Biology subjects

Borenstein, E.

Publications and source records attributed to Borenstein, E..

At least 19 recordsLinked to original sources

bioRxiv2026

MAAMOUL: Metabolic network-based discovery of microbiome-metabolome shifts in disease

MotivationA central goal in human gut microbiome research is to identify disease-associated functional shifts, an objective increasingly pursued through metagenomic and metabolomic assays. However, common differential abundance analyses of genes or metabolites often yield long and difficult-to-interpret feature lists. Aggregating features into predefined pathways can improve interpretability but relies on fixed pathway boundaries that may not reflect context-specific functional changes. Moreover, even when paired metagenomic-metabolomic data are available, they are often analyzed separately or linked only through simple statistical associations. ResultsWe introduce MAAMOUL, a knowledge-based computational framework that integrates metagenomic and metabolomic data to identify disease-associated, data-driven microbial metabolic modules. Leveraging prior knowledge of bacterial metabolism, MAAMOUL maps disease-association scores onto a global microbiome-wide metabolic network and identifies custom modules enriched for altered genes and metabolites. Applying MAAMOUL to inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS) datasets revealed significant disease-associated modules not detected by conventional pathway-level analysis. In IBD, modules reflected disrupted sulfur and aromatic amino acid metabolism and enhanced microbial nucleotide salvage, whereas in IBS they linked purine and nicotinate/nicotinamide metabolism. These results demonstrate that network-guided multi-omic integration can uncover coherent functional shifts in the gut microbiome overlooked by single-omic or purely statistical approaches. AvailabilityMAAMOUL is available as an R package at https://github.com/borenstein-lab/MAAMOUL.

bioinformatics↗

bioRxiv2025

Phenotype-driven parallel embedding for microbiome multi-omic data integration

The human microbiome is widely recognized as a key determinant of health and disease, yet most reported links between observed microbial features and clinical outcomes remain descriptive and lack an integrated system-level perspective. Multi-omic studies of the microbiome, which jointly profile and analyze multiple molecular aspects of the microbiome via metagenomics, metabolomics, proteomics, and transcriptomics assays, offers a more comprehensive view of this system, with the potential to uncover how microbial communities and functions influence host physiology. However, integration of such multi-omic data remains challenging due to high dimensionality, major differences in data properties across omics, and the need to utilize and preserve omic-specific information. Embedding omic data in low-dimensional spaces offer a promising avenue to capture complex patterns, reduce noise, and improve downstream analysis, yet most embedding-based microbiome studies to date exhibited limited predictive power or relied on a single joint embedding of all omics thus failing to preserve omic-species properties. To address this, we introduce PAPRICA (Phenotype-Aware Parallel Representation for Integrative omiC Analysis), an encoder-decoder framework for microbiome multi-omic integration that embeds each omic into its own latent space while jointly modeling their relationships. The model consists of parallel autoencoders trained with a loss function that promotes three objectives: (1) accurate reconstruction of each omic, (2) alignment of samples across omics such that proximity in one latent space reflects proximity in the others, and (3) alignment with a phenotype space to capture variation associated with continuous outcomes, such as fecal calprotectin levels in IBD. The resulting models support cross-omic inference and phenotype prediction from the learned latent representations, and enables integration without collapsing data into a single space. This modeling approach thus preserves omic-specific signals while capturing phenotype-associated variation. We compared PAPRICA to four alternative models that represent successive advances in multi-omic integration architectures. We found that across two complementary tasks, predicting one omic profile from another and predicting a continuous phenotype from an input omic profile, our parallel autoencoder approach, and particularly the PAPRICA model, demonstrated better performance across three multi-omic datasets (the Franzosa IBD cohort, Lifelines DEEP and the Dog Aging Project Precision Cohort). Combined, these findings suggest that our embedding strategy effectively captures and balances omic-specific structure, cross-omic relationships, and phenotype-relevant signals across diverse datasets, offering a flexible, scalable framework for embedding complex multi-omic microbiome data and advancing our ability to gain new insights into host-microbiome interactions.

