scholarly journals MarVis-Pathway: integrative and exploratory pathway analysis of non-targeted metabolomics data

Metabolomics ◽  
2014 ◽  
Vol 11 (3) ◽  
pp. 764-777 ◽  
Author(s):  
Alexander Kaever ◽  
Manuel Landesfeind ◽  
Kirstin Feussner ◽  
Alina Mosblech ◽  
Ingo Heilmann ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Targeted Metabolomics ◽  
Metabolomics Data

scholarly journals Short Report: Using Targeted Urine Metabolomics to Distinguish Between Manganese Exposed and Unexposed Workers in a Small Occupational Cohort

2021 ◽  
Vol 9 ◽  
Author(s):  
Kayla A. Carter ◽  
Christopher D. Simpson ◽  
Daniel Raftery ◽  
Marissa G. Baker
Keyword(s):  
Amino Acids ◽  
Pathway Analysis ◽  
Short Report ◽  
Targeted Metabolomics ◽  
Metabolomics Data ◽  
Mn Toxicity ◽  
Spot Urine ◽  
Beta Alanine ◽  
Neurological Outcomes ◽  
Exposed Workers

Objectives: Despite the widespread use of manganese (Mn) in industrial settings and its association with adverse neurological outcomes, a validated and reliable biomarker for Mn exposure is still elusive. Here, we utilize targeted metabolomics to investigate metabolic differences between Mn-exposed and -unexposed workers, which could inform a putative biomarker for Mn and lead to increased understanding of Mn toxicity.Methods: End of shift spot urine samples collected from Mn exposed (n = 17) and unexposed (n = 15) workers underwent a targeted assay of 362 metabolites using LC-MS/MS; 224 were quantified and retained for analysis. Differences in metabolite abundances between exposed and unexposed workers were tested with a Benjamini-Hochberg adjusted Wilcoxon Rank-Sum test. We explored perturbed pathways related to exposure using a pathway analysis.Results: Seven metabolites were significantly differentially abundant between exposed and unexposed workers (FDR ≤ 0.1), including n-isobutyrylglycine, cholic acid, anserine, beta-alanine, methionine, n-isovalerylglycine, and threonine. Three pathways were significantly perturbed in exposed workers and had an impact score >0.5: beta-alanine metabolism, histidine metabolism, and glycine, serine, and threonine metabolism.Conclusion: This is one of few studies utilizing targeted metabolomics to explore differences between Mn-exposed and -unexposed workers. Metabolite and pathway analysis showed amino acid metabolism was perturbed in these Mn-exposed workers. Amino acids have also been shown to be perturbed in other occupational cohorts exposed to Mn. Additional research is needed to characterize the biological importance of amino acids in the Mn exposure-disease continuum, and to determine how to appropriately utilize and interpret metabolomics data collected from occupational cohorts.

CPVA: a web-based metabolomic tool for chromatographic peak visualization and annotation

2020 ◽  
Vol 36 (12) ◽  
pp. 3913-3915
Author(s):  
Hemi Luan ◽  
Xingen Jiang ◽  
Fenfen Ji ◽  
Zhangzhang Lan ◽  
Zongwei Cai ◽  
...  
Keyword(s):  
False Positive ◽  
Supplementary Information ◽  
Liquid Chromatography Mass Spectrometry ◽  
Targeted Metabolomics ◽  
Metabolomics Data ◽  
Web Based ◽  
Tremendous Amount ◽  
Chromatographic Peaks ◽  
User Friendly

Abstract Motivation Liquid chromatography–mass spectrometry-based non-targeted metabolomics is routinely performed to qualitatively and quantitatively analyze a tremendous amount of metabolite signals in complex biological samples. However, false-positive peaks in the datasets are commonly detected as metabolite signals by using many popular software, resulting in non-reliable measurement. Results To reduce false-positive calling, we developed an interactive web tool, termed CPVA, for visualization and accurate annotation of the detected peaks in non-targeted metabolomics data. We used a chromatogram-centric strategy to unfold the characteristics of chromatographic peaks through visualization of peak morphology metrics, with additional functions to annotate adducts, isotopes and contaminants. CPVA is a free, user-friendly tool to help users to identify peak background noises and contaminants, resulting in decrease of false-positive or redundant peak calling, thereby improving the data quality of non-targeted metabolomics studies. Availability and implementation The CPVA is freely available at http://cpva.eastus.cloudapp.azure.com. Source code and installation instructions are available on GitHub: https://github.com/13479776/cpva. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Metabomxtr: an R package for mixture-model analysis of non-targeted metabolomics data

2014 ◽  
Vol 30 (22) ◽  
pp. 3287-3288 ◽  
Author(s):  
Michael Nodzenski ◽  
Michael J. Muehlbauer ◽  
James R. Bain ◽  
Anna C. Reisetter ◽  
William L. Lowe ◽  
...  
Keyword(s):  
Mixture Model ◽  
R Package ◽  
Model Analysis ◽  
Targeted Metabolomics ◽  
Metabolomics Data

scholarly journals PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data

2016 ◽  
Vol 3 ◽  
Author(s):  
Renata Bujak ◽  
Emilia Daghir-Wojtkowiak ◽  
Roman Kaliszan ◽  
Michał J. Markuszewski
Keyword(s):  
Targeted Metabolomics ◽  
Metabolomics Data ◽  
Relevant Variables ◽  
Selection Of

scholarly journals Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

2021 ◽  
Vol 17 (9) ◽  
pp. e1009105
Author(s):  
Cecilia Wieder ◽  
Clément Frainay ◽  
Nathalie Poupin ◽  
Pablo Rodríguez-Mier ◽  
Florence Vinson ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
False Positive ◽  
Best Practice ◽  
Alternative Pathway ◽  
Pathway Database ◽  
Compound Identification ◽  
Functional Interpretation ◽  
Metabolomics Data ◽  
Large Numbers ◽  
And Function

