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DRomics tool training course : Management of molecular data obtained in a dose-response framework
Le 04/05/2022 de 09:00 à 12:30
Description
DRomics tool training course – Management of molecular data obtained in a dose-response framework
Omics methods are increasingly used, especially in ecotoxicology, to explore stressor effects at the molecular scale, to investigate pathway responses or to discover new biomarkers. Applied in a dose response framework, they open new perspectives for mechanistic understanding and risk assessment. But the specificity and the complexity of their responses require a dedicated workflow to manage properly the data. DRomics is a tool that was recently developed to help anyone who wants to analyze omics data collected through a dose-response design (https://lbbe.univ-lyon1.fr/fr/dromics) or even in-situ data collected on individuals naturally exposed to various doses. It aims to:
Omics methods are increasingly used, especially in ecotoxicology, to explore stressor effects at the molecular scale, to investigate pathway responses or to discover new biomarkers. Applied in a dose response framework, they open new perspectives for mechanistic understanding and risk assessment. But the specificity and the complexity of their responses require a dedicated workflow to manage properly the data. DRomics is a tool that was recently developed to help anyone who wants to analyze omics data collected through a dose-response design (https://lbbe.univ-lyon1.fr/fr/dromics) or even in-situ data collected on individuals naturally exposed to various doses. It aims to:
- Select monotonic and/or biphasic responsive items (e.g. metabolites, transcripts) ;
- For each selected item, characterize its response by fitting a dose-response (DR) curve and calculating an effect dose/concentration as a benchmark dose/concentration (BMD) ;
- And globally visualize the DR modeling results (shapes of DR curves, BMD values) of all selected items, potentially at different omics levels (e.g. transcriptomics, metabolomics) or in different conditions (e.g. with or without pre-exposition or at different exposure times), and interpret them regarding their biological annotation in a common database (e.g. KEGG or Gene Ontology).

