Microbial communities are ubiquitous and dynamic systems that inhabit a multitude of environments. They underpin natural as well as biotechnological processes, and are also implicated in human health. The elucidation and understanding of these structurally and functionally complex microbial systems using a broad spectrum of toolkits ranging from in situ sampling, high-throughput data generation ("omics"), bioinformatic analyses, computational modelling and laboratory experiments is the aim of the emerging discipline of Eco-Systems Biology. Integrated workflows which allow the systematic investigation of microbial consortia are being developed. However, in silico methods for analysing multi-omic data sets are so far typically lab-specific, applied ad hoc, limited in terms of their reproducibility by different research groups and suboptimal in the amount of data actually being exploited. To address these limitations, the present work initially focused on the development of the Integrated Meta-omic Pipeline (IMP), a large-scale reference-independent bioinformatic analyses pipeline for the integrated analysis of coupled metagenomic and metatranscriptomic data. IMP is an elaborate pipeline that 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 greatly enhances overall data usage, output volume and quality as demonstrated using relevant use-cases. Finally, IMP is encapsulated within a user-friendly implementation using Python while relying on Docker for reproducibility. The IMP pipeline was then applied to a longitudinal multi-omic dataset derived from a model microbial community from an activated sludge biological wastewater treatment plant with the explicit aim of following bacteria-phage interaction dynamics using information from the CRISPR-Cas system. This work provides a multi-omic perspective of community-level CRISPR dynamics, namely changes in CRISPR repeat and spacer complements over time, demonstrating that these are heterogeneous, dynamic and transcribed genomic regions. Population-level analysis of two lipid accumulating bacterial species associated with 158 putative bacteriophage sequences enabled the observation of phage-host population dynamics. Several putatively identified bacteriophages were found to occur at much higher abundances compared to other phages and these specific peaks usually do not overlap with other putative phages. In addition, there were several RNA-based CRISPR targets that were found to occur in high abundances. In summary, the present work describes the development of a new bioinformatic pipeline for the analysis of coupled metagenomic and metatranscriptomic datasets derived from microbial communities and its application to a study focused on the dynamics of bacteria-virus interactions. Finally, this work demonstrates the power of integrated multi-omic investigation of microbial consortia towards the conversion of high-throughput next-generation sequencing data into new insights.