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1. specificity: an R package for analysis of feature specificity to environmental and higher dimensional variables, applied to microbiome species data

   https://doi.org/10.1186/s40793-022-00426-0  

   Darcy, John L. ; Amend, Anthony S. ; Swift, Sean O. I. ; Sommers, Pacifica S. ; Lozupone, Catherine A. ( June 2022 , Environmental Microbiome)

   Understanding the factors that influence microbes’ environmental distributions is important for determining drivers of microbial community composition. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogeny. In microbial ecology, “specificity” is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao’s (Theor Popul Biol, 1982. https://doi.org/10.1016/0040-5809(82)90004-1, Sankhya A, 2010. https://doi.org/10.1007/s13171-010-0016-3 ) Quadratic Entropy can be conveniently applied to calculate specificity of a feature, such as a species, to many different environmental variables.

   We present our R packagespecificityfor performing the above analyses, and apply it to four real-life microbial data sets to demonstrate its application. We found that many fungi within the leaves of native Hawaiian plants had strong specificity to rainfall and elevation, even though these variables showed minimal importance in a previous analysis of fungal beta-diversity. In Antarctic cryoconite holes, our tool revealed that many bacteria have specificity to co-occurring algal community composition. Similarly, in the human gut microbiome, many bacteria showed specificity to the composition of bile acids. Finally, our analysis of the Earth Microbiome Project data set showed that most bacteria show strong ontological specificity to sample type. Our software performed as expected on synthetic data as well.

   specificityis well-suited to analysis of microbiome data, both in synthetic test cases, and across multiple environment types and experimental designs. The analysis and software we present here can reveal patterns in microbial taxa that may not be evident from a community-level perspective. These insights can also be visualized and interactively shared among researchers usingspecificity’s companion package,specificity.shiny.

    

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2. Multiscale adaptive differential abundance analysis in microbial compositional data

   https://doi.org/10.1093/bioinformatics/btad178  

   Wang, Shulei ; Alkan, ed., Can ( April 2023 , Bioinformatics)

   Differential abundance analysis is an essential and commonly used tool to characterize the difference between microbial communities. However, identifying differentially abundant microbes remains a challenging problem because the observed microbiome data are inherently compositional, excessive sparse, and distorted by experimental bias. Besides these major challenges, the results of differential abundance analysis also depend largely on the choice of analysis unit, adding another practical complexity to this already complicated problem.

   In this work, we introduce a new differential abundance test called the MsRDB test, which embeds the sequences into a metric space and integrates a multiscale adaptive strategy for utilizing spatial structure to identify differentially abundant microbes. Compared with existing methods, the MsRDB test can detect differentially abundant microbes at the finest resolution offered by data and provide adequate detection power while being robust to zero counts, compositional effect, and experimental bias in the microbial compositional dataset. Applications to both simulated and real microbial compositional datasets demonstrate the usefulness of the MsRDB test.

   All analyses can be found under https://github.com/lakerwsl/MsRDB-Manuscript-Code.

    

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3. GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data

   https://doi.org/10.3389/fbinf.2022.828703  

   Tataru, Christine ; Eaton, Austin ; David, Maude M. ( April 2022 , Frontiers in Bioinformatics)

   Large-scale microbiome studies investigating disease-inducing microbial roles base their findings on differences between microbial count data in contrasting environments (e.g., stool samples between cases and controls). These microbiome survey studies are often impeded by small sample sizes and database bias. Combining data from multiple survey studies often results in obvious batch effects, even when DNA preparation and sequencing methods are identical. Relatedly, predictive models trained on one microbial DNA dataset often do not generalize to outside datasets. In this study, we address these limitations by applying word embedding algorithms (GloVe) and PCA transformation to ASV data from the American Gut Project and generating translation matrices that can be applied to any 16S rRNA V4 region gut microbiome sequencing study. Because these approaches contextualize microbial occurrences in a larger dataset while reducing dimensionality of the feature space, they can improve generalization of predictive models that predict host phenotype from stool associated gut microbiota. The GMEmbeddings R package contains GloVe and PCA embedding transformation matrices at 50, 100 and 250 dimensions, each learned using ∼15,000 samples from the American Gut Project. It currently supports the alignment, matching, and matrix multiplication to allow users to transform their V4 16S rRNA data into these embedding spaces. We show how to correlate the properties in the new embedding space to KEGG functional pathways for biological interpretation of results. Lastly, we provide benchmarking on six gut microbiome datasets describing three phenotypes to demonstrate the ability of embedding-based microbiome classifiers to generalize to independent datasets. Future iterations of GMEmbeddings will include embedding transformation matrices for other biological systems. Available at: https://github.com/MaudeDavidLab/GMEmbeddings . 

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4. Optimizing UniFrac with OpenACC Yields Greater Than One Thousand Times Speed Increase

   https://doi.org/10.1128/msystems.00028-22  

   Sfiligoi, Igor ; Armstrong, George ; Gonzalez, Antonio ; McDonald, Daniel ; Knight, Rob ( June 2022 , mSystems)

   Greene, Casey S. (Ed.)

   ABSTRACT UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another (beta diversity). Striped UniFrac recently added the ability to split the problem into many independent subproblems, exhibiting nearly linear scaling but suffering from memory contention. Here, we adapt UniFrac to graphics processing units using OpenACC, enabling greater than 1,000× computational improvement, and apply it to 307,237 samples, the largest 16S rRNA V4 uniformly preprocessed microbiome data set analyzed to date. IMPORTANCE UniFrac is an important tool in microbiome research that is used for phylogenetically comparing microbiome profiles to one another. Here, we adapt UniFrac to operate on graphics processing units, enabling a 1,000× computational improvement. To highlight this advance, we perform what may be the largest microbiome analysis to date, applying UniFrac to 307,237 16S rRNA V4 microbiome samples preprocessed with Deblur. These scaling improvements turn UniFrac into a real-time tool for common data sets and unlock new research questions as more microbiome data are collected. 

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5. Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea

   https://doi.org/10.1111/mec.15354  

   Capunitan, Darien C. ; Johnson, Oscar ; Terrill, Ryan S. ; Hird, Sarah M. ( February 2020 , Molecular Ecology)

   How the microbiome interacts with hosts across evolutionary time is poorly understood. Data sets including many host species are required to conduct comparative analyses. Here, we analyzed 142 intestinal microbiome samples from 92 birds belonging to 74 species from Equatorial Guinea, using the 16S rRNA gene. Using four definitions for microbial taxonomic units (97%OTU, 99%OTU, 99%OTU with singletons removed, ASV), we conducted alpha and beta diversity analyses. We found that raw abundances and diversity varied between the data sets but relative patterns were largely consistent across data sets. Host taxonomy, diet and locality were significantly associated with microbiomes, at generally similar levels using three distance metrics. Phylogenetic comparative methods assessed the evolutionary relationship between the microbiome as a trait of a host species and the underlying bird phylogeny. Using multiple ways of defining “microbiome traits”, we found that a neutral Brownian motion model did not explain variation in microbiomes. Instead, we found a White Noise model (indicating little phylogenetic signal), was most likely. There was some support for the Ornstein‐Uhlenbeck model (that invokes selection), but the level of support was similar to that of a White Noise simulation, further supporting the White Noise model as the best explanation for the evolution of the microbiome as a trait of avian hosts. Our study demonstrated that both environment and evolution play a role in the gut microbiome and the relationship does not follow a neutral model; these biological results are qualitatively robust to analytical choices.

    

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