bioinformatics↗

bioRxiv2025

TAGINE: Fast Taxonomy-based Feature Engineering for Microbiome Analysis

SummaryTAGINE is a feature engineering algorithm that leverages the microbial taxonomic tree to optimize feature sets in microbiome data for predictive modeling. The algorithm starts with features at high taxonomic levels and iteratively splits them into lower-level clades in cases where it improves predictive accuracy, ultimately producing a feature set spanning multiple taxonomic levels. This approach aims to markedly reduce the number of features while preserving biological relevance and interpretability. We compare TAGINEs performances to other standard and taxonomy-based feature engineering methods on several different datasets, and show that TAGINE yields more compact feature sets and is orders of magnitude faster than other methods, while maintaining predictive accuracy. Availability and ImplementationTAGINE is freely available under the MIT license with source code available at https://github.com/borenstein-lab/tagine_fe.

bioinformatics↗

bioRxiv2024

Mapping the canine microbiome: Insights from the Dog Aging Project

Companion dogs (Canis lupus familiaris) offer a unique model for studying the gut microbiome and its relation to aging due to their cohabitation with humans, sharing similar environments, diets, and healthcare practices. Here, we present the Dog Aging Project (DAP) Precision cohort, the largest population-wide study of the canine gut microbiome to date. This cohort encompasses over 900 dogs of diverse breeds, environments, and demographics living across the United States. Coupling fecal shotgun metagenomic sequencing with comprehensive phenotypic and environmental surveys and clinical lab tests, we explore the intricate relationships between microbiome composition, aging, and key factors such as diet, health, and living conditions. Our analyses identify various factors associated with microbiome composition. In addition, we find a gradual shift in microbiome composition with age, which allows us to develop a novel metagenomics-based "clock" to predict biological aging based on microbial signatures. Overall, these findings provide an unprecedented and detailed understanding of the role the gut microbiome plays in our four-legged companions, offering both potential applications in veterinary medicine and an exciting model for aging research.

microbiology↗

bioRxiv2024

Protein catabolites as blood-based biomarkers of aging physiology: Findings from the Dog Aging Project

Our understanding of age-related physiology and metabolism has grown through the study of systems biology, including transcriptomics, single-cell analysis, proteomics and metabolomics. Studies in lab organisms in controlled environments, while powerful and complex, fall short of capturing the breadth of genetic and environmental variation in nature. Thus, there is now a major effort in geroscience to identify aging biomarkers and to develop aging interventions that might be applied across the diversity of humans and other free-living species. To meet this challenge, the Dog Aging Project (DAP) is designed to identify cross-sectional and longitudinal patterns of aging in complex systems, and how these are shaped by the diversity of genetic and environmental variation among companion dogs. Here we surveyed the plasma metabolome from the first year of sampling of the Precision Cohort of the DAP. By incorporating extensive metadata and whole genome sequencing information, we were able to overcome the limitations inherent in breed-based estimates of genetic and physiological effects, and to probe the physiological and dietary basis of the age-related metabolome. We identified a significant effect of age on approximately 40% of measured metabolites. Among other insights, we discovered a potentially novel biomarker of age in the post-translationally modified amino acids (ptmAAs). The ptmAAs, which can only be generated by protein hydrolysis, covaried both with age and with other biomarkers of amino acid metabolism, and in a way that was robust to diet. Clinical measures of kidney function mediated about half of the higher ptmAA levels in older dogs. This work identifies ptmAAs as robust indicators of age in dogs, and points to kidney function as a physiological mediator of age-associated variation in the plasma metabolome.

systems biology↗

bioRxiv2024

DNA methylation of transposons pattern aging differences across a diverse cohort of dogs from the Dog Aging Project

