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Relevant bibliographies by topics / Microbial association networks

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Journal articles

Academic literature on the topic 'Microbial association networks'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Microbial association networks"

1

Lo, Chieh, and Radu Marculescu. "MPLasso: Inferring microbial association networks using prior microbial knowledge." PLOS Computational Biology 13, no. 12 (2017): e1005915. http://dx.doi.org/10.1371/journal.pcbi.1005915.

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2

Rocha-Viggiano, Ana K., Saray Aranda-Romo, Mariana Salgado-Bustamante, and Cesaré Ovando-Vázquez. "Meconium Microbiota Composition and Association with Birth Delivery Mode." Advanced Gut & Microbiome Research 2022 (November 7, 2022): 1–18. http://dx.doi.org/10.1155/2022/6077912.

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Recently, the intrauterine sterile environment theory has been questioned. Growing evidence shows that microbial in utero pioneer gut colonization could occur prebirth, and this initial colonization may play an important role in the development of the neonate immune system and setting up a niche for the adult-like microbiota. In this study, we compared the microbiota networks from public available meconium datasets from different countries. The findings showed differences at the genera level and were country-dependent. We generated and analyzed bacterial networks, at the genera level of meconi

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3

Centler, Florian, Sarah Günnigmann, Ingo Fetzer, and Annelie Wendeberg. "Keystone Species and Modularity in Microbial Hydrocarbon Degradation Uncovered by Network Analysis and Association Rule Mining." Microorganisms 8, no. 2 (2020): 190. http://dx.doi.org/10.3390/microorganisms8020190.

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Natural microbial communities in soils are highly diverse, allowing for rich networks of microbial interactions to unfold. Identifying key players in these networks is difficult as the distribution of microbial diversity at the local scale is typically non-uniform, and is the outcome of both abiotic environmental factors and microbial interactions. Here, using spatially resolved microbial presence-absence data along an aquifer transect contaminated with hydrocarbons, we combined co-occurrence analysis with association rule mining to identify potential keystone species along the hydrocarbon deg

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4

Ai, Dongmei, Hongfei Pan, Xiaoxin Li, Min Wu, and Li C. Xia. "Association network analysis identifies enzymatic components of gut microbiota that significantly differ between colorectal cancer patients and healthy controls." PeerJ 7 (July 29, 2019): e7315. http://dx.doi.org/10.7717/peerj.7315.

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The human gut microbiota plays a major role in maintaining human health and was recently recognized as a promising target for disease prevention and treatment. Many diseases are traceable to microbiota dysbiosis, implicating altered gut microbial ecosystems, or, in many cases, disrupted microbial enzymes carrying out essential physio-biochemical reactions. Thus, the changes of essential microbial enzyme levels may predict human disorders. With the rapid development of high-throughput sequencing technologies, metagenomics analysis has emerged as an important method to explore the microbial comm

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5

Faust, Karoline, and Jeroen Raes. "CoNet app: inference of biological association networks using Cytoscape." F1000Research 5 (June 27, 2016): 1519. http://dx.doi.org/10.12688/f1000research.9050.1.

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Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequenc

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6

Faust, Karoline, and Jeroen Raes. "CoNet app: inference of biological association networks using Cytoscape." F1000Research 5 (October 14, 2016): 1519. http://dx.doi.org/10.12688/f1000research.9050.2.

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Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequenc

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7

Nagpal, Sunil, Rashmi Singh, Deepak Yadav, and Sharmila S. Mande. "MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks." Nucleic Acids Research 48, W1 (2020): W572—W579. http://dx.doi.org/10.1093/nar/gkaa254.

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Abstract Microbial association networks are frequently used for understanding and comparing community dynamics from microbiome datasets. Inferring microbial correlations for such networks and obtaining meaningful biological insights, however, requires a lengthy data management workflow, choice of appropriate methods, statistical computations, followed by a different pipeline for suitably visualizing, reporting and comparing the associations. The complexity is further increased with the added dimension of multi-group ‘meta-data’ and ‘inter-omic’ functional profiles that are often associated wit

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8

Liu, Fei, Shao-Wu Zhang, Ze-Gang Wei, Wei Chen, and Chen Zhou. "Mining Seasonal Marine Microbial Pattern with Greedy Heuristic Clustering and Symmetrical Nonnegative Matrix Factorization." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/189590.

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With the development of high-throughput and low-cost sequencing technology, a large number of marine microbial sequences were generated. The association patterns between marine microbial species and environment factors are hidden in these large amount sequences. Mining these association patterns is beneficial to exploit the marine resources. However, very few marine microbial association patterns are well investigated in this field. The present study reports the development of a novel method called HC-sNMF to detect the marine microbial association patterns. The results show that the four seas

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9

Poudel, R., A. Jumpponen, D. C. Schlatter, et al. "Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management." Phytopathology® 106, no. 10 (2016): 1083–96. http://dx.doi.org/10.1094/phyto-02-16-0058-fi.

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Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. “General network analysis” identifies candidate taxa for maintaining an existing microbial community. “Host-focused analysis” includes a node representing a plant response such as yield, identifying ta

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10

Avila-Jimenez, Maria-Luisa, Gavin Burns, Zhili He, et al. "Functional Associations and Resilience in Microbial Communities." Microorganisms 8, no. 6 (2020): 951. http://dx.doi.org/10.3390/microorganisms8060951.

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Microbial communities have inherently high levels of metabolic flexibility and functional redundancy, yet the structure of microbial communities can change rapidly with environmental perturbation. To understand whether such changes observed at the taxonomic level translate into differences at the functional level, we analyzed the structure of taxonomic and functional gene distribution across Arctic and Antarctic locations. Taxonomic diversity (in terms of alpha diversity and species richness) differed significantly with location. However, we found that functional genes distributed evenly acros

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