Network science and complex systems analysis applied to DNA metabarcoding data.
Jul 03, 2018 10:00
Sep 30, 2018 12:00
|LAMA / University of Savoie Mont Blanc, UMR CNRS 5127
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This post-doctoral position is part of the ANR project Globnets (Global biogeography of ecological networks in forest ecosystems). This project is intrinsically multidisciplinary and requires many competences in Ecology, Biology, Computer Science and Mathematics. It aims at using DNA metabarcoding data collected in various sites all over the world to understand and construct ecological networks in forest ecosystems.
DNA metabarcoding data is a powerful tool to detect a large variety of species (ie eukaryotes, plants, fungi and bacteria) in communities or ecosystem. The main ecological issue is to use this huge amount of data to construct relevant complex networks describing plant-soil multi-trophic assemblages across large spatial scales. In this context, we are searching for a researcher with skills in Big Data issues, graph theory, complex network structures or clustering analysis.
The post-doctoral project can be decomposed into two stages.
First, we propose to use a simple clustering approach to provide the co-occurrence of markers in the different sites (based on the MOTU markers characterizing the different species eukaryotes, plants, fungi and bacteria all together). More specifically, we should be able to construct a bipartite graph of sites/markers and use its discrete structure to construct similarity measures between sites or similarity measures between markers.
Based on this information, we might construct two new graphs one for the similarity of markers and the second for the similarity of the sites. By applying community detection tools and machine learning tools to these graphs, we will “blindly” (i.e. independent from prior knowledge) unravel the dependences in the ecological network and compare it with ecological knowledge. Second, we aim at using more sophisticated methods to get a fine analysis of each cluster (using Bayesian network analysis, LASSO regression, multi-scale analysis, automatic detection of relevant sub- networks) and to detect community for large graphs along the disturbance and environmental gradients. More specifically, those methods will explicitly return the explanatory variables that drive the distribution of each cluster (e.g. temperature or disturbance), which should then allow us to understand the whole complex system.
We are searching candidates for this post-doctoral project with skills in Big Data issues, graph theory, complex network structures or clustering analysis. It will take place at the LAMA (University of Savoie Mont Blanc, UMR CNRS 5127) for the whole year 2019 and will be directed by Laurent Vuillon and Jimmy Garnier. The post-doctorant will interact also with the group of Wilfried Thuiller at the LECA (Alpine Ecology Lab., UGA, UMR CNRS 5553) in Grenoble.
If you are interested by this post-doc, please send an email (before the 30th of September) to firstname.lastname@example.org with your CV and two recommendation letters.