Within a species, larger individuals often have shorter lives and higher rates of age-related disease. Despite this well-known link, we still know little about underlying age-related epigenetic differences, which could help us better understand inter-individual variation in aging and the etiology, onset, and progression of age-associated disease. Dogs exhibit this negative correlation between size, health, and longevity and thus represent an excellent system in which to test the underlying mechanisms. Here, we quantified genome-wide DNA methylation in a cohort of 864 dogs in the Dog Aging Project. Age strongly patterned the dog epigenome, with the majority (66% of age-associated loci) of regions associating age-related loss of methylation. These age effects were non-randomly distributed in the genome and differed depending on genomic context. We found the LINE1 (long interspersed elements) class of TEs (transposable elements) were the most frequently hypomethylated with age (FDR < 0.05, 40% of all LINE1 regions). This LINE1 pattern differed in magnitude across breeds of different sizes- the largest dogs lost 0.26% more LINE1 methylation per year than the smallest dogs. This suggests that epigenetic regulation of TEs, particularly LINE1s, may contribute to accelerated age and disease phenotypes within a species. Since our study focused on the methylome of immune cells, we looked at LINE1 methylation changes in golden retrievers, a breed highly susceptible to hematopoietic cancers, and found they have accelerated age-related LINE1 hypomethylation compared to other breeds. We also found many of the LINE1s hypomethylated with age are located on the X chromosome and are, when considering X chromosome inactivation, counter-intuitively more methylated in males. These results have revealed the demethylation of LINE1 transposons as a potential driver of intra-species, demographic-dependent aging variation.

genomics↗

bioRxiv2024

Aging at scale: Younger dogs and larger breeds from the Dog Aging Project show accelerated epigenetic aging.

Dogs exhibit striking within-species variability in lifespan, with smaller breeds often living more than twice as long as larger breeds. This longevity discrepancy also extends to health and aging-larger dogs show higher rates of age-related diseases. Despite this well-established phenomenon, we still know little about the biomarkers and molecular mechanisms that might underlie breed differences in aging and survival. To address this gap, we generated an epigenetic clock using DNA methylation from over 3 million CpG sites in a deeply phenotyped cohort of 864 companion dogs from the Dog Aging Project, including some dogs sampled annually for 2-3 years. We found that the largest breed size tends to have epigenomes that are, on average, 0.37 years older per chronological year compared to the smallest breed size. We also found that higher residual epigenetic age was significantly associated with increased mortality risk, with dogs experiencing a 34% higher risk of death for each year increase in residual epigenetic age. These findings not only broaden our understanding of how aging manifests within a diverse species but also highlight the significant role that demographic factors play in modulating the biological mechanisms underlying aging. Additionally, they highlight the utility of DNA methylation as both a biomarker for healthspan-extending interventions, a mortality predictor, and a mechanism for understanding inter-individual variation in aging in dogs.

genomics↗

bioRxiv2024

Interpolation of Microbiome Composition in Longitudinal Datasets

The human gut microbiome significantly impacts health, prompting a rise in longitudinal studies that capture microbiome samples at multiple time points. Such studies allow researchers to characterize microbiome changes over time, but importantly, also present major analytical challenges due to incomplete or irregular sampling. To address this challenge, longitudinal microbiome studies often employ various interpolation methods, aiming to infer missing microbiome data. However, to date, a comprehensive assessment of such microbiome interpolation techniques, as well as best practice guidelines for interpolating microbiome data, are still lacking. This work aims to fill this gap, rigorously implementing and systematically evaluating a large array of interpolation methods, spanning several different categories, for longitudinal microbiome interpolation. To assess each method and its ability to accurately infer microbiome composition at missing time points, we used three longitudinal microbiome datasets that follow individuals over a long period of time, and a leave-one-out approach. Overall, our analysis demonstrated that the K-nearest neighbors algorithm consistently outperforms other methods in interpolation accuracy, yet, accuracy varied widely across datasets, individuals, and time. Factors such as microbiome stability, sample size, and the time gap between interpolated and adjacent samples significantly influenced accuracy, allowing us to develop a model for predicting the expected interpolation accuracy at a missing time point. Our findings, combined, suggest that accurate interpolation in longitudinal microbiome data is feasible, especially in dense cohorts. Furthermore, using our predictive model, future studies can interpolate data only in time points where the expected interpolation accuracy is high.

