ArticleGenomics, proteomics & bioinformatics2016
Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.
Article in Genomics, proteomics & bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Optimizing microbiome reference databases with PacBio full-length 16S rRNA sequencing for enhanced taxonomic classification and biomarker discovery.Frontiers in microbiology · 2024Article
- Filtering ASVs/OTUs via mutual information-based microbiome network analysis.BMC bioinformatics · 2022Article
- Tomato Domestication Affects Potential Functional Molecular Pathways of Root-Associated Soil Bacteria.Plants (Basel, Switzerland) · 2021Article
- Review of tools and algorithms for network motif discovery in biological networks.IET systems biology · 2020Review
- Mobilization of Antibiotic Resistance: Are Current Approaches for Colocalizing Resistomes and Mobilomes Useful?Frontiers in microbiology · 2020Article
- Deciphering the scalene association among type-2 diabetes mellitus, prostate cancer, and chronic myeloid leukemia via enrichment analysis of disease-gene network.Cancer medicine · 2019Article
- Functional signature analysis of extreme Prakriti endophenotypes in gut microbiome of western Indian rural population.Bioinformation · 2019Article
- Application of dynamic expansion tree for finding large network motifs in biological networks.PeerJ · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D.
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Registered trials
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