Dataset Information

MetExplore: collaborative edition and exploration of metabolic networks.

ABSTRACT: Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers an all-in-one online solution composed of interactive tools for metabolic network curation, network exploration and omics data analysis. In particular, it is possible to curate and annotate metabolic networks in a collaborative environment. The network exploration is also facilitated in MetExplore by a system of interactive tables connected to a powerful network visualization module. Finally, the contextualization of metabolic elements in the network and the calculation of over-representation statistics make it possible to interpret any kind of omics data. MetExplore is a sustainable project maintained since 2010 freely available at https://metexplore.toulouse.inra.fr/metexplore2/.

SUBMITTER: Cottret L

PROVIDER: S-EPMC6030842 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MetExplore: collaborative edition and exploration of metabolic networks.

Cottret Ludovic L Frainay Clément C Chazalviel Maxime M Cabanettes Floréal F Gloaguen Yoann Y Camenen Etienne E Merlet Benjamin B Heux Stéphanie S Portais Jean-Charles JC Poupin Nathalie N Vinson Florence F Jourdan Fabien F

Nucleic acids research 20180701 W1

Metabolism of an organism is composed of hundreds to thousands of interconnected biochemical reactions responding to environmental or genetic constraints. This metabolic network provides a rich knowledge to contextualize omics data and to elaborate hypotheses on metabolic modulations. Nevertheless, performing this kind of integrative analysis is challenging for end users with not sufficiently advanced computer skills since it requires the use of various tools and web servers. MetExplore offers a ...[more]

PMID: 29718355

Similar Datasets

Project description:BackgroundOsteoarthritis is a multifactorial disease characterized by destruction of the articular cartilage due to genetic, mechanical and environmental components affecting more than 100 million individuals all over the world. Despite the high prevalence of the disease, the absence of large-scale molecular studies limits our ability to understand the molecular pathobiology of osteoathritis and identify targets for drug development.Methodology/principal findingsIn this study we integrated genetic, bioinformatic and proteomic approaches in order to identify new genes and their collaborative networks involved in osteoarthritis pathogenesis. MicroRNA profiling of patient-derived osteoarthritic cartilage in comparison to normal cartilage, revealed a 16 microRNA osteoarthritis gene signature. Using reverse-phase protein arrays in the same tissues we detected 76 differentially expressed proteins between osteoarthritic and normal chondrocytes. Proteins such as SOX11, FGF23, KLF6, WWOX and GDF15 not implicated previously in the genesis of osteoarthritis were identified. Integration of microRNA and proteomic data with microRNA gene-target prediction algorithms, generated a potential "interactome" network consisting of 11 microRNAs and 58 proteins linked by 414 potential functional associations. Comparison of the molecular and clinical data, revealed specific microRNAs (miR-22, miR-103) and proteins (PPARA, BMP7, IL1B) to be highly correlated with Body Mass Index (BMI). Experimental validation revealed that miR-22 regulated PPARA and BMP7 expression and its inhibition blocked inflammatory and catabolic changes in osteoarthritic chondrocytes.Conclusions/significanceOur findings indicate that obesity and inflammation are related to osteoarthritis, a metabolic disease affected by microRNA deregulation. Gene network approaches provide new insights for elucidating the complexity of diseases such as osteoarthritis. The integration of microRNA, proteomic and clinical data provides a detailed picture of how a network state is correlated with disease and furthermore leads to the development of new treatments. This strategy will help to improve the understanding of the pathogenesis of multifactorial diseases such as osteoarthritis and provide possible novel therapeutic targets.

Project description:PurposeOne of the aims of medical education is to generate lifelong learners, leading to the identification of self-directed learning (SDL) as an essential component of medical education. While SDL is focused on an individual learner, collaboration is critically important in medicine. We developed an online course using the collaborative SDL (CSDL) framework. A goal for the course was for students to gain a better appreciation for the SDL process by exploring the COVID-19 pandemic.MethodsWe utilized CSDL to implement a 2-week elective attended by fifteen M3 and M4 medical students. Students submitted short videos reflecting on their course experience and the relevance of the material to their future training. Qualitative analysis of reflections was conducted to determine the effectiveness of the CSDL framework, and an assessment of the course evaluations was performed to explore student perceptions of the course and its effectiveness at preparing them for practice. A survey regarding student perceptions of SDL was offered to M3 and M4 students in order to explore their experiences with SDL and perceptions of its importance in the context of the school curriculum.ResultsThe CSDL framework was effective in making students aware of the importance of SDL in medical practice. Students gained basic and clinical knowledge about the subject, experienced increased confidence, and appreciated collaborating with their peers. The survey offered to the general student body reflected that all students perceived that they employed SDL in their time as medical students. However, many students indicated that they had not utilized components of SDL beyond synthesizing and assessing their learning needs.ConclusionCSDL is an effective method for promoting self-directed learning. Undergraduate medical course constructs utilizing CSDL will benefit students as they continue their career development.Supplementary informationThe online version contains supplementary material available at 10.1007/s40670-021-01493-7.

Project description:BackgroundAntimicrobial resistance (AMR) is a global health security threat requiring research collaboration globally and regionally. Despite repeated calls for international research collaboration in Asia, literature analyzing the nature of collaborative AMR research in Asia has been sparse. This study aims to describe the characteristics of the AMR research network in Asia and investigate the factors influencing collaborative tie formation between organizations.MethodsWe carried out a mixed-methods study by combining social network analysis (SNA) and in-depth interviews. SNA was first conducted on primary data to describe the characteristics of the AMR research network in Asia. Exponential random graph models (ERGMs) were then used to examine the influence of factors such as organization type, country affluence levels, regional proximity and One Health research on collaborative tie formation among organizations. In-depth interviews were conducted with network participants to provide contextual insights to the quantitative data.ResultsThe results reveal that the research network exhibits a core-periphery structure, where a minority of organizations have a significantly higher number of collaborations with others. The most influential organizations in the network are academic institutions from high-income countries within and outside Asia. The ERGM results demonstrate that organizations prefer to collaborate with others of similar organization types, country-based affluence levels and One Health domains of focus, but also with others across different World Health Organization regions. The qualitative analysis identified three main themes: the challenges that impede collaboration, the central role of academic institutions, and the nature of collaborations across One Health domains, giving rise to important empirical milestones in understanding AMR research in Asia.ConclusionWe thus recommend leveraging academic institutions as "integrators" to bridge differences, increasing funds channelled towards research capacity building to alleviate structural barriers to collaboration, streamlining collaborative mechanisms to overcome cumbersome administrative hurdles, and increasing efforts to establish trust between all organizations.

Dataset Information

MetExplore: collaborative edition and exploration of metabolic networks.

Publications

MetExplore: collaborative edition and exploration of metabolic networks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets