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	Analisi della diversità del microbioma in serie temporali	Analisi della Diversità del Microbioma Multi-Omica
Campo	Bioinformatica	Bioinformatica
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2010s (formalized with 16S amplicon sequencing era; expanded ~2012–2020)	2010s–present
Ideatore	Developed iteratively through the microbiome research community; key contributions from Susan Holmes, Rob Knight, and colleagues	Developed collectively; key frameworks by Le Cao et al. (mixOmics, 2017) and Argelaguet et al. (MOFA, 2018)
Tipo	Longitudinal observational / bioinformatics pipeline	Integrative computational pipeline
Fonte seminale	Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581–583.	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.
Alias	longitudinal microbiome diversity analysis, temporal microbiome analysis, repeated-measures microbiome diversity, time-course microbiome analysis	multi-omics microbiome profiling, integrated microbiome omics, multi-modal microbiome analysis, microbiome multi-omics integration
Correlati	5	5
Sintesi	Time-series microbiome diversity analysis tracks how the richness, evenness, and community composition of microbial communities change across multiple time points within the same subjects. By combining standard diversity metrics with longitudinal statistical models, it separates true temporal dynamics from inter-individual variation, identifying when and how perturbations such as diet changes, antibiotic treatment, or disease onset reshape the microbiome.	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.