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Conversely, reference-independent methodologies, such as approaches based on de novo assemblies, enable the retrieval of the actual genomes and/or potentially novel genes present in samples, thereby allowing more of the data to be mapped and exploited for analysis [].

Furthermore, it has been demonstrated that the assembly of sequencing reads into longer contiguous sequences (contigs) greatly improves the taxonomic assignments and prediction of genes as opposed to their direct identification from short sequencing reads []).

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.

A major drawback of such methods is the large number of sequencing reads from uncultured species and/or divergent strains which are discarded during data analysis, thereby resulting in the loss of potentially useful information.

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