Biology subjects

Joseph, T. A.

Publications and source records attributed to Joseph, T. A..

5 recordsLinked to original sources

bioRxiv2021

Directional Gaussian Mixture Models of the gut microbiome elucidate microbial spatial structure

The gut microbiome is spatially heterogeneous, with environmental niches contributing to the distribution and composition of microbial populations. A recently developed mapping technology, MaPS-seq, aims to characterize the spatial organization of the gut microbiome by providing data about local microbial populations. However, information about the global arrangement of these populations is lost by MaPS-seq. To address this, we propose a class of Gaussian Mixture Models (GMM) with spatial dependencies between mixture components in order to computationally recover the relative spatial arrangement of microbial communities. We demonstrate on synthetic data that our spatial models can identify global spatial dynamics, accurately cluster data, and improve parameter inference over a naive GMM. We applied our model to three MaPS-Seq datasets taken from varying regions of the mouse intestine. On cecal and distal colon datasets, we find our model accurately recapitulates known spatial behaviors of the gut microbiome, including compositional differences between mucus and lumen-associated populations. Our model also seem to capture the role of a pH gradient on microbial populations in the mouse ileum and proposes new behaviors as well. ImportanceThe spatial arrangement of the microbes in the gut microbiome is a defining characteristic of its behavior. Various experimental studies have attempted to provide glimpses into the mechanisms that contribute to microbial arrangements. However, many of these descriptions are qualitative. We developed a computational method that takes microbial spatial data and learns many of the experimentally validated spatial factors. We can then use our model to propose previously unknown spatial behaviors. Our results demonstrate that the gut microbiome, while exceptionally large, has predictable spatial patterns that can be used to help us understand its role in health and disease. Code availabilitygithub.com/amepas/Spatial_Mbiome

bioinformatics↗

bioRxiv2021

Accurate and robust inference of microbial growth dynamics from metagenomic sequencing

Patterns of sequencing coverage along a bacterial genome--summarized by a peak-to-trough ratio (PTR)--have been shown to accurately reflect microbial growth rates, revealing a new facet of microbial dynamics and host-microbe interactions. Here, we introduce CoPTR (Compute PTR): a tool for computing PTRs from complete reference genomes and assemblies. We show that CoPTR is more accurate than the current state-of-the-art, while also providing more PTR estimates overall. We further develop theory formalizing a biological interpretation for PTRs. Using a reference database of 2935 species, we applied CoPTR to a case-control study of 1304 metagenomic samples from 106 individuals with irritable bowel disease. We show that PTRs have high inter-individual variation, are only loosely correlated with relative abundances, and are associated with disease status. We conclude by demonstrating how PTRs can be combined with relative abundances and metabolomics to investigate their effect on the microbiome. AvailabilityCoPTR is available from https://github.com/tyjo/coptr, with documentation on https://coptr.readthedocs.io.

bioinformatics↗

bioRxiv2020

Efficient and accurate inference of microbial trajectories from longitudinal count data

The recently completed second phase of the Human Microbiome Project has highlighted the relationship between dynamic changes in the microbiome and disease, motivating new microbiome study designs based on longitudinal sampling. Yet, analysis of such data is hindered by presence of technical noise, high dimensionality, and data sparsity. To address these challenges, we propose LUMINATE (LongitUdinal Microbiome INference And zero deTEction), a fast and accurate method for inferring relative abundances from noisy read count data. We demonstrate on synthetic data that LUMINATE is orders of magnitude faster than current approaches, with better or similar accuracy. This translates to feasibility of analyzing data at the requisite dimensionality for current studies. We further show that LUMINATE can accurately distinguish biological zeros, when a taxon is absent from the community, from technical zeros, when a taxon is below the detection threshold. We conclude by demonstrating the utility of LUMINATE for downstream analysis by using estimates of latent relative abundances to fit the parameters of a dynamical system, leading to more accurate predictions of community dynamics. Code availabilityhttps://github.com/tyjo/luminate

bioinformatics↗

bioRxiv2019

Population history from the Neolithic to present on the Mediterranean island of Sardinia: An ancient DNA perspective

Recent ancient DNA studies of western Eurasia have revealed a dynamic history of admixture, with evidence for major migrations during the Neolithic and Bronze Age. The population of the Mediterranean island of Sardinia has been notable in these studies - Neolithic individuals from mainland Europe cluster more closely with Sardinian individuals than with all other present-day Europeans. The current model to explain this result is that Sardinia received an initial influx of Neolithic ancestry and then remained relatively isolated from expansions in the later Neolithic and Bronze Age that took place in continental Europe. To test this model, we generated genome-wide capture data (approximately 1.2 million variants) for 43 ancient Sardinian individuals spanning the Neolithic through the Bronze Age, including individuals from Sardinias Nuragic culture, which is known for the construction of numerous large stone towers throughout the island. We analyze these new samples in the context of previously generated genome-wide ancient DNA data from 972 ancient individuals across western Eurasia and whole-genome sequence data from approximately 1,500 modern individuals from Sardinia. The ancient Sardinian individuals show a strong affinity to western Mediterranean Neolithic populations and we infer a high degree of genetic continuity on the island from the Neolithic (around fifth millennium BCE) through the Nuragic period (second millennium BCE). In particular, during the Bronze Age in Sardinia, we do not find significant levels of the \"Steppe\" ancestry that was spreading in many other parts of Europe at that time. We also characterize subsequent genetic influx between the Nuragic period and the present. We detect novel, modest signals of admixture between 1,000 BCE and present-day, from ancestry sources in the eastern and northern Mediterranean. Within Sardinia, we confirm that populations from the more geographically isolated mountainous provinces have experienced elevated levels of genetic drift and that northern and southwestern regions of the island received more gene flow from outside Sardinia. Overall, our genetic analysis sheds new light on the origin of Neolithic settlement on Sardinia, reinforces models of genetic continuity on the island, and provides enhanced power to detect post-Bronze-Age gene flow. Together, these findings offer a refined demographic model for future medical genetic studies in Sardinia.

genetics↗

bioRxiv2018

Inference of population structure from ancient DNA

Methods for inferring population structure from genetic information traditionally assume samples are contemporary. Yet, the increasing availability of ancient DNA sequences begs revision of this paradigm. We present Dystruct (Dynamic Structure), a framework and toolbox for inference of shared ancestry from data that include ancient DNA. By explicitly modeling population history and genetic drift as a time-series, Dystruct more accurately and realistically discovers shared ancestry from ancient and contemporary samples. Formally, we use a normal approximation of drift, which allows a novel, efficient algorithm for optimizing model parameters using stochastic variational inference. We show that Dystruct outperforms the state of the art when individuals are sampled over time, as is common in ancient DNA datasets. We further demonstrate the utility of our method on a dataset of 92 ancient samples alongside 1941 modern ones genotyped at 222755 loci. Our model tends to present modern samples as the mixtures of ancestral populations they really are, rather than the artifactual converse of presenting ancestral samples as mixtures of contemporary groups.\n\nAvailabilityDystruct is implemented in C++, open-source, and available at https://github.com/tyjo/dystruct.

evolutionary biology↗