene whose involvement was unknown in the muscle development and growth. We also gave new insight about the variations in patterns of co-expression as the already known age-dependent loss of connectivity was found coupled to a genes interactions reorganization leading to the expression of other functions involved in aging. Conclusion: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as conditions comparison, it eases the understanding of genes interactions involved in diseases or phenotypes. Going beyond actual packages to perform co-expression analysis, GWENA includes new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization.

scholarly journals ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Alyssa Imbert ◽  
Magali Rompais ◽  
Mohammed Selloum ◽  
Florence Castelli ◽  
Emmanuelle Mouton-Barbosa ◽  
...  
Keyword(s):  
Mouse Models ◽  
Physiological Role ◽  
R Package ◽  
Level Data ◽  
Molecular Information ◽  
Number Of Individuals ◽  
Deep Phenotyping ◽  
Omics Data Integration

AbstractGenes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

scholarly journals Bringing radiomics into a multi-omics framework for a comprehensive genotype–phenotype characterization of oncological diseases

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Mario Zanfardino ◽  
Monica Franzese ◽  
Katia Pane ◽  
Carlo Cavaliere ◽  
Serena Monti ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Structures ◽  
R Package ◽  
Biological Information ◽  
Phenotype Definition ◽  
Current State ◽  
Oncological Diseases ◽  
Cancer Phenotype

Abstract Genomic and radiomic data integration, namely radiogenomics, can provide meaningful knowledge in cancer diagnosis, prognosis and treatment. Despite several data structures based on multi-layer architecture proposed to combine multi-omic biological information, none of these has been designed and assessed to include radiomic data as well. To meet this need, we propose to use the MultiAssayExperiment (MAE), an R package that provides data structures and methods for manipulating and integrating multi-assay experiments, as a suitable tool to manage radiogenomic experiment data. To this aim, we first examine the role of radiogenomics in cancer phenotype definition, then the current state of radiogenomics data integration in public repository and, finally, challenges and limitations of including radiomics in MAE, designing an extended framework and showing its application on a case study from the TCGA-TCIA archives. Radiomic and genomic data from 91 patients have been successfully integrated in a single MAE object, demonstrating the suitability of the MAE data structure as container of radiogenomic data.

Digital x-ray mapping of nanometer-size supported bimetallic catalysts

1988 ◽  
Vol 46 ◽  
pp. 530-531
Author(s):  
L. T. Germinario
Keyword(s):  
Background Radiation ◽  
Metal Cluster ◽  
Single Element ◽  
Beam Diameter ◽  
X Rays ◽  
X Ray ◽  
Major Interest ◽  
Induced Changes

Understanding the role of metal cluster composition in determining catalytic selectivity and activity is of major interest in heterogeneous catalysis. The electron microscope is well established as a powerful tool for ultrastructural and compositional characterization of support and catalyst. Because the spatial resolution of x-ray microanalysis is defined by the smallest beam diameter into which the required number of electrons can be focused, the dedicated STEM with FEG is the instrument of choice. The main sources of errors in energy dispersive x-ray analysis (EDS) are: (1) beam-induced changes in specimen composition, (2) specimen drift, (3) instrumental factors which produce background radiation, and (4) basic statistical limitations which result in the detection of a finite number of x-ray photons. Digital beam techniques have been described for supported single-element metal clusters with spatial resolutions of about 10 nm. However, the detection of spurious characteristic x-rays away from catalyst particles produced images requiring several image processing steps.

Consideraciones en torno a la reducción solipsista

1970 ◽  
Vol 11 ◽  
Author(s):  
Natalia Carolina Petrillo

ResumenEn el presente trabajo se intentará mostrar que la fenomenología no conduce a una postura solipsista. Para ello, se caracterizará en qué consiste el solipsismo. Luego, se intentará refutar a lo que se ha de llamar “solipsismo metafísico” y “solipsismo gnoseológico”, con el objetivo principal de poner de manifiesto el fundamento de motivación para la salida de la ficción solipsista.Palabras claves:Phenomenology – solipsim – empatía - HusserlAbstractWith the aim of showing that phenomenology does not lead in solipsism, I will first attempt a characterization of it. Then, I will attempt a refutation of the so-called “metaphysical” and “epistemological” solipsisms. Finally, the nature and role of Husserl´s solipsistic fiction is examined, and the grounds that motivate the overcoming of this standpoint are disclosed.key wordsFenomenología – solipsismo - empathy – Husserl

ART STUDIOS – A WAY TO DEVELOPE CHILDREN’S IMAGINATION

2020 ◽  
Vol 11 (1) ◽  
pp. 144-148
Author(s):  
Liuba Zlatkova ◽  
Keyword(s):  
Fairy Tale ◽  
Psychological Effects ◽  
Complete Product ◽  
The Arts ◽  
The Creation ◽  
Role Of Parents

The report describes the steps for creating a musical tale by children in the art studios of „Art Workshop“, Shumen. These studios are led by students volunteers related to the arts from pedagogical department of Shumen University, and are realized in time for optional activities in the school where the child studies. The stages of creating a complete product with the help of different arts are traced – from the birth of the idea; the creation of a fairy tale plot by the children; the characterization of the fairy-tale characters; dressing them in movement, song and speech; creating sets and costumes and creating a finished product to present on stage. The role of parents as a link and a necessary helper for children and leaders is also considered, as well as the positive psychological effects that this cooperation creates.

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