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Recovering metagenome-assembled genomes (MAGs) from shotgun sequencing data is an increasingly common task in microbiome studies, as MAGs provide deeper insight into the functional potential of both culturable and non-culturable microorganisms. However, metagenome-assembled genomes vary in quality and may contain omissions and contamination. These errors present challenges for detecting genes and comparing gene enrichment across sample types. To address this, we propose happi, an approach to testing hypotheses about gene enrichment that accounts for genome quality. We illustrate the advantages of happi over existing approaches using published Saccharibacteria MAGs, Streptococcus thermophilus MAGs, and via simulation., (© 2023. BioMed Central Ltd., part of Springer Nature.)

High-throughput sequencing is widely used to study microbial communities. However, choice of laboratory protocol is known to affect the resulting microbiome data, which has an unquantified impact on many comparisons between communities of scientific interest. We propose a novel approach to evaluating replicability in high-dimensional data and apply it to assess the cross-laboratory replicability of signals in microbiome data using the Microbiome Quality Control Project data set. We learn distinctions between samples as measured by a single laboratory and evaluate whether the same distinctions hold in data produced by other laboratories. While most sequencing laboratories can consistently distinguish between samples (median correct classification 87% on genus-level proportion data), these distinctions frequently fail to hold in data from other laboratories (median correct classification 55% across laboratory on genus-level proportion data). As identical samples processed by different laboratories generate substantively different quantitative results, we conclude that 16S sequencing does not reliably resolve differences in human microbiome samples. However, because we observe greater replicability under certain data transformations, our results inform the analysis of microbiome data., (© The Author 2021. Published by Oxford University Press.)

Comparing ecological communities across environmental gradients can be challenging, especially when the number of different taxonomic groups in the communities is large. In this setting, community-level summaries called diversity indices are widely used to detect changes in the community ecology. However, estimation of diversity indices has received relatively little attention from the statistical community. The most common estimates of diversity are the maximum likelihood estimates of the parameters of a multinomial model, even though the multinomial model implies strict assumptions about the sampling mechanism. In particular, the multinomial model prohibits ecological networks, where taxa positively and negatively co-occur. In this article, we leverage models from the compositional data literature that explicitly account for co-occurrence networks and use them to estimate diversity. Instead of proposing new diversity indices, we estimate popular diversity indices under these models. While the methodology is general, we illustrate the approach for the estimation of the Shannon, Simpson, Bray-Curtis, and Euclidean diversity indices. We contrast our method to multinomial, low-rank, and nonparametric methods for estimating diversity indices. Under simulation, we find that the greatest gains of the method are in strongly networked communities with many taxa. Therefore, to illustrate the method, we analyze the microbiome of seafloor basalts based on a 16S amplicon sequencing dataset with 1425 taxa and 12 communities., (© The Author 2020. Published by Oxford University Press.)

Introduction: Microbial residents of the human oral cavity have long been a major focus of microbiology due to their influence on host health and intriguing patterns of site specificity amidst the lack of dispersal limitation. However, the determinants of niche partitioning in this habitat are yet to be fully understood, especially among taxa that belong to recently discovered branches of microbial life., Results: Here, we assemble metagenomes from tongue and dental plaque samples from multiple individuals and reconstruct 790 non-redundant genomes, 43 of which resolve to TM7, a member of the Candidate Phyla Radiation, forming six monophyletic clades that distinctly associate with either plaque or tongue. Both pangenomic and phylogenomic analyses group tongue-specific clades with other host-associated TM7 genomes. In contrast, plaque-specific TM7 group with environmental TM7 genomes. Besides offering deeper insights into the ecology, evolution, and mobilome of cryptic members of the oral microbiome, our study reveals an intriguing resemblance between dental plaque and non-host environments indicated by the TM7 evolution, suggesting that plaque may have served as a stepping stone for environmental microbes to adapt to host environments for some clades of microbes. Additionally, we report that prophages are widespread among oral-associated TM7, while absent from environmental TM7, suggesting that prophages may have played a role in adaptation of TM7 to the host environment., Conclusions: Our data illuminate niche partitioning of enigmatic members of the oral cavity, including TM7, SR1, and GN02, and provide genomes for poorly characterized yet prevalent members of this biome, such as uncultivated Flavobacteriaceae.

Manipulating the microbiome has enormous potential to treat important human diseases. Microbiome surveys are often used to identify potential therapeutic targets by finding associations between microbial elements and disease status. We argue that many reported associations between the microbiome and disease are incompatible with translational research because they are insufficiently specific. We encourage the clear specification of manipulable microbial elements that can be tested in follow-up randomized experiments, and we provide multiple examples of specific and nonspecific microbial elements., (Copyright © 2020 Elsevier Ltd. All rights reserved.)