Статті в журналах: "Metabolomics and trascriptomics analysis"

1

Worley, Bradley, and Robert Powers. "Multivariate Analysis in Metabolomics." Current Metabolomics 1, no. 1 (November 1, 2012): 92–107. http://dx.doi.org/10.2174/2213235x11301010092.

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2

Worley, Bradley, and Robert Powers. "Multivariate Analysis in Metabolomics." Current Metabolomics 1, no. 1 (November 1, 2012): 92–107. http://dx.doi.org/10.2174/2213235x130108.

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3

Chen, Yang, En-Min Li, and Li-Yan Xu. "Guide to Metabolomics Analysis: A Bioinformatics Workflow." Metabolites 12, no. 4 (April 15, 2022): 357. http://dx.doi.org/10.3390/metabo12040357.

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Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach’s ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

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4

IKEDA, Kazutaka, and Takeshi BAMBA. "Hydrophobic Metabolite Analysis in Metabolomics." Journal of the Mass Spectrometry Society of Japan 65, no. 5 (2017): 199–202. http://dx.doi.org/10.5702/massspec.s17-48.

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5

Jansen, J. J., H. C. J. Hoefsloot, H. F. M. Boelens, J. van der Greef, and A. K. Smilde. "Analysis of longitudinal metabolomics data." Bioinformatics 20, no. 15 (April 15, 2004): 2438–46. http://dx.doi.org/10.1093/bioinformatics/bth268.

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6

Jansen, Jeroen J., and Johan A. Westerhuis. "Editorial–data analysis in metabolomics." Metabolomics 8, S1 (March 24, 2012): 1–2. http://dx.doi.org/10.1007/s11306-012-0418-4.

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7

Saglik, Ayhan, Ismail Koyuncu, Ataman Gonel, Hamza Yalcin, Fatih Mehmet Adibelli, and Muslum Toptan. "Metabolomics analysis in pterygium tissue." International Ophthalmology 39, no. 10 (January 8, 2019): 2325–33. http://dx.doi.org/10.1007/s10792-018-01069-2.

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8

Barnes, S., H. P. Benton, K. Casazza, S. J. Cooper, X. Cui, X. Du, J. Engler, et al. "Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future." Journal of Mass Spectrometry 51, no. 8 (August 2016): ii—iii. http://dx.doi.org/10.1002/jms.3676.

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Barnes, Stephen, H. Paul Benton, Krista Casazza, Sara J. Cooper, Xiangqin Cui, Xiuxia Du, Jeffrey Engler, et al. "Training in metabolomics research. II. Processing and statistical analysis of metabolomics data, metabolite identification, pathway analysis, applications of metabolomics and its future." Journal of Mass Spectrometry 51, no. 8 (July 15, 2016): 535–48. http://dx.doi.org/10.1002/jms.3780.

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10

Moseley, Hunter N. B. "ERROR ANALYSIS AND PROPAGATION IN METABOLOMICS DATA ANALYSIS." Computational and Structural Biotechnology Journal 4, no. 5 (January 2013): e201301006. http://dx.doi.org/10.5936/csbj.201301006.

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11

Kim, Dong-Shin, Sun Lee, Suk Man Park, Su Hyun Yun, Han-Seung Gab, Sang Suk Kim, and Hyun-Jin Kim. "Comparative Metabolomics Analysis of Citrus Varieties." Foods 10, no. 11 (November 16, 2021): 2826. http://dx.doi.org/10.3390/foods10112826.

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Many citrus varieties are hybridized to improve their quality and to overcome the effects of climate change. However, there is limited information on the effect of the chemical profiles of hybrid varieties on their quality. In this study, we analyzed 10 citrus varieties and evaluated the correlation with their general characteristics and antioxidant activities. Chemical profiles, including the contents of sugars, organic acid compounds, flavonoids, limonoids, and carotenoids, which are related to taste, color, and health benefits, were significantly different depending on the citrus varieties, leading to different antioxidant capacities and general quality parameters. Based on these data, the correlations were investigated, and 10 citrus varieties were clustered into four groups—Changshou kumquat and Jeramon (cluster I); Setoka (cluster II-1); Natsumi, Satsuma mandarin, and Navel orange (cluster II-2); Kanpei, Tamnaneunbong, Saybyeolbong, and Shiranui (cluster II-3). Moreover, a metabolomic pathway was proposed. Although citrus peels were not analyzed and the sensory and functional qualities of the citrus varieties were not investigated in this study, our results are useful to better understand the relationship between citrus quality and metabolite profiles, which can provide basic information for the development and improvement of new citrus varieties.

