Open challenges for microbial network construction and analysis

Faust, Karoline

doi:10.1038/s41396-021-01027-4

Cited by 176 publications

(138 citation statements)

References 52 publications

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“…This includes the Google search algorithm, the discovery of emergent phase transition in material science and the invention of reference models for the internet [49] . In the context of human microbiomes, network science is used to construct microbial association networks [50] . Increasingly, a significant number of methods are being developed for improved microbial network analysis, where microbial clades are represented as nodes and edges (between them) determine associations [51] .…”

Section: Emerging Computational Methods For Microbiome Analyticsmentioning

confidence: 99%

“…Important limitations of such network analysis however include dependence of microbial association networks on sampling resolution, limitations of sequence read analysis and the inability to distinguish live, dead, or dormant cells, although the latter may be accounted for by use of RNA sequencing techniques [50] . Concerns with the interpretability of the output networks remain an important challenge for those using such analysis [50] .…”

Section: Emerging Computational Methods For Microbiome Analyticsmentioning

confidence: 99%

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Mathematical-based microbiome analytics for clinical translation

Narayana

Aogáin

Goh

et al. 2021

Computational and Structural Biotechnology Journal

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Section: Emerging Computational Methods For Microbiome Analyticsmentioning

confidence: 99%

Section: Emerging Computational Methods For Microbiome Analyticsmentioning

confidence: 99%

Mathematical-based microbiome analytics for clinical translation

Narayana

Aogáin

Goh

et al. 2021

Computational and Structural Biotechnology Journal

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“…In that way, the influence of the environment on the network structure would be reduced, allowing other potential ecological factors, such as interactions, to become more visible in the network structure. Alternatively environmental factors can be directly integrated into the network construction, for example by representing them as nodes in the network, enabling one to examine their influence on the other nodes (taxa) (Faust (2021)). In the same way, data on the distance between samples can also be used to check if the links result indirectly due spatial dispersion patterns (Goberna et al (2019); D’Amen et al (2018)).…”

Section: Challenges and Way Forwardmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…species that respond in the same way to an environmental factor tend to co-occur in samples with variation of that environmental factor), species interactions (f.e. two microbial taxa need to exchange specific metabolites for increasing their fitness), dispersal dynamics and stochastic processes (Faust (2021)). Network analysis of in situ distribution of microbial taxa in soil reveals the final result of these community assembly processes, and can therefore be a great starting point to better understand them.…”

Section: Introductionmentioning

confidence: 99%

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From Diversity to Complexity: Microbial Networks in Soils

Guseva¹,

D'Arcy²,

Simon³

et al. 2021

Preprint

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Network analysis has been used for many years in ecological research to analyze organismal associations, for example in food webs, plant-plant or plant-animal interactions. Although network analysis is widely applied in microbial ecology, only recently has it entered the realms of soil microbial ecology, shown by a rapid rise in studies applying co-occurrence analysis to soil microbial communities. While this application offers great potential for deeper insights into the ecological structure of soil microbial ecosystems, it also brings new challenges related to the specific characteristics of soil datasets and the type of ecological questions that can be addressed. In this Perspectives Paper we assess the challenges of applying network analysis to soil microbial ecology due to the small-scale heterogeneity of the soil environment and the nature of soil microbial datasets. We review the different approaches of network construction that are commonly applied to soil microbial datasets and discuss their features and limitations. Using a test dataset of microbial communities from two depths of a forest soil, we demonstrate how different experimental designs and network constructing algorithms affect the structure of the resulting networks, and how this in turn may influence ecological conclusions. We will also reveal how assumptions of the construction method, methods of preparing the dataset, and definitions of thresholds affect the network structure. Finally, we discuss the particular questions in soil microbial ecology that can be approached by analyzing and interpreting specific network properties. Targeting these network properties in a meaningful way will allow applying this technique not in merely descriptive, but in hypothesis-driven research.

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An Explainable Graph Neural Framework to Identify Cancer‐Associated Intratumoral Microbial Communities

Liu,

Sun,

et al. 2024

Advanced Science

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Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer‐association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer‐associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor‐microbe‐immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer‐associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow‐up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

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Open challenges for microbial network construction and analysis

Cited by 176 publications

References 52 publications

Mathematical-based microbiome analytics for clinical translation

Mathematical-based microbiome analytics for clinical translation

From Diversity to Complexity: Microbial Networks in Soils

An Explainable Graph Neural Framework to Identify Cancer‐Associated Intratumoral Microbial Communities

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