bioinformatics↗

bioRxiv2023

A Manifold-Based Framework for Studying the Dynamics of the Vaginal Microbiome

The vaginal bacterial community plays a crucial role in preventing infections. The composition of this community can be classified into five main groups, termed community state types (CSTs). Four of these CSTs, which are primarily consisted of Lactobacillus species, are considered healthy, while the fifth, which is composed of non-Lactobacillus populations, is considered less protective. This latter CST is often considered to represent a state termed Bacterial vaginosis (BV) - a common disease condition associated with unpleasant symptoms and increased susceptibility to sexually transmitted diseases. However, the exact mechanisms underlying BV development are not yet fully understood, including specifically, the dynamics of the vaginal microbiome in BV, and the possible routes it may take from a healthy to a BV state. This study aims to identify the progression from healthy Lactobacillus-dominant populations to symptomatic BV by analyzing 8,026 vaginal samples and using a manifold-detection framework. This approach is inspired by single-cell analysis and aims to identify low-dimensional trajectories in the high-dimensional composition space. This framework further order samples along these trajectories and assign a score (pseudo-time) to each sample based on its proximity to the BV state. Our results reveal distinct routes of progression between healthy and BV state for each CST, with pseudo-time scores correlating with community diversity and quantifying the health state of each sample. BV indicators, including Nugent score, positive Amsels test, and several Amsels criteria, can also be successfully predicted based on pseudo-time scores. Additionally, Gardnerella vaginalis can be identified as a key taxon in BV development using this approach, with increased abundance in samples with high pseudo-time, indicating an unhealthier state across all BV-development routes on the manifold. Taken together, these findings demonstrate how manifold detection can be used to successfully characterizes the progression from healthy Lactobacillus-dominant populations to BV and to accurately quantify the health condition of new samples along the route of BV development.

microbiology↗

bioRxiv2023

Microbial dispersion in the human gut through the lens of fecal transplant

Microorganisms frequently migrate from one ecosystem to another, influencing and shaping their new environment. Yet, despite the potential importance of this process in modulating the environment and the microbial ecosystem, our understanding of the fundamental forces that govern microbial migration and dispersion is still lacking. Moreover, while theoretical studies and in-vitro experimental work have highlighted the contribution of biotic interactions to the assembly of the community, identifying such interactions in vivo, specifically in communities as complex as the human gut, remains challenging. To this end, we developed a new, robust, and compositionally invariant approach, and leveraged data from well-characterized translocation experiments, namely, clinical fecal microbiota transplant (FMT) trials, to rigorously pinpoint dependencies between taxa during the colonization of human gastrointestinal habitat. Our analysis identified numerous pairwise dependencies between co-colonizing microbes during migration between gastrointestinal environments. We further demonstrated that identified dependencies agree with previously reported findings from in-vitro experiments and population-wide distribution patterns. Finally, we characterized the web of metabolic dependencies between these taxa and explored the functional properties that may promote better dispersion. Combined, our findings provide insights into the principles and determinants of community dynamics following ecological translocation, informing potential opportunities for precise community design.

microbiology↗

bioRxiv2023

Multi-view integration of microbiome data for predicting host disease and identifying disease-associated features

The human gut microbiome is a complex ecosystem with profound implications for health and disease. This recognition has led to a surge in multi-omic microbiome studies, employing various molecular assays to elucidate the microbiomes role in diseases across multiple functional layers. However, despite the clear value of these multi-omic datasets, rigorous integrative analysis of such data poses significant challenges, hindering a comprehensive understanding of microbiome-disease interactions. Perhaps most notably, multiple approaches, including univariate and multivariate analyses, as well as machine learning, have been applied to such data to identify disease-associated markers, namely, specific features (e.g., species, pathways, metabolites) that are significantly altered in disease state. These methods, however, often yield extensive lists of features associated with the disease without effectively capturing the multi-layered structure of multi-omic data or offering clear, interpretable hypotheses about underlying microbiome-disease mechanisms. Here, we address this challenge by introducing MintTea - an intermediate integration-based method for analyzing multi-omic microbiome data. MintTea combines a canonical correlation analysis (CCA) extension, consensus analysis, and an evaluation protocol to robustly identify disease-associated multi-omic modules. Each such module consists of a set of features from the various omics that both shift in concord, and collectively associate with the disease. Applying MintTea to diverse case-control cohorts with multi-omic data, we show that this framework is able to capture modules with high predictive power for disease, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome (MS) study, we found a MS-associated module comprising of a highly correlated cluster of serum glutamate- and TCA cycle-related metabolites, as well as bacterial species previously implicated in insulin resistance. In another cohort, we identified a module associated with late-stage colorectal cancer, featuring Peptostreptococcus and Gemella species and several fecal amino acids, in agreement with these species reported role in the metabolism of these amino acids and their coordinated increase in abundance during disease development. Finally, comparing modules identified in different datasets, we detected multiple significant overlaps, suggesting common interactions between microbiome features. Combined, this work serves as a proof of concept for the potential benefits of advanced integration methods in generating integrated multi-omic hypotheses underlying microbiome-disease interactions and a promising avenue for researchers seeking systems-level insights into coherent mechanisms governing microbiome-related diseases.