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

scholarly journals A new pipeline for the normalization and pooling of metabolomics data

2021 ◽  
Author(s):  
Vivian Viallon ◽  
Mathilde His ◽  
Sabina Rinaldi ◽  
Marie Breeur ◽  
Audrey Gicquiau ◽  
...  
Keyword(s):  
Mixed Models ◽  
Statistical Power ◽  
Molecular Data ◽  
Proteomics Data ◽  
Targeted Metabolomics ◽  
Case Control Studies ◽  
Metabolomics Data ◽  
Comprehensive Examination ◽  
Residual Variances ◽  
Prospective Investigation

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through PC-PR2 analysis; (iii) application of linear mixed models to remove unwanted variability, including samples originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

scholarly journals Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis

2021 ◽  
Author(s):  
Cecilia Wieder ◽  
Clément Frainay ◽  
Nathalie Poupin ◽  
Pablo Rodríguez-Mier ◽  
Florence Vinson ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
False Positive ◽  
Best Practice ◽  
Alternative Pathway ◽  
Pathway Database ◽  
Compound Identification ◽  
Functional Interpretation ◽  
Metabolomics Data ◽  
Large Numbers ◽  
And Function

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention in the field. We developed  in-silico  simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases: KEGG, Reactome, and BioCyc, led to vastly different results in both the number and function of significantly enriched pathways. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

scholarly journals Widely Targeted Metabolomics Analysis to Reveal Transformation Mechanism of Cistanche Deserticola Active Compounds During Steaming and Drying Processes

2021 ◽  
Vol 8 ◽  
Author(s):  
Ziping Ai ◽  
Yue Zhang ◽  
Xingyi Li ◽  
Wenling Sun ◽  
Yanhong Liu
Keyword(s):  
Pathway Analysis ◽  
Thermal Processing ◽  
Fatty Acid Biosynthesis ◽  
Targeted Metabolomics ◽  
Transformation Mechanism ◽  
Metabolic Pathway Analysis ◽  
Active Compounds ◽  
Cistanche Deserticola ◽  
Widely Targeted Metabolomics ◽  
Metabolomics Analysis

Cistanche deserticola is one of the most precious plants, traditionally as Chinese medicine, and has recently been used in pharmaceutical and healthy food industries. Steaming and drying are two important steps in the processing of Cistanche deserticola. Unfortunately, a comprehensive understanding of the chemical composition changes of Cistanche deserticola during thermal processing is limited. In this study, ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS)-based widely targeted metabolomics analysis was used to investigate the transformation mechanism of Cistanche deserticola active compounds during steaming and drying processes. A total of 776 metabolites were identified in Cistanche deserticola during thermal processing, among which, 77 metabolites were differentially regulated (p < 0.05) wherein 39 were upregulated (UR) and 38 were downregulated (DR). Forty-seven (17 UR, 30 DR) and 30 (22 UR, 8 DR) differential metabolites were identified during steaming and drying, respectively. The most variation of the chemicals was observed during the process of steaming. Metabolic pathway analysis indicated that phenylpropanoid, flavonoid biosynthesis, and alanine metabolism were observed during steaming, while glycine, serine, and threonine metabolism, thiamine metabolism, and unsaturated fatty acid biosynthesis were observed during drying. The possible mechanisms of the chemical alterations during thermal processing were also provided by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Furthermore, the blackening of the appearance of Cistanche deserticola mainly occurred in the steaming stage rather than the drying stage, which is associated with the metabolism of the amino acids. All results indicated that the formation of active compounds during the processing of Cistanche deserticola mainly occurred in the steaming stage.

scholarly journals Widely Targeted Metabolomics Analysis Reveals the Effect of Flooding Stress on the Synthesis of Flavonoids in Chrysanthemum morifolium

Molecules ◽  
2019 ◽  
Vol 24 (20) ◽  
pp. 3695 ◽  
Author(s):  
Tao Wang ◽  
Qingjun Zou ◽  
Qiaosheng Guo ◽  
Feng Yang ◽  
Liwei Wu ◽  
...  
Keyword(s):  
Chrysanthemum Morifolium ◽  
Growth Stages ◽  
Targeted Metabolomics ◽  
Flower Bud ◽  
Metabolomics Data ◽  
Flooding Stress ◽  
Flavonoid Synthesis ◽  
Key Enzyme ◽  
Widely Targeted Metabolomics ◽  
Different Growth Stages

Chrysanthemum morifolium. cv “Hangju” is an important medicinal material with many functions in China. Flavonoids as the main secondary metabolites are a major class of medicinal components in “Hangju” and its composition and content can change significantly after flooding. This study mimicked the flooding stress of “Hangju” during flower bud differentiation and detected its metabolites in different growth stages. From widely targeted metabolomics data, 661 metabolites were detected, of which 46 differential metabolites exist simultaneously in the different growth stages of “Hangju”. The top three types of the 46 differential metabolites were flavone C-glycosides, flavonol and flavone. Our results demonstrated that the accumulation of flavonoids in different growth stages of “Hangju” was different; however, quercetin, eriodictyol and most of the flavone C-glycosides were significantly enhanced in the two stages after flooding stress. The expression of key enzyme genes in the flavonoid synthesis pathway were determined using RT-qPCR, which verified the consistency of the expression levels of CHI, F3H, DFR and ANS with the content of the corresponding flavonoids. A regulatory network of flavonoid biosynthesis was established to illustrate that flooding stress can change the accumulation of flavonoids by affecting the expression of the corresponding key enzymes in the flavonoid synthesis pathway.

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