Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Analyse de la diversité du microbiome multi-omique× | Analyse de la diversité du microbiome basée sur les réseaux× | |
|---|---|---|
| Domaine | Bio-informatique | Bio-informatique |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2010s–present | 2012 |
| Auteur d'origine≠ | Developed collectively; key frameworks by Le Cao et al. (mixOmics, 2017) and Argelaguet et al. (MOFA, 2018) | Faust, Raes, Friedman, Alm and colleagues |
| Type≠ | Integrative computational pipeline | Integrative bioinformatics pipeline |
| Source fondatrice≠ | Rohart, F., Gautier, B., Singh, A., & Le Cao, K.-A. (2017). mixOmics: An R package for 'omics feature selection and multiple data integration. PLOS Computational Biology, 13(11), e1005752. DOI ↗ | Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687. DOI ↗ |
| Alias | multi-omics microbiome profiling, integrated microbiome omics, multi-modal microbiome analysis, microbiome multi-omics integration | microbial co-occurrence network analysis, microbiome network ecology, ecological network-based diversity, NBMDA |
| Apparentées | 5 | 5 |
| Résumé≠ | Multi-omics microbiome diversity analysis integrates two or more omic data layers — such as metagenomics, metatranscriptomics, metabolomics, and metaproteomics — to characterise both the composition and functional activity of microbial communities. By linking taxonomic diversity metrics with molecular phenotype data, the approach uncovers how community structure translates into ecological and host-relevant functions that no single omic layer can reveal alone. | Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associations as edges in a network, enabling identification of keystone taxa, community modules, and ecological interaction patterns that simple diversity indices cannot detect. |
| ScholarGateJeu de données ↗ |
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