bioinformatics↗

bioRxiv2022

A data-driven approach for predicting the impact of drugs on the human microbiome

Many medications can negatively impact the bacteria residing in our gut, depleting beneficial species and causing adverse effects. To determine individualized response to pharmaceutical treatment, a comprehensive understanding of the impact of various drugs on the gut microbiome is needed, yet, to date, experimentally challenging to obtain. Towards this end, we developed a data-driven approach, integrating information about the chemical properties of each drug and the genomic content of each microbe, to systematically predicts drug-microbiome interactions. We show that this framework successfully predicts outcomes of in-vitro pairwise drug-microbe experiments, as well as drug-induced microbiome dysbiosis in both animal models and clinical trials. Applying this methodology, we systematically map all interactions between pharmaceuticals and bacteria and demonstrate that medications anti-microbial properties are tightly linked to their adverse effects. This computational framework has the potential to unlock the development of personalized medicine and microbiome-based therapeutic approaches, improving outcomes and minimizing side effects.

microbiology↗

bioRxiv2022

A versatile, chemically-controlled DNA binding switch enables temporal modulation of Cas9-based effectors

CRISPR-Cas9s RNA-guided genome targeting ability has been leveraged to develop a plethora of effectors including targeted transcriptional activators, DNA base editors, and DNA prime editors. Although systems for inducibly modulating Cas9 activity have been developed, current approaches for conferring temporal control require extensive screening of functional protein components. A simpler and general strategy for conferring temporal control over diverse Cas9-based effector activities is needed. Here we describe a versatile chemically-controlled and rapidly-activated DNA binding Cas9 module (ciCas9) that is able to confer temporal control over a variety of Cas9-based effectors. Using the ciCas9 module, we engineer temporally-controlled cytidine and adenine DNA base editors. We employ the ciCas9 base editors to reveal that in vivo bystander editing kinetics occurs via a dependent process where editing at a preferred nucleotide position increases the frequency of edits at a second nucleotide within a target site. Finally, we demonstrate the versatility of the ciCas9 module by creating a temporally-controlled transcriptional activator, a dual cytidine and adenine base editor, and a prime editor.

molecular biology↗

bioRxiv2022

Integrating phylogenetic and functional data in microbiome studies

Microbiome functional data are frequently analyzed to identify associations between microbial gene families and sample groups of interest. This is most often performed with approaches focused on the metagenome-wide relative abundance of microbial functions. Although such approaches can provide valuable insights, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed a novel, phylogeny-aware framework to expand taxonomic balance tree-based approaches to identify enriched functions more robustly. The key focus of our approach, termed POMS, is on identifying functions that are consistently enriched in sample groups across independent taxonomic lineages. Based on simulated data we demonstrate that POMS can more accurately identify gene families that confer a selective advantage compared with commonly used differential abundance approaches. We also show that POMS can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. While this framework may not be able to identify all potential functional enrichments, the enrichments it does identify are more interpretable and conservative compared with those identified by existing differential abundance approaches. More generally, POMS is a novel approach for exploring microbiome functional data, which could be used to complement standard analyses. POMS is freely available as an R package at: https://github.com/gavinmdouglas/POMS.

bioinformatics↗

bioRxiv2021

MIMOSA2: A metabolic network-based tool for inferring mechanism-supported relationships in microbiome-metabolome data

MotivationRecent technological developments have facilitated an expansion of microbiome-metabolome studies, in which a set of microbiome samples are assayed using both genomic and metabolomic technologies to characterize the composition of microbial taxa and the concentrations of various metabolites. A common goal of many of these studies is to identify microbial features (species or genes) that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of such microbe-metabolite links. ResultsWe present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and specific taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and can be customized to incorporate user-defined metabolic pathways. We demonstrate MIMOSA2s ability to identify ground truth microbial mechanisms in simulation datasets, and compare its results with experimentally inferred mechanisms in a dataset describing honeybee gut microbiota. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products. Availability and ImplementationMIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server and R package from www.borensteinlab.com/software_MIMOSA2.html.

