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Article ; Online: GraphOmics: an interactive platform to explore and integrate multi-omics data.

Wandy, Joe / Daly, Rónán

2021 Volume 22, Issue 1, Page(s) 603

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 ...

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.
MeSH term(s)	Animals ; COVID-19 ; Humans ; Metabolomics ; Proteomics ; SARS-CoV-2 ; Zebrafish/genetics
Language	English
Publishing date	2021-12-18
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-021-04500-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: GraphOmics

Joe Wandy / Rónán Daly

BMC Bioinformatics, Vol 22, Iss 1, Pp 1-

an interactive platform to explore and integrate multi-omics data

2021 Volume 19

Abstract: 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 ... ...

Abstract	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.
Keywords	Omics integration ; Data exploration ; Visualisation ; Pathway analysis ; Reactome ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments.

McBride, Ross / Wandy, Joe / Weidt, Stefan / Rogers, Simon / Davies, Vinny / Daly, Rónán / Bryson, Kevin

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 7

Abstract: Motivation: Liquid Chromatography Tandem Mass Spectrometry experiments aim to produce high-quality fragmentation spectra, which can be used to annotate metabolites. However, current Data-Dependent Acquisition approaches may fail to collect spectra of ... ...

Abstract	Motivation: Liquid Chromatography Tandem Mass Spectrometry experiments aim to produce high-quality fragmentation spectra, which can be used to annotate metabolites. However, current Data-Dependent Acquisition approaches may fail to collect spectra of sufficient quality and quantity for experimental outcomes, and extend poorly across multiple samples by failing to share information across samples or by requiring manual expert input. Results: We present TopNEXt, a real-time scan prioritization framework that improves data acquisition in multi-sample Liquid Chromatography Tandem Mass Spectrometry metabolomics experiments. TopNEXt extends traditional Data-Dependent Acquisition exclusion methods across multiple samples by using a Region of Interest and intensity-based scoring system. Through both simulated and lab experiments, we show that methods incorporating these novel concepts acquire fragmentation spectra for an additional 10% of our set of target peaks and with an additional 20% of acquisition intensity. By increasing the quality and quantity of fragmentation spectra, TopNEXt can help improve metabolite identification with a potential impact across a variety of experimental contexts. Availability and implementation: TopNEXt is implemented as part of the ViMMS framework and the latest version can be found at https://github.com/glasgowcompbio/vimms. A stable version used to produce our results can be found at 10.5281/zenodo.7468914.
MeSH term(s)	Mass Spectrometry/methods ; Chromatography, Liquid/methods ; Metabolomics/methods
Language	English
Publishing date	2023-06-26
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad406
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Article: Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics.

Wandy, Joe / McBride, Ross / Rogers, Simon / Terzis, Nikolaos / Weidt, Stefan / van der Hooft, Justin J J / Bryson, Kevin / Daly, Rónán / Davies, Vinny

Frontiers in molecular biosciences

2023 Volume 10, Page(s) 1130781

Abstract: Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little ... ...

Abstract	Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic
Language	English
Publishing date	2023-03-07
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2023.1130781
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Article: Ranking Metabolite Sets by Their Activity Levels.

McLuskey, Karen / Wandy, Joe / Vincent, Isabel / van der Hooft, Justin J J / Rogers, Simon / Burgess, Karl / Daly, Rónán

Metabolites

2021 Volume 11, Issue 2

Abstract: Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such ...

Abstract	Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. The main algorithm in PALS is based on the pathway level analysis of gene expression (PLAGE) factorisation method and is denoted as mPLAGE (PLAGE for metabolomics). As an example of an application, PALS is used to analyse metabolites grouped as metabolic pathways and by shared tandem mass spectrometry fragmentation patterns. A comparison of mPLAGE with two other commonly used methods (overrepresentation analysis (ORA) and gene set enrichment analysis (GSEA)) is also given and reveals that mPLAGE is more robust to missing features and noisy data than the alternatives. As further examples, PALS is also applied to human African trypanosomiasis, Rhamnaceae, and American Gut Project data. In addition, normalisation can have a significant impact on pathway analysis results, and PALS offers a framework to further investigate this. PALS is freely available from our project Web site.
Language	English
Publishing date	2021-02-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo11020103
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Article ; Online: Rapid Development of Improved Data-Dependent Acquisition Strategies.

Davies, Vinny / Wandy, Joe / Weidt, Stefan / van der Hooft, Justin J J / Miller, Alice / Daly, Rónán / Rogers, Simon

Analytical chemistry

2021 Volume 93, Issue 14, Page(s) 5676–5683

Abstract: Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based upon intensities observed in MS1 survey scans and typically only fragments a ... ...