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12

Bünger, Rolf, and Robert T. Mallet. "Metabolomics and Receiver Operating Characteristic Analysis." Critical Care Medicine 44, no. 9 (September 2016): 1784–85. http://dx.doi.org/10.1097/ccm.0000000000001795.

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13

Zhang, Aihua, Hui Sun, Ping Wang, Ying Han, and Xijun Wang. "Modern analytical techniques in metabolomics analysis." Analyst 137, no. 2 (2012): 293–300. http://dx.doi.org/10.1039/c1an15605e.

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14

Clendinen, Chaevien S., Christian Pasquel, Ramadan Ajredini, and Arthur S. Edison. "13C NMR Metabolomics: INADEQUATE Network Analysis." Analytical Chemistry 87, no. 11 (May 14, 2015): 5698–706. http://dx.doi.org/10.1021/acs.analchem.5b00867.

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15

Navarro, David Alejandro Peña, Matthias Gerstl, Christian Jungreuthmayer, and Jürgen Zanghellini. "Pathway thermodynamics: Metabolomics integrated pathway analysis." New Biotechnology 33 (July 2016): S186. http://dx.doi.org/10.1016/j.nbt.2016.06.1366.

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16

Aru, Violetta, Maria Barbara Pisano, Francesco Savorani, Søren Balling Engelsen, Sofia Cosentino, and Flaminia Cesare Marincola. "Metabolomics analysis of shucked mussels’ freshness." Food Chemistry 205 (August 2016): 58–65. http://dx.doi.org/10.1016/j.foodchem.2016.02.152.

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17

Wieder, Cecilia, Clément Frainay, Nathalie Poupin, Pablo Rodríguez-Mier, Florence Vinson, Juliette Cooke, Rachel PJ Lai, Jacob G. Bundy, Fabien Jourdan, and Timothy Ebbels. "Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis." PLOS Computational Biology 17, no. 9 (September 7, 2021): e1009105. http://dx.doi.org/10.1371/journal.pcbi.1009105.

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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.

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18

Du, Xinsong, Juan J. Aristizabal-Henao, Timothy J. Garrett, Mathias Brochhausen, William R. Hogan, and Dominick J. Lemas. "A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research." Metabolites 12, no. 1 (January 17, 2022): 87. http://dx.doi.org/10.3390/metabo12010087.

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Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

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19

Zhang, Tao, Can Chen, Kaizhou Xie, Jinyu Wang, and Zhiming Pan. "Current State of Metabolomics Research in Meat Quality Analysis and Authentication." Foods 10, no. 10 (October 9, 2021): 2388. http://dx.doi.org/10.3390/foods10102388.

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In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

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20

Carey, Maureen A., Vincent Covelli, Audrey Brown, Gregory L. Medlock, Mareike Haaren, Jessica G. Cooper, Jason A. Papin, and Jennifer L. Guler. "Influential Parameters for the Analysis of Intracellular Parasite Metabolomics." mSphere 3, no. 2 (April 18, 2018): e00097-18. http://dx.doi.org/10.1128/msphere.00097-18.

