Microbiome metagenomics

Short talk: rRbiMs, r tools for Reconstructing bin Metabolisms.

rRbiMs, r tools for Reconstructing bin Metabolisms. Mirna Vazquez Rosas Landa,Valerie de Anda Torres,Sahil Shan,Brett Baker The University of Texas at Austin Abstract Although microorganisms play a crucial role in the biogeochemical cycles and ecosystems, most of their diversity is still unknown. Understanding the metabolic potential of novel microbial genomes is an essential step to access this diversity. Most annotation pipelines use predefined databases to add the functional annotation. However, they do not allow the user to add metadata information, making it challenging to explore the metabolism based on a specific scientific question.

Short talk: Microbiome data science in the SummarizedExperiment universe

Microbiome data science in the SummarizedExperiment universe Felix G.M. Ernst,Tuomas Borman,Sudarshan Shetty,Ruizhu Huang,Domenick James Braccia,Hector Bravo,Leo M Lahti University of Turku Abstract Recent developments in Bioconductor classes and methods have opened up new opportunities for microbiome data science. Contemporary microbiome research needs support for hierarchies in the sample and phylogenetic feature spaces, for integrating multiple data types, for analysing spatio-temporal variation, and for general performance optimization. Whereas many promising techniques for such purposes are readily available through the Bioconductor SummarizedExperiment universe, they have been largely underutilized in microbiome data science.

Short talk: Microbial community diversity and network analysis

Microbial community diversity and network analysis Rui Guan,Ruben Garrido Oter Max Planck Institute for Plant Breeding Research Abstract With the help of rapidly developing sequencing technologies, an increasing number of microbiome datasets have been generated and analysed. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members and limits the study of community dynamics. To better understand the principles that govern the establishment of these microbial communities, we developed a framework for microbial community diversity analysis based on higher-order features.