scholarly journals Cloud‐based archived metabolomics data: A resource for in‐source fragmentation/annotation, meta‐analysis and systems biology

2020 ◽  
Author(s):  
Amelia Palermo ◽  
Tao Huan ◽  
Duane Rinehart ◽  
Markus M. Rinschen ◽  
Shuzhao Li ◽  
...  
Keyword(s):  
Systems Biology ◽  
Meta Analysis ◽  
Metabolomics Data

scholarly journals Systems biology analysis using a genome-scale metabolic model shows that phosphine triggers global metabolic suppression in a resistant strain of C. elegans

2017 ◽  
Author(s):  
Li Ma ◽  
Angelo Hoi Chung Chan ◽  
Jake Hattwell ◽  
Paul R. Ebert ◽  
Horst Joachim Schirra
Keyword(s):  
Systems Biology ◽  
Resistant Strain ◽  
Metabolic Model ◽  
Loss Of Function ◽  
Metabolic Resistance ◽  
Metabolomics Data ◽  
C Elegans ◽  
Phosphine Resistance ◽  
A Genome ◽  
Genome Scale

AbstractBackgroundPest insects are increasingly resistant to phosphine gas, which is used globally to protect grain reserves. The enzyme dihydrolipoamide dehydrogenase (DLD) is a phosphine resistance factor and participates in four key steps of core metabolism, making it a potential central metabolic regulator.ResultsHere we used microarray data and NMR-based metabolomics to characterize the phosphine response of wild-type C. elegans and the phosphine-resistant strain dld-1(wr4) which has a partial loss-of-function mutation in the gene for DLD. In addition, we have constructed CeCon, a C. elegans genome-scale metabolic model to facilitate integration of gene expression and metabolomics data.ConclusionsThe resulting systems biology analysis is consistent with the hypothesis that adaptation to a hypometabolic state is the most prominent mechanism of phosphine resistance in this nematode strain. The involvement of DLD in regulating and creating hypometabolic adaptation has implications for other biological phenomena involving hypometabolism, such as reperfusion injury and metabolic resistance.

scholarly journals Bringing metabolomics data into the forefront of systems biology

2006 ◽  
Vol 2 (1) ◽  
Author(s):  
Steve Van Dien ◽  
Christophe H Schilling
Keyword(s):  
Systems Biology ◽  
Metabolomics Data

scholarly journals From correlation to causation: analysis of metabolomics data using systems biology approaches

Metabolomics ◽  
2018 ◽  
Vol 14 (4) ◽  
Author(s):  
Antonio Rosato ◽  
Leonardo Tenori ◽  
Marta Cascante ◽  
Pedro Ramon De Atauri Carulla ◽  
Vitor A. P. Martins dos Santos ◽  
...  
Keyword(s):  
Systems Biology ◽  
Metabolomics Data ◽  
Causation Analysis

scholarly journals Identifying novel glioma associated pathways based on systems biology level meta-analysis

2013 ◽  
Vol 7 (Suppl 2) ◽  
pp. S9 ◽  
Author(s):  
Yangfan Hu ◽  
Jinquan Li ◽  
Wenying Yan ◽  
Jiajia Chen ◽  
Yin Li ◽  
...  
Keyword(s):  
Systems Biology ◽  
Meta Analysis

scholarly journals Ontology-based metabolomics data integration with quality control

Bioanalysis ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 1139-1154 ◽  
Author(s):  
Patricia Buendia ◽  
Ray M Bradley ◽  
Thomas J Taylor ◽  
Emma L Schymanski ◽  
Gary J Patti ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Integration ◽  
Meta Analysis ◽  
Metabolite Identification ◽  
Integrated System ◽  
Background Information ◽  
Metabolomics Data ◽  
Rule Based System ◽  
Acquisition Strategies ◽  
Chemical Background

Aim: The complications that arise when performing meta-analysis of datasets from multiple metabolomics studies are addressed with computational methods that ensure data quality, completeness of metadata and accurate interpretation across studies. Results & methodology: This paper presents an integrated system of quality control (QC) methods to assess metabolomics results by evaluating the data acquisition strategies and metabolite identification process when integrating datasets for meta-analysis. An ontology knowledge base and a rule-based system representing the experiment and chemical background information direct the processes involved in data integration and QC verification. A diabetes meta-analysis study using these QC methods finds putative biomarkers that differ between cohorts. Conclusion: The methods presented here ensure the validity of meta-analysis when integrating data from different metabolic profiling studies.

scholarly journals Epigenome-wide meta-analysis of PTSD across 10 military and civilian cohorts identifies novel methylation loci

2019 ◽  
Author(s):  
Alicia K Smith ◽  
Andrew Ratanatharathorn ◽  
Adam X Maihofer ◽  
Robert K Naviaux ◽  
Allison E Aiello ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Stress Disorder ◽  
Smoking Status ◽  
Meta Analysis ◽  
Biological Processes ◽  
Metabolomics Data ◽  
Cpg Sites ◽  
Aryl Hydrocarbon ◽  
Analysis Strategy ◽  
Insight Into

AbstractDifferences in susceptibility to posttraumatic stress disorder (PTSD) may be related to epigenetic differences between PTSD cases and trauma-exposed controls. Such epigenetic differences may provide insight into the biological processes underlying the disorder. Here we describe the results of the largest DNA methylation meta-analysis of PTSD to date with data from the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup. Ten cohorts, military and civilian, contributed blood-derived DNA methylation data (HumanMethylation450 BeadChip) from 1,896 PTSD cases (42%) and trauma-exposed controls (58%). Utilizing a common QC and analysis strategy, we identified ten CpG sites associated with PTSD (9.61E-07<p<4.72E-11) after adjustment for multiple comparisons (FDR<.05). Several CpGs were located in genes previously implicated in PTSD and other psychiatric disorders. The top four CpG sites fell within the aryl-hydrocarbon receptor repressor (AHRR) locus and were associated with lower DNA methylation in PTSD cases relative to controls. Interestingly, this association appeared to uncorrelated with smoking status and was most pronounced in non-smokers with PTSD. Additional evaluation of metabolomics data supported our findings and revealed that AHRR methylation associated with kynurenine levels, which were lower among subjects with PTSD relative to controls. Overall, this study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD.

scholarly journals LipidLynxX: a data transfer hub to support integration of large scale lipidomics datasets

2020 ◽  
Author(s):  
Zhixu Ni ◽  
Maria Fedorova
Keyword(s):  
Systems Biology ◽  
Data Integration ◽  
High Throughput ◽  
Large Scale ◽  
Data Transfer ◽  
Structural Information ◽  
Meta Analysis ◽  
Health And Disease ◽  
Different Levels ◽  
Cross Match

AbstractModern high throughput lipidomics provides large-scale datasets reporting hundreds of lipid molecular species. However, cross-laboratory comparison, meta-analysis, and systems biology integration of in-house generated and published datasets remain challenging due to a high diversity of used lipid annotation systems, different levels of reported structural information, and shortage in links to data integration resources. To support lipidomics data integration and interoperability of experimental lipidomics with data integration tools, we developed LipidLynxX serving as a hub facilitating data flow from high-throughput lipidomics analysis to systems biology data integration. LipidLynxX provides the possibility to convert, cross-match, and link various lipid annotations to the tools supporting lipid ontology, pathway, and network analysis aiming systems-wide integration and functional annotation of lipidome dynamics in health and disease. LipidLynxX is a flexible, customizable open-access tool freely available for download at https://github.com/SysMedOs/LipidLynxX.

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