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 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 GAIT-GM: Galaxy tools for modeling metabolite changes as a function of gene expression

2020 ◽  
Author(s):  
Lauren M. McIntyre ◽  
Francisco Huertas ◽  
Olexander Moskalenko ◽  
Marta Llansola ◽  
Vicente Felipo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Text Mining ◽  
Kegg Pathway ◽  
Annotation Tool ◽  
Omics Data ◽  
Metabolomics Data ◽  
Pathway Data ◽  
User Friendly ◽  
Omics Data Analysis

AbstractGalaxy is a user-friendly platform with a strong development community and a rich set of tools for omics data analysis. While multi-omics experiments are becoming popular, tools for multi-omics data analysis are poorly represented in this platform. Here we present GAIT-GM, a set of new Galaxy tools for integrative analysis of gene expression and metabolomics data. In the Annotation Tool, features are mapped to KEGG pathway using a text mining approach to increase the number of mapped metabolites. Several interconnected databases are used to maximally map gene IDs across species. In the Integration Tool, changes in metabolite levels are modelled as a function of gene expression in a flexible manner. Both unbiased exploration of relationships between genes and metabolites and biologically informed models based on pathway data are enabled. The GAIT-GM tools are freely available at https://github.com/SECIMTools/gait-gm.

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.

THE VALUE OF DATA INTEGRATION IN A COMPLEX WELL SCENARIO: A CASE STUDY FROM THE WEST NETHERLANDS

2020 ◽  
Author(s):  
Abhinandan Kohli ◽  
◽  
Emile Fokkema ◽  
Oscar Kelder ◽  
Zulkifli Ahmad ◽  
...  
Keyword(s):  
Data Integration ◽  
The West ◽  
Complex Well

Optimales Layout einer Walderschliessung

2016 ◽  
Vol 167 (5) ◽  
pp. 294-301
Author(s):  
Leo Bont
Keyword(s):  
Road Network ◽  
Spatial Arrangement ◽  
Problem Situation ◽  
Optimal Layout ◽  
Forest Road ◽  
The Road ◽  
Network Cost ◽  
User Friendly ◽  
Short Time

Optimal layout of a forest road network The road network is the backbone of forest management. When creating or redesigning a forest road network, one important question is how to shape the layout, this means to fix the spatial arrangement and the dimensioning standard of the roads. We consider two kinds of layout problems. First, new forest road network in an area without any such development yet, and second, redesign of existing road network for actual requirements. For each problem situation, we will present a method that allows to detect automatically the optimal road and harvesting layout. The method aims to identify a road network that concurrently minimizes the harvesting cost, the road network cost (construction and maintenance) and the hauling cost over the entire life cycle. Ecological issues can be considered as well. The method will be presented and discussed with the help of two case studies. The main benefit of the application of optimization tools consists in an objective-based planning, which allows to check and compare different scenarios and objectives within a short time. The responses coming from the case study regions were highly positive: practitioners suggest to make those methods a standard practice and to further develop the prototype to a user-friendly expert software.

Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches

2020 ◽  
Vol 15 ◽  
Author(s):  
Omer Irshad ◽  
Muhammad Usman Ghani Khan
Keyword(s):  
Data Integration ◽  
Semantic Integration ◽  
Biological Data ◽  
Cellular System ◽  
Formal Specifications ◽  
Integration Model ◽  
Geographically Dispersed ◽  
Proposed Model ◽  
Data Heterogeneity ◽  
Biological Entities

Aim: To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations. Background: Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell. Objective: To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data. Method: We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with Result: To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers. Conclusion: Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax

scholarly journals Multi-omics data integration reveals correlated regulatory features of triple negative breast cancer

2021 ◽  
Author(s):  
Kevin Chappell ◽  
Kanishka Manna ◽  
Charity L. Washam ◽  
Stefan Graw ◽  
Duah Alkam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Data Integration ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Biological Pathways ◽  
Omics Data ◽  
Insight Into ◽  
Omics Data Integration

Multi-omics data integration of triple negative breast cancer (TNBC) provides insight into biological pathways.

scholarly journals A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

2021 ◽  
Vol 22 (6) ◽  
pp. 2822
Author(s):  
Efstathios Iason Vlachavas ◽  
Jonas Bohn ◽  
Frank Ückert ◽  
Sylvia Nürnberg
Keyword(s):  
Cancer Research ◽  
Clinical Information ◽  
Disease Diagnosis ◽  
Molecular Data ◽  
Molecular Networks ◽  
Biological Knowledge ◽  
Omics Data ◽  
Translational Cancer Research ◽  
Using Data ◽  
Biological Entities

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

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