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ABSTRACT Metabolomics is increasingly popular for the study of pathogens. For the malaria parasite Plasmodium falciparum, both targeted and untargeted metabolomics have improved our understanding of pathogenesis, host-parasite interactions, and antimalarial drug treatment and resistance. However, purification and analysis procedures for performing metabolomics on intracellular pathogens have not been explored. Here, we purified in vitro-grown ring-stage intraerythrocytic P. falciparum parasites for untargeted metabolomics studies; the small size of this developmental stage amplifies the challenges associated with metabolomics studies as the ratio between host and parasite biomass is maximized. Following metabolite identification and data preprocessing, we explored multiple confounding factors that influence data interpretation, including host contamination and normalization approaches (including double-stranded DNA, total protein, and parasite numbers). We conclude that normalization parameters have large effects on differential abundance analysis and recommend the thoughtful selection of these parameters. However, normalization does not remove the contribution from the parasite’s extracellular environment (culture media and host erythrocyte). In fact, we found that extraparasite material is as influential on the metabolome as treatment with a potent antimalarial drug with known metabolic effects (artemisinin). Because of this influence, we could not detect significant changes associated with drug treatment. Instead, we identified metabolites predictive of host and medium contamination that could be used to assess sample purification. Our analysis provides the first quantitative exploration of the effects of these factors on metabolomics data analysis; these findings provide a basis for development of improved experimental and analytical methods for future metabolomics studies of intracellular organisms. IMPORTANCE Molecular characterization of pathogens such as the malaria parasite can lead to improved biological understanding and novel treatment strategies. However, the distinctive biology of the Plasmodium parasite, including its repetitive genome and the requirement for growth within a host cell, hinders progress toward these goals. Untargeted metabolomics is a promising approach to learn about pathogen biology. By measuring many small molecules in the parasite at once, we gain a better understanding of important pathways that contribute to the parasite’s response to perturbations such as drug treatment. Although increasingly popular, approaches for intracellular parasite metabolomics and subsequent analysis are not well explored. The findings presented in this report emphasize the critical need for improvements in these areas to limit misinterpretation due to host metabolites and to standardize biological interpretation. Such improvements will aid both basic biological investigations and clinical efforts to understand important pathogens.

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21

Beirnaert, Charlie, Laura Peeters, Pieter Meysman, Wout Bittremieux, Kenn Foubert, Deborah Custers, Anastasia Van der Auwera, et al. "Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis." Metabolites 9, no. 3 (March 20, 2019): 54. http://dx.doi.org/10.3390/metabo9030054.

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Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.

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22

Hernández-Mesa, M., B. Le Bizec, and G. Dervilly. "Metabolomics in chemical risk analysis – A review." Analytica Chimica Acta 1154 (April 2021): 338298. http://dx.doi.org/10.1016/j.aca.2021.338298.

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23

OOGA, Takushi. "Metabolomics; a comprehensive analysis of metabolic compounds." Japanese Journal of Thrombosis and Hemostasis 25, no. 3 (2014): 357–62. http://dx.doi.org/10.2491/jjsth.25.357.

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24

Liang, Lingfan, Fei Sun, Hongbo Wang, and Zeping Hu. "Metabolomics, metabolic flux analysis and cancer pharmacology." Pharmacology & Therapeutics 224 (August 2021): 107827. http://dx.doi.org/10.1016/j.pharmthera.2021.107827.

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25

Alberich, Ricardo, José A. Castro, Mercè Llabrés, and Pere Palmer-Rodríguez. "Metabolomics analysis: Finding out metabolic building blocks." PLOS ONE 12, no. 5 (May 11, 2017): e0177031. http://dx.doi.org/10.1371/journal.pone.0177031.

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26

Camacho, D., P. Mendes, and A. de la Fuente. "Modelling and simulation for metabolomics data analysis." Biochemical Society Transactions 33, no. 6 (December 1, 2005): 1427. http://dx.doi.org/10.1042/bst20051427.

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27

Cuperlovic-Culf, Miroslava, Dean Ferguson, Adrian Culf, Pier Morin, and Mohamed Touaibia. "1H NMR Metabolomics Analysis of Glioblastoma Subtypes." Journal of Biological Chemistry 287, no. 24 (April 23, 2012): 20164–75. http://dx.doi.org/10.1074/jbc.m111.337196.

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28

Cuperlovic-Culf, Miroslava, Nabil Belacel, and Adrian Culf. "Integrated analysis of transcriptomics and metabolomics profiles." Expert Opinion on Medical Diagnostics 2, no. 5 (April 29, 2008): 497–509. http://dx.doi.org/10.1517/17530059.2.5.497.

