scholarly journals Visualizing metabolic network dynamics through time-series metabolomics data

2018 ◽  
Author(s):  
Lea F. Buchweitz ◽  
James T. Yurkovich ◽  
Christoph M. Blessing ◽  
Veronika Kohler ◽  
Fabian Schwarzkopf ◽  
...  
Keyword(s):  
Time Series ◽  
Metabolic Network ◽  
Time Course ◽  
Human Platelet ◽  
New Method ◽  
Omics Data ◽  
Visualization Technique ◽  
Data Types ◽  
Metabolomics Data

ABSTRACTNew technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine.The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.In conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.AUTHOR SUMMARYProfiling the dynamic state of a metabolic network through the use of time-course metabolomics technologies allows insights into cellular biochemistry. Interpreting these data together at the systems level provides challenges that can be addressed through the development of new visualization approaches. Here, we present a new method for the visualization of time-course metabolomics data that integrates data into an existing metabolic network map. In brief, the metabolomics data are visualized directly on a network map with dynamic elements (nodes that either change size, fill level, or color corresponding with the concentration) while the user controls the time series (i.e., which time point is being displayed) through a graphical interface. We provide short videos that illustrate the utility of this method through its application to existing data sets for the human platelet and erythrocyte. The results presented here give blueprints for the development of visualization methods for other time-course -omics data types that attempt to understand systems-level physiology.

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.

Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data

2016 ◽  
Vol 59 (1) ◽  
pp. 172-191 ◽  
Author(s):  
Viktorian Miok ◽  
Saskia M. Wilting ◽  
Wessel N. van Wieringen
Keyword(s):  
Time Series ◽  
Time Course ◽  
Omics Data ◽  
Chain Graph ◽  
Ridge Estimation

scholarly journals GraphOmics: an interactive platform to explore and integrate multi-omics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Joe Wandy ◽  
Rónán Daly
Keyword(s):  
Data Integration ◽  
Computational Methods ◽  
Statistical Significance ◽  
Hypothesis Generation ◽  
Omics Data ◽  
Metabolomics Data ◽  
Context Sensitive ◽  
Biological Entities ◽  
User Friendly

Abstract Background An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for deeper understanding and insights to be gained from the study. In particular, data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. Results Here we present GraphOmics, a user-friendly platform to explore and integrate multiple omics datasets and support hypothesis generation. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generation using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. Conclusions GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org/. It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that by using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

scholarly journals Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

2021 ◽  
Author(s):  
Takoua Jendoubi
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Analysis Pipeline ◽  
Metabolomics Data ◽  
Computational Tools ◽  
Complex Chemical Reactions ◽  
Living Organisms ◽  
Underlying Mechanisms ◽  
Omics Data Integration

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

Detecting and Correcting Biases in Long-Term Ocean Observatory Time Series: Case Study on Current Directions Estimated From Acoustic Doppler Current Profiler Data

2016 ◽  
Vol 50 (3) ◽  
pp. 109-113
Author(s):  
Michael G. Morley ◽  
Marlene A. Jeffries ◽  
Steven F. Mihály ◽  
Reyna Jenkyns ◽  
Ben R. Biffard
Keyword(s):  
Time Series ◽  
Acoustic Doppler Current Profiler ◽  
Lessons Learned ◽  
Ocean Current ◽  
Data Types ◽  
Current Profiler ◽  
Future Data ◽  
Multiple Deployments

AbstractOcean Networks Canada (ONC) operates the NEPTUNE and VENUS cabled ocean observatories to collect continuous data on physical, chemical, biological, and geological ocean conditions over multiyear time periods. Researchers can download real-time and historical data from a large variety of instruments to study complex earth and ocean processes from their home laboratories. Ensuring that the users are receiving the most accurate data is a high priority at ONC, requiring QAQC (quality assurance and quality control) procedures to be developed for a variety of data types (Abeysirigunawardena et al., 2015). Acquiring long-term time series of oceanographic data from remote locations on the seafloor presents significant challenges from a QAQC perspective. In order to identify and study important scientific events and trends, data consolidated from multiple deployments and instruments need to be self-consistent and free of biases due to changes to instrument configurations, calibrations, metadata, biofouling, or a degradation in instrument performance. As a case study, this paper describes efforts at ONC to identify and correct systematic biases in ocean current directions measured by ADCPs (acoustic Doppler current profilers), as well as the lessons learned to improve future data quality.

scholarly journals Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Metabolites ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 184
Author(s):  
Takoua Jendoubi
Keyword(s):  
Integrative Analysis ◽  
Omics Data ◽  
Data Types ◽  
Analysis Pipeline ◽  
Metabolomics Data ◽  
Computational Tools ◽  
Complex Chemical Reactions ◽  
Living Organisms ◽  
Underlying Mechanisms ◽  
Omics Data Integration

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria—hypothesis, data types, strategies, study design and study focus— to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

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 GraphOmics: an Interactive Platform to Explore and Integrate Multi-Omics Data

2021 ◽  
Author(s):  
Joe Wandy ◽  
Ronan Daly
Keyword(s):  
Data Integration ◽  
Computational Methods ◽  
Statistical Significance ◽  
Omics Data ◽  
Metabolomics Data ◽  
P Values ◽  
Context Sensitive ◽  
Biological Entities ◽  
User Friendly

An increasing number of studies now produce multiple omics measurements that require using sophisticated computational methods for analysis. While each omics data can be examined separately, jointly integrating multiple omics data allows for a deeper understanding and insights to be gained from the study. In particular data integration can be performed horizontally, where biological entities from multiple omics measurements are mapped to common reactions and pathways. However, data integration remains a challenge due to the complexity of the data and the difficulty in interpreting analysis results. Here we present GraphOmics, a user-friendly platform to explore, integrate multiple omics datasets and support hypothesis generations. Users can upload transcriptomics, proteomics and metabolomics data to GraphOmics. Relevant entities are connected based on their biochemical relationships, and mapped to reactions and pathways from Reactome. From the Data Browser in GraphOmics, mapped entities and pathways can be ranked, sorted and filtered according to their statistical significance (p-values) and fold changes. Context-sensitive panels provide information on the currently selected entities, while interactive heatmaps and clustering functionalities are also available. As a case study, we demonstrated how GraphOmics was used to interactively explore multi-omics data and support hypothesis generations using two complex datasets from existing Zebrafish regeneration and Covid-19 human studies. GraphOmics is fully open-sourced and freely accessible from https://graphomics.glasgowcompbio.org. It can be used to integrate multiple omics data horizontally by mapping entities across omics to reactions and pathways. Our demonstration showed that using interactive explorations from GraphOmics, interesting insights and biological hypotheses could be rapidly revealed.

scholarly journals Tag Clouds for Software Documents Visualization

2019 ◽  
Vol 3 (4) ◽  
Author(s):  
Ra'fat Ahmad Al-msie'deen
Keyword(s):  
New Method ◽  
Font Size ◽  
Visualization Technique ◽  
Software Developers ◽  
Alphabetical Order ◽  
Legacy Software ◽  
Tag Clouds ◽  
Tag Cloud

Legacy software documents are hard to understand and visualize. The tag cloud technique helps software developers to visualize the contents of software documents. A tag cloud is a well-known and simple visualization technique. This paper proposes a new method to visualize software documents, using a tag cloud. In this paper, tags visualize in the cloud based on their frequency in an alphabetical order. The most important tags are displayed with a larger font size. The originality of this method is that it visualizes the contents of JavaDoc as a tag cloud. To validate the JavaDocCloud method, it was applied to NanoXML case study, the results of these experiments display the most common and uncommon tags used in the software documents.

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