scholarly journals Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data

mSystems ◽  
2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Daniel Ruiz-Perez ◽  
Jose Lugo-Martinez ◽  
Natalia Bourguignon ◽  
Kalai Mathee ◽  
Betiana Lerner ◽  
...  
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Joint Modeling ◽  
Dynamic Bayesian Networks ◽  
Series Data ◽  
Omics Data ◽  
Computational Pipeline ◽  
Metabolomics Data ◽  
Experimental Validations ◽  
Microbiome Data

ABSTRACT A key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites that they consume and produce, and host genes. To address these challenges, we developed a computational pipeline, a pipeline for the analysis of longitudinal multi-omics data (PALM), that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxon interactions. IMPORTANCE While a number of large consortia collect and profile several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses dynamic Bayesian networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes, and the metabolites that they produce and consume, as well as their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

scholarly journals Dynamic Bayesian networks for integrating multi-omics time-series microbiome data

2019 ◽  
Author(s):  
Daniel Ruiz-Perez ◽  
Jose Lugo-Martinez ◽  
Natalia Bourguignon ◽  
Kalai Mathee ◽  
Betiana Lerner ◽  
...  
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Joint Modeling ◽  
Dynamic Bayesian Networks ◽  
Series Data ◽  
Omics Data ◽  
Computational Pipeline ◽  
Metabolomics Data ◽  
Experimental Validations ◽  
Microbiome Data

ABSTRACTA key challenge in the analysis of longitudinal microbiome data is the inference of temporal interactions between microbial taxa, their genes, the metabolites they consume and produce, and host genes. To address these challenges we developed a computational pipeline, PALM, that first aligns multi-omics data and then uses dynamic Bayesian networks (DBNs) to reconstruct a unified model. Our approach overcomes differences in sampling and progression rates, utilizes a biologically-inspired multi-omic framework, reduces the large number of entities and parameters in the DBNs, and validates the learned network. Applying PALM to data collected from inflammatory bowel disease patients, we show that it accurately identifies known and novel interactions. Targeted experimental validations further support a number of the predicted novel metabolite-taxa interactions.Source code and data will be freely available after publication under the MIT Open Source license agreement on our GitHub page.IMPORTANCEWhile a number of large consortia are collecting and profiling several different types of microbiome and genomic time series data, very few methods exist for joint modeling of multi-omics data sets. We developed a new computational pipeline, PALM, which uses Dynamic Bayesian Networks (DBNs) and is designed to integrate multi-omics data from longitudinal microbiome studies. When used to integrate sequence, expression, and metabolomics data from microbiome samples along with host expression data, the resulting models identify interactions between taxa, their genes and the metabolites they produce and consume, and their impact on host expression. We tested the models both by using them to predict future changes in microbiome levels, and by comparing the learned interactions to known interactions in the literature. Finally, we performed experimental validations for a few of the predicted interactions to demonstrate the ability of the method to identify novel relationships and their impact.

Time Series: Dynamic Bayesian Networks

2021 ◽  
pp. 79-90
Author(s):  
Marco Scutari ◽  
Jean-Baptiste Denis
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Dynamic Bayesian Networks

Cancer classification from time series microarray data through regulatory Dynamic Bayesian Networks

2020 ◽  
Vol 116 ◽  
pp. 103577 ◽  
Author(s):  
Konstantina Kourou ◽  
George Rigas ◽  
Costas Papaloukas ◽  
Michalis Mitsis ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Time Series ◽  
Bayesian Networks ◽  
Microarray Data ◽  
Dynamic Bayesian Networks ◽  
Cancer Classification ◽  
Time Series Microarray

scholarly journals Dynamic interaction network inference from longitudinal microbiome data

2018 ◽  
Author(s):  
Jose Lugo-Martinez ◽  
Daniel Ruiz-Perez ◽  
Giri Narasimhan ◽  
Ziv Bar-Joseph
Keyword(s):  
Network Inference ◽  
Dynamic Models ◽  
Dynamic Bayesian Network ◽  
Interaction Network ◽  
Predictive Performance ◽  
Dynamic Bayesian Networks ◽  
Data Sets ◽  
Computational Pipeline ◽  
Novel Interactions ◽  
Microbiome Data

AbstractBackgroundSeveral studies have focused on the microbiota living in environmental niches including human body sites. In many of these studies researchers collect longitudinal data with the goal of understanding not just the composition of the microbiome but also the interactions between the different taxa. However, analysis of such data is challenging and very few methods have been developed to reconstruct dynamic models from time series microbiome data.ResultsHere we present a computational pipeline that enables the integration of data across individuals for the reconstruction of such models. Our pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network which represents causal relationships between taxa and clinical variables. Testing our methods on three longitudinal microbiome data sets we show that our pipeline improve upon prior methods developed for this task. We also discuss the biological insights provided by the models which include several known and novel interactions.ConclusionsWe propose a computational pipeline for analyzing longitudinal microbiome data. Our results provide evidence that microbiome alignments coupled with dynamic Bayesian networks improve predictive performance over previous methods and enhance our ability to infer biological relationships within the microbiome and between taxa and clinical factors.

scholarly journals TimeNexus: A Novel Cytoscape App to Analyze Time-Series Data Using Temporal MultiLayer Networks (tMLNs)

2020 ◽  
Author(s):  
Michaël Pierrelée ◽  
Ana Reynders ◽  
Fabrice Lopez ◽  
Aziz Moqrich ◽  
Laurent Tichit ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Time Series ◽  
Network Structure ◽  
Time Series Data ◽  
Series Data ◽  
Omics Data ◽  
Expression Data ◽  
Temporal Expression ◽  
Multilayer Networks ◽  
Multilayer Network

Abstract Integrating -omics data with biological networks such as protein-protein interaction networks is a popular and useful approach to interpret expression changes of genes in changing conditions, and to identify relevant cellular pathways, active subnetworks or network communities. Yet, most -omics data integration tools are restricted to static networks and therefore cannot easily be used for analyzing time-series data. Determining regulations or exploring the network structure over time requires time-dependent networks which incorporate time as one component in their structure. Here, we present a method to project time-series data on sequential layers of a multilayer network, thus creating a temporal multilayer network (tMLN). We implemented this method as a Cytoscape app we named TimeNexus. TimeNexus allows to easily create, manage and visualize temporal multilayer networks starting from a combination of node and edge tables carrying the information on the temporal network structure. To allow further analysis of the tMLN, TimeNexus creates and passes on regular Cytoscape networks in form of static versions of the tMLN in three different ways: i) over the entire set of layers, ii) over two consecutive layers at a time, iii) or on one single layer at a time. We combined TimeNexus with the Cytoscape apps PathLinker and AnatApp/ANAT to extract active subnetworks from tMLNs. To test the usability of our app, we applied TimeNexus together with PathLinker or ANAT on temporal expression data of the yeast cell cycle and were able to identify active subnetworks relevant for different cell cycle phases. We furthermore used TimeNexus on our own temporal expression data from a mouse pain assay inducing hindpaw inflammation and detected active subnetworks relevant for an inflammatory response to injury, including immune response, cell stress response and regulation of apoptosis. TimeNexus is freely available from the Cytoscape app store at https://apps.cytoscape.org/apps/TimeNexus.

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