microbiology↗

bioRxiv2018

Acquired interbacterial defense systems protect against interspecies antagonism in the human gut microbiome

The impact of direct interactions between co-resident microbes on microbiome composition is not well understood. Here we report the occurrence of acquired interbacterial defense (AID) gene clusters in bacterial residents of the human gut microbiome. These clusters encode arrays of immunity genes that protect against type VI secretion toxin-mediated intra- and inter-species bacterial antagonism. Moreover, the clusters reside on mobile elements and we demonstrate that their transfer is sufficient to confer toxin resistance in vitro and in gnotobiotic mice. Finally, we identify and validate the protective capacity of a recombinase-associated AID subtype (rAID-1) present broadly in Bacteroidales genomes. These rAID-1 gene clusters have a structure suggestive of active gene acquisition and include predicted immunity factors of toxins deriving from diverse organisms. Our data suggest that neutralization of contact-dependent interbacterial antagonism via AID systems shapes human gut microbiome ecology.

microbiology↗

bioRxiv2018

Defining and Evaluating Microbial Contributions to Metabolite Variation in Microbiome-Metabolome Association Studies

Correlation-based analysis of paired microbiome-metabolome datasets is becoming a widespread research approach, aiming to comprehensively identify microbial drivers of metabolic variation. To date, however, the limitations of this approach have not been comprehensively evaluated. To address this challenge, we introduce a mathematical framework to quantify the contribution of each taxon to metabolite variation based on uptake and secretion fluxes. We additionally use a multi-species metabolic model to simulate simplified gut communities, generating idealized microbiome-metabolome datasets. We then compare observed taxon-metabolite correlations in these datasets to calculated ground-truth taxonomic contribution values. We find that in simulations of both a model 10-species community and of complex human gut microbiota, correlation-based analysis poorly identifies key contributors, with extremely low predictive value despite the idealized setting. We further demonstrate that the predictive value of correlation analysis is strongly influenced by both metabolite and taxon properties, as well as exogenous environmental variation. We finally discuss the practical implications of our findings for interpreting microbiome-metabolome studies.\n\nImportanceIdentifying the key microbial taxa responsible for metabolic differences between microbiomes is an important step towards understanding and manipulating microbiome metabolism. To achieve this goal, researchers commonly conduct microbiome-metabolome association studies, comprehensively measuring both the composition of species and the concentration of metabolites across a set of microbial community samples, and then testing for correlations between microbes and metabolites. Here, we evaluated the utility of this general approach by first developing a rigorous mathematical definition of the contribution of each microbial taxon to metabolite variation, and then examining these contributions in simulated datasets of microbial community metabolism. We found that standard correlation-based analysis of our simulated microbiome-metabolome datasets identifies true contributions with very low predictive value, and that its performance depends strongly on specific properties of both metabolites and microbes, as well as on the surrounding environment. Combined, our findings can guide future interpretation and validation of microbiome-metabolome studies.

microbiology↗

bioRxiv2017

BURRITO: An interactive multi-omic tool for visualizing taxa-function relationships in microbiome data

The abundance of both taxonomic groups and gene categories in microbiome samples can now be easily assayed via various sequencing technologies, and visualized using a variety of software tools. However, the assemblage of taxa in the microbiome and its gene content are clearly linked, and tools for visualizing the relationship between these two facets of microbiome composition and for facilitating exploratory analysis of their co-variation are lacking. Here we introduce BURRITO, a web tool for interactive visualization of microbiome multi-omic data with paired taxonomic and functional information. BURRITO simultaneously visualizes the taxonomic and functional compositions of multiple samples and dynamically highlights relationships between taxa and functions to capture the underlying structure of these data. Users can browse for taxa and functions of interest and interactively explore the share of each function attributed to each taxon across samples. BURRITO supports multiple input formats for taxonomic and metagenomic data, allows adjustment of data granularity, and can export generated visualizations as static publication-ready formatted figures. In this paper, we describe the functionality of BURRITO, and provide illustrative examples of its utility for visualizing various trends in the relationship between the composition of taxa and functions in complex microbiomes.

bioinformatics↗