2021-03-232016-12-161474760X14747596WOS;000404477300001SCOPUS;2-s2.0-85041521409http://hdl.handle.net/10784/26821Existing 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).enghttps://v2.sherpa.ac.uk/id/publication/issn/1474-760XarticleMulti-omics data integrationMetagenomicsMetatranscriptomicsMicrobial ecologyMicrobiomeReproducibility2021-03-23Narayanasamy, S.Jarosz, Y.Muller, E.E.L.Heintz-Buschart, A.Herold, M.Kaysen, A.Laczny, C.C.Pinel, N.May, P.Wilmes, P.10.1186/s13059-016-1116-8