Examinando por Materia "Microbiome"

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    Ítem
    IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses
    (BioMed Central Ltd., 2016-12-16) Narayanasamy, S.; Jarosz, Y.; Muller, E.E.L.; Heintz-Buschart, A.; Herold, M.; Kaysen, A.; Laczny, C.C.; Pinel, N.; May, P.; Wilmes, P.; Universidad EAFIT. Departamento de Ciencias; Biodiversidad, Evolución y Conservación
    Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).
  • No hay miniatura disponible
    Ítem
    IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses
    (BioMed Central Ltd., 2016-12-16) Narayanasamy, S.; Jarosz, Y.; Muller, E.E.L.; Heintz-Buschart, A.; Herold, M.; Kaysen, A.; Laczny, C.C.; Pinel, N.; May, P.; Wilmes, P.; Universidad EAFIT. Departamento de Ciencias; Ciencias Biológicas y Bioprocesos (CIBIOP)
    Existing workflows for the analysis of multi-omic microbiome datasets are lab-specific and often result in sub-optimal data usage. Here we present IMP, a reproducible and modular pipeline for the integrated and reference-independent analysis of coupled metagenomic and metatranscriptomic data. IMP incorporates robust read preprocessing, iterative co-assembly, analyses of microbial community structure and function, automated binning, as well as genomic signature-based visualizations. The IMP-based data integration strategy enhances data usage, output volume, and output quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python and Docker. IMP is available at http://r3lab.uni.lu/web/imp/ (MIT license).