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29

Przybylowski, P., G. Wasilewski, E. Koc-Zorawska, D. Dudzik, J. Malyszko, and C. Barbas. "Metabolomics Analysis in Assessing Immunosuppressive Drug Toxicity." Transplantation 98 (July 2014): 431. http://dx.doi.org/10.1097/00007890-201407151-01431.

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30

Mendes, P., D. Camacho, and A. de la Fuente. "Modelling and simulation for metabolomics data analysis." Biochemical Society Transactions 33, no. 6 (October 26, 2005): 1427–29. http://dx.doi.org/10.1042/bst0331427.

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The advent of large data sets, such as those produced in metabolomics, presents a considerable challenge in terms of their interpretation. Several mathematical and statistical methods have been proposed to analyse these data, and new ones continue to appear. However, these methods often disagree in their analyses, and their results are hard to interpret. A major contributing factor for the difficulties in interpreting these data lies in the data analysis methods themselves, which have not been thoroughly studied under controlled conditions. We have been producing synthetic data sets by simulation of realistic biochemical network models with the purpose of comparing data analysis methods. Because we have full knowledge of the underlying ‘biochemistry’ of these models, we are better able to judge how well the analyses reflect true knowledge about the system. Another advantage is that the level of noise in these data is under our control and this allows for studying how the inferences are degraded by noise. Using such a framework, we have studied the extent to which correlation analysis of metabolomics data sets is capable of recovering features of the biochemical system. We were able to identify four major metabolic regulatory configurations that result in strong metabolite correlations. This example demonstrates the utility of biochemical simulation in the analysis of metabolomics data.

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31

Jeon, Jin Pyeong, and Jeong Eun Kim. "NMR-Based Metabolomics Analysis of Leptomeningeal Carcinomatosis." Neurosurgery 75, no. 4 (October 2014): N12—N13. http://dx.doi.org/10.1227/neu.0000000000000517.

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32

Tillack, Jana, Nicole Paczia, Katharina Nöh, Wolfgang Wiechert, and Stephan Noack. "Error Propagation Analysis for Quantitative Intracellular Metabolomics." Metabolites 2, no. 4 (November 21, 2012): 1012–30. http://dx.doi.org/10.3390/metabo2041012.

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33

Yang, Xianyi, Lin Chai, Chunyan Liu, Mei Liu, Limei Han, Changsheng Li, Hui Guo, et al. "Serum Metabolomics Analysis in Wasp Sting Patients." BioMed Research International 2018 (December 25, 2018): 1–8. http://dx.doi.org/10.1155/2018/5631372.

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To analyze the dynamic changes of serum metabolomics in wasp sting victims, we collected serum from 10 healthy volunteers and 10 patients who had been stung 3 hours, 24 hours, and 72 hours before sample collection. We analyzed the metabolomics by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) techniques and then performed enrichment analysis. A total of 838 metabolites were identified. Serum metabolomics analysis using MetaboAnalyst revealed 289 metabolites that were significantly different among patients in the 3-hour group versus healthy controls (P<0.001). Pathway analysis of those metabolites indicated that those metabolic sets were associated with sphingolipid metabolism. Based on the differences among the control, 3-hour, 24-hour, and 72-hour groups, we classified serum metabolites into different categories. The first and second categories included 297 and 280 metabolites that were significantly different in terms of concentration among healthy controls versus the participants whose sera were analyzed 3 hours, 24 hours, and 72 hours after wasp stings. Pathway analysis of those metabolites indicated that those metabolic sets were associated with thiamine metabolism. The third category included 269 significant metabolites. The fourth category included 28 significant metabolites. Pathway analysis of the metabolites in third and fourth categories indicated that those metabolic sets were associated with phenylalanine, tyrosine, and tryptophan biosynthesis. The fifth category included 31 metabolites, which were not significantly different between the control and 3-hour groups but were higher in concentration in the 24-hour and 72-hour groups. Pathway analysis of the fifth category of significant metabolites identified linoleic acid metabolism. In conclusion, multiple metabolic pathways are associated with wasp stings, and these might provide a basis for exploring mechanisms of wasp sting injury and potential targets for therapy.

