Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Applications of network theory to microbial ecology are an emerging and promising approach to understanding both global and local patterns in the structure and interplay of these microbial communities. In this paper, we present an open-source python toolbox which consists of two modules: on one hand, we introduce a visualization module that incorporates the use of UMAP, a dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density; on the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple the resulting networks with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) we validate the improvements of our new version of SparCC. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data. This easy-to-use implementation is aimed to microbial ecologists with little to no experience in programming, while the most experienced bioinformatics will also be able to manipulate the source code’s functions with ease.

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Section: Referencesmentioning

confidence: 99%

MicNet toolbox: Visualizing and unraveling a microbial network

Favila¹,

Madrigal-Trejo

Legorreta³

et al. 2022

PLoS ONE

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“…Table 3 -Relationships between the ecological mechanisms of an interaction and the motif effects observed on the sequence counts or the interacting ASVs. Assessing the predictive accuracy of a network inference tool is done using either computer generated datasets where interactions are known (Röttjers and Faust, 2018) or by measuring other properties of the inferred network (Barroso-Bergadà et al, 2021). This uses 'consensus' networks, where those interactions that exist in several network examples are pooled, maximising the likelihood of the hypotheses being common to the whole system.…”

Section: Inferring Network Using Abductive/inductive Logic Programmingmentioning

confidence: 99%

Coupling ecological network analysis with high-throughput sequencing-based surveys: Lessons from the next-generation biomonitoring project

Dubart

Alonso

Barroso-Bergadà

et al. 2021

Advances in Ecological Research

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“…Microbial network where every OTU/ASV have an indirect ecological association with every other OUT/ASV [27,[38][39][40] Average degree, Complexity (taxon scale), Connectedness (normalized degree) Average number of edges connected to a node; average number of neighbors for a given node.…”

Section: Relationship Connectionmentioning

confidence: 99%

MicNet Toolbox: visualizing and deconstructing a microbial network

Favila¹,

Madrigal-Trejo

Legorreta³

et al. 2021

Preprint

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Understanding both global and local patterns in the structure and interplay of microbial communities has been a fundamental question in ecological research. In this paper, we present a python toolbox that combines two emerging techniques that have been proposed as useful when analyzing compositional microbial data. On one hand, we introduce a visualization module that incorporates the use of UMAP, a recent dimensionality reduction technique that focuses on local patterns, and HDBSCAN, a clustering technique based on density. On the other hand, we have included a module that runs an enhanced version of the SparCC code, sustaining larger datasets than before, and we couple this with network theory analyses to describe the resulting co-occurrence networks, including several novel analyses, such as structural balance metrics and a proposal to discover the underlying topology of a co-occurrence network. We validated the proposed toolbox on 1) a simple and well described biological network of kombucha, consisting of 48 ASVs, and 2) using simulated community networks with known topologies to show that we are able to discern between network topologies. Finally, we showcase the use of the MicNet toolbox on a large dataset from Archean Domes, consisting of more than 2,000 ASVs. Our toolbox is freely available as a github repository (https://github.com/Labevo/MicNetToolbox), and it is accompanied by a web dashboard (http://micnetapplb-1212130533.us-east-1.elb.amazonaws.com) that can be used in a simple and straightforward manner with relative abundance data.Author SummaryMicrobial communities are complex systems that cannot be wholly understood when studied by its individual components. Hence, global pattern analyses seem to be a promising complement to highly focused local approaches. Here, we introduce the MicNet toolbox, an open-source collection of several analytical methods for visualizing abundance data and creating co-occurrence networks for further analysis. We include two modules: one for visualization and one for network analysis based on graph theory. Additionally, we introduce an enhanced version of SparCC, a method to estimate correlations for co-occurrence network construction, that is faster and can support larger datasets. We performed method validations using simulated data and a simple biological network. Our toolbox is freely available in a github repository at https://github.com/Labevo/MicNetToolbox, and it is accompanied by a web dashboard that could be easily accessed and manipulated by non-specialist users. With this implementation, we attempt to provide a simple and straightforward way to explore and analyze microbial relative abundance data.

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Microbial networks inferred from environmental DNA data for biomonitoring ecosystem change: Strengths and pitfalls

Cited by 22 publications

References 121 publications

MicNet toolbox: Visualizing and unraveling a microbial network

MicNet toolbox: Visualizing and unraveling a microbial network

Coupling ecological network analysis with high-throughput sequencing-based surveys: Lessons from the next-generation biomonitoring project

MicNet Toolbox: visualizing and deconstructing a microbial network

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