Abstract	Tandem mass spectrometry (LC-MS/MS) is widely used to identify unknown ions in untargeted metabolomics. Data-dependent acquisition (DDA) chooses which ions to fragment based upon intensities observed in MS1 survey scans and typically only fragments a small subset of the ions present. Despite this inefficiency, relatively little work has addressed the development of new DDA methods, partly due to the high overhead associated with running the many extracts necessary to optimize approaches in busy MS facilities. In this work, we first provide theoretical results that show how much improvement is possible over current DDA strategies. We then describe an in silico framework for fast and cost-efficient development of new DDA strategies using a previously developed virtual metabolomics mass spectrometer (ViMMS). Additional functionality is added to ViMMS to allow methods to be used both in simulation and on real samples via an Instrument Application Programming Interface (IAPI). We demonstrate this framework through the development and optimization of two new DDA methods that introduce new advanced ion prioritization strategies. Upon application of these developed methods to two complex metabolite mixtures, our results show that they are able to fragment more unique ions than standard DDA strategies.
Language	English
Publishing date	2021-03-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1508-8
ISSN	1520-6882 ; 0003-2700
ISSN (online)	1520-6882
ISSN	0003-2700
DOI	10.1021/acs.analchem.0c03895
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Article ; Online: Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics

Joe Wandy / Ross McBride / Simon Rogers / Nikolaos Terzis / Stefan Weidt / Justin J. J. van der Hooft / Kevin Bryson / Rónán Daly / Vinny Davies

Frontiers in Molecular Biosciences, Vol

2023 Volume 10

Abstract	Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for ...
Keywords	liquid chromatography tandem mass spectrometry ; metabolomics ; data-dependent acquisition ; data independent acquisition ; digital twin ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
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Article: ShinyKGode: an interactive application for ODE parameter inference using gradient matching

Wandy, Joe / Niu, Mu / Giurghita, Diana / Daly, Rónán / Rogers, Simon / Husmeier, Dirk / Wren, Jonathan

Bioinformatics. 2018 July 01, v. 34, no. 13

2018

Abstract: Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to ... ...

Abstract	Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualized alongside diagnostic plots to assess convergence. The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. Supplementary data are available at Bioinformatics online.
Keywords	Internet ; bioinformatics ; computer software ; differential equation ; expert opinion ; mathematical models
Language	English
Dates of publication	2018-0701
Size	p. 2314-2315.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	1468345-3
ISSN	1460-2059 ; 1367-4811 ; 1367-4803
ISSN (online)	1460-2059 ; 1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty089
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets.

Wandy, Joe / Daly, Rónán / Breitling, Rainer / Rogers, Simon

Bioinformatics (Oxford, England)

2015 Volume 31, Issue 12, Page(s) 1999–2006

Abstract: Motivation: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is ... ...

Abstract	Motivation: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that coelute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pair-wise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result. Results: We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools. Availability: The proposed alignment method has been implemented as a stand-alone application in Python, available for download at http://github.com/joewandy/peak-grouping-alignment.
MeSH term(s)	Algorithms ; Chromatography, Liquid/methods ; Glycomics/methods ; Humans ; Mass Spectrometry/methods ; Metabolomics/methods ; Peptide Fragments/analysis ; Proteomics/methods
Chemical Substances	Peptide Fragments
Language	English
Publishing date	2015-02-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btv072
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Article ; Online: ShinyKGode: an interactive application for ODE parameter inference using gradient matching.

Wandy, Joe / Niu, Mu / Giurghita, Diana / Daly, Rónán / Rogers, Simon / Husmeier, Dirk

Bioinformatics (Oxford, England)

2018 Volume 34, Issue 13, Page(s) 2314–2315

Abstract: Motivation: Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown ... ...

Abstract	Motivation: Mathematical modelling based on ordinary differential equations (ODEs) is widely used to describe the dynamics of biological systems, particularly in systems and pathway biology. Often the kinetic parameters of these ODE systems are unknown and have to be inferred from the data. Approximate parameter inference methods based on gradient matching (which do not require performing computationally expensive numerical integration of the ODEs) have been getting popular in recent years, but many implementations are difficult to run without expert knowledge. Here, we introduce ShinyKGode, an interactive web application to perform fast parameter inference on ODEs using gradient matching. Results: ShinyKGode can be used to infer ODE parameters on simulated and observed data using gradient matching. Users can easily load their own models in Systems Biology Markup Language format, and a set of pre-defined ODE benchmark models are provided in the application. Inferred parameters are visualized alongside diagnostic plots to assess convergence. Availability and implementation: The R package for ShinyKGode can be installed through the Comprehensive R Archive Network (CRAN). Installation instructions, as well as tutorial videos and source code are available at https://joewandy.github.io/shinyKGode. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Kinetics ; Software ; Systems Biology/methods
Language	English
Publishing date	2018-03-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/bty089
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