TY  - JOUR
AB  - Linking sequence-derived microbial taxa abundances to host (patho-)physiology or habitat characteristics in a reproducible and interpretable manner has remained a formidable challenge for the analysis of microbiome survey data. Here, we introduce a flexible probabilistic modeling framework, VI-MIDAS (variational inference for microbiome survey data analysis), that enables joint estimation of context-dependent drivers and broad patterns of associations of microbial taxon abundances from microbiome survey data. VI-MIDAS comprises mechanisms for direct coupling of taxon abundances with covariates and taxa-specific latent coupling, which can incorporate spatio-temporal information and taxon-taxon interactions. We leverage mean-field variational inference for posterior VI-MIDAS model parameter estimation and illustrate model building and analysis using Tara Ocean Expedition survey data. Using VI-MIDAS' latent embedding model and tools from network analysis, we show that marine microbial communities can be broadly categorized into five modules, including SAR11-, nitrosopumilus-, and alteromondales-dominated communities, each associated with specific environmental and spatiotemporal signatures. VI-MIDAS also finds evidence for largely positive taxon-taxon associations in SAR11 or Rhodospirillales clades, and negative associations with Alteromonadales and Flavobacteriales classes. Our results indicate that VI-MIDAS provides a powerful integrative statistical analysis framework for discovering broad patterns of associations between microbial taxa and context-specific covariate data from microbiome survey data.
AU  - Mishra, A.*
AU  - McNichol, J.*
AU  - Fuhrman, J.*
AU  - Blei, D.*
AU  - Müller, C.L.
C1  - 74494
C2  - 57493
CY  - Great Clarendon St, Oxford Ox2 6dp, England
TI  - Variational inference for microbiome survey data with application to global ocean data.
JO  - ISME Commun.
VL  - 5
IS  - 1
PB  - Oxford Univ Press
PY  - 2025
SN  - 2730-6151
ER  - 

TY  - JOUR
AB  - While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiome's composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment. We show that long-read sequencing can resolve species-level annotations and specific ecosystem functions through de novo metagenomic assemblies despite the low amount of fragmented DNA used as an input for nanopore sequencing. We then apply our pipeline to assess the diversity and variability of an urban air microbiome, using Barcelona, Spain, as an example; this randomized experiment gives first insights into the presence of highly stable location-specific air microbiomes within the city's boundaries, and showcases the robust microbial assessments that can be achieved through automatable, fast, and portable nanopore sequencing technology.
AU  - Reska, T.T.M.
AU  - Pozdniakova, S.*
AU  - Borràs, S.*
AU  - Perlas Puente,A.
AU  - Sauerborn, E.
AU  - Cañas, L.*
AU  - Schloter, M.
AU  - Rodó, X.*
AU  - Wang, Y.*
AU  - Winkler, J.B.
AU  - Schnitzler, J.-P.
AU  - Urban, L.
C1  - 71372
C2  - 56081
CY  - Great Clarendon St, Oxford Ox2 6dp, England
TI  - Air monitoring by nanopore sequencing.
JO  - ISME Commun.
VL  - 4
IS  - 1
PB  - Oxford Univ Press
PY  - 2024
SN  - 2730-6151
ER  -