UniFuncNet: a flexible network annotation framework

AbstractSummaryFunctional annotation is an integral part in the analysis of organisms, as well as of multi-species communities. A common way to integrate such information is using biological networks. However, current data integration network tools are heavily dependent on a single source of information, which might strongly limit the amount of relevant data contained within the network. Here we present UniFuncNet, a network annotation framework that dynamically integrates data from multiple biological databases, thereby enabling data collection from various sources based on user preference. This results in a flexible and comprehensive data retrieval framework for network based analyses of omics data. Importantly, UniFuncNet’s data integration methodology allows for the output of a non-redundant composite network and associated metadata. In addition, a workflow exporting UniFuncNet’s output to the graph database management system Neo4j was implemented, which allows for efficient querying and analysis.AvailabilitySource code is available at https://github.com/PedroMTQ/UniFuncNet

Using metabolic networks to resolve ecological properties of microbiomes

The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference, metabolic network reconstruction and modelling, we are now able to define ecological interactions based on metabolic exchanges, identify keystone genes, functions and species, and resolve ecological niches of constituent microbial populations. The resulting knowledge provides detailed information on ecosystem functioning. However, as microbial communities are dynamic in nature the field needs to move towards the integration of time- and space-resolved multi-omic data along with detailed environmental information to fully harness the power of community- and population-level metabolic network modelling. Such approaches will be fundamental for future targeted management strategies with wide-ranging applications in biotechnology and biomedicine

Integrated multi-omic analyses of mobile genetic elements within a mixed microbial community

Microbial communities are ubiquitous, complex and dynamic systems that constantly adapt to changing environmental conditions, while playing important roles in natural environments, human health and biotechnological processes. Invasive mobile genetic elements (iMGE) are considered as important biotic components of microbial communities, in particular (bacterio)-phages and plasmids are some of the most abundant and diverse biological entities, which may influence community structure and dynamics. Microbial populations within naturally occurring communities are constantly interacting with each other. Ecological interactions between those populations can be generally classified as competitive and cooperative relationships. To date, extensive studies on biotic interactions, i.e. relationships between microbial hosts with iMGEs and between microbial populations, have been somewhat limited, thus restricting our understanding of microbial community dynamics. Fortunately, high-throughput multi-omics derived from microbiomes, i.e. metagenomics and metatranscriptomics, enables access to both functional -potential and -expression information of those biotic components. Combining longitudinal multi-omics data with mathematical frameworks allows us to model microbial community interactions and dynamics, unlike ever before. Here, I present a longitudinal integrated multi-omics analysis of biotic components within foaming activated sludge, spanning ~1.5 years to unravel i) iMGE-host dynamics and ii) ecological interactome. In the first part of this work, empirical host-iMGE CRISPR-based links in combination with mathematical modelling highlighted the importance of plasmids, relative to phages, in shaping community structure, while also showing that plasmids vastly outnumbered, and were more targeted via CRISPR-Cas systems, compared to their phage counterparts. In the second part of this work, mathematical modelling is used to provide ecological contexts for the relationships between microbial community members. In general, we observed a dynamic interactome, with higher cooperative interactions, despite these populations encoding highly similar functional potential. In summary, this work demonstrates the potential of longitudinal multi-omics in expanding our understanding of microbial community dynamics, which could be expanded to other microbial ecosystems and potentially lead to applications in human health and biotechnological processes

Using metabolic networks to resolve ecological properties of microbiomes

© 2017 The Authors The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference, metabolic network reconstruction and modelling, we are now able to define ecological interactions based on metabolic exchanges, identify keystone genes, functions and species, and resolve ecological niches of constituent microbial populations. The resulting knowledge provides detailed information on ecosystem functioning. However, as microbial communities are dynamic in nature the field needs to move towards the integration of time- and space-resolved multi-omic data along with detailed environmental information to fully harness the power of community- and population-level metabolic network modelling. Such approaches will be fundamental for future targeted management strategies with wide-ranging applications in biotechnology and biomedicine.status: publishe

Challenges, Strategies, and Perspectives for Reference-Independent Longitudinal Multi-Omic Microbiome Studies

In recent years, multi-omic studies have enabled resolving community structure and interrogating community function of microbial communities. Simultaneous generation of metagenomic, metatranscriptomic, metaproteomic, and (meta) metabolomic data is more feasible than ever before, thus enabling in-depth assessment of community structure, function, and phenotype, thus resulting in a multitude of multi-omic microbiome datasets and the development of innovative methods to integrate and interrogate those multi-omic datasets. Specifically, the application of reference-independent approaches provides opportunities in identifying novel organisms and functions. At present, most of these large-scale multi-omic datasets stem from spatial sampling (e.g., water/soil microbiomes at several depths, microbiomes in/on different parts of the human anatomy) or case-control studies (e.g., cohorts of human microbiomes). We believe that longitudinal multi-omic microbiome datasets are the logical next step in microbiome studies due to their characteristic advantages in providing a better understanding of community dynamics, including: observation of trends, inference of causality, and ultimately, prediction of community behavior. Furthermore, the acquisition of complementary host-derived omics, environmental measurements, and suitable metadata will further enhance the aforementioned advantages of longitudinal data, which will serve as the basis to resolve drivers of community structure and function to understand the biotic and abiotic factors governing communities and specific populations. Carefully setup future experiments hold great potential to further unveil ecological mechanisms to evolution, microbe-microbe interactions, or microbe-host interactions. In this article, we discuss the challenges, emerging strategies, and best-practices applicable to longitudinal microbiome studies ranging from sampling, biomolecular extraction, systematic multi-omic measurements, reference-independent data integration, modeling, and validation