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34

Delgado-Povedano, M. M., L. S. Castillo-Peinado, M. Calderón-Santiago, M. D. Luque de Castro, and F. Priego-Capote. "Dry sweat as sample for metabolomics analysis." Talanta 208 (February 2020): 120428. http://dx.doi.org/10.1016/j.talanta.2019.120428.

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Radenkovic, Silvia, Ivan Vuckovic, and Ian R. Lanza. "Metabolic Flux Analysis: Moving beyond Static Metabolomics." Trends in Biochemical Sciences 45, no. 6 (June 2020): 545–46. http://dx.doi.org/10.1016/j.tibs.2020.02.011.

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36

Hu, Chunxiu, and Guowang Xu. "Mass-spectrometry-based metabolomics analysis for foodomics." TrAC Trends in Analytical Chemistry 52 (December 2013): 36–46. http://dx.doi.org/10.1016/j.trac.2013.09.005.

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Liang, Yu-Jen, Yu-Ting Lin, Chia-Wei Chen, Chien-Wei Lin, Kun-Mao Chao, Wen-Harn Pan, and Hsin-Chou Yang. "SMART: Statistical Metabolomics Analysis—An R Tool." Analytical Chemistry 88, no. 12 (June 2016): 6334–41. http://dx.doi.org/10.1021/acs.analchem.6b00603.

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Cui, Xiujuan, Xiaoyan Yu, Guang Sun, Ting Hu, Sergei Likhodii, Jingmin Zhang, Edward Randell, Xiang Gao, Zhaozhi Fan, and Weidong Zhang. "Differential metabolomics networks analysis of menopausal status." PLOS ONE 14, no. 9 (September 18, 2019): e0222353. http://dx.doi.org/10.1371/journal.pone.0222353.

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Heresi, G. A., I. Haddadin, J. R. Bartholomew, M. Gomes, R. Renapurkar, N. G. Smedira, and R. A. Dweik. "Metabolomics Analysis in Chronic Thromboembolic Pulmonary Hypertension." Journal of Heart and Lung Transplantation 37, no. 4 (April 2018): S25. http://dx.doi.org/10.1016/j.healun.2018.01.040.

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40

Zhang, Weidong, Sergei Likhodii, Yuhua Zhang, Erfan Aref-Eshghi, Patricia E. Harper, Edward Randell, Roger Green, et al. "Classification of osteoarthritis phenotypes by metabolomics analysis." BMJ Open 4, no. 11 (November 2014): e006286. http://dx.doi.org/10.1136/bmjopen-2014-006286.

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41

Verouden, Maikel P. H., Johan A. Westerhuis, Mariët J. van der Werf, and Age K. Smilde. "Exploring the analysis of structured metabolomics data." Chemometrics and Intelligent Laboratory Systems 98, no. 1 (August 2009): 88–96. http://dx.doi.org/10.1016/j.chemolab.2009.05.004.

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42

Ren, Sheng, Anna A. Hinzman, Emily L. Kang, Rhonda D. Szczesniak, and Long Jason Lu. "Computational and statistical analysis of metabolomics data." Metabolomics 11, no. 6 (July 28, 2015): 1492–513. http://dx.doi.org/10.1007/s11306-015-0823-6.

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43

Pelczer, István. "NMR-based mixture analysis-metabolomics and beyond…" Magnetic Resonance in Chemistry 47, S1 (November 6, 2009): S1. http://dx.doi.org/10.1002/mrc.2547.

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44

Kastenmüller, Gabi, Werner Römisch-Margl, Brigitte Wägele, Elisabeth Altmaier, and Karsten Suhre. "metaP-Server: A Web-Based Metabolomics Data Analysis Tool." Journal of Biomedicine and Biotechnology 2011 (2011): 1–7. http://dx.doi.org/10.1155/2011/839862.

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Metabolomics is an emerging field that is based on the quantitative measurement of as many small organic molecules occurring in a biological sample as possible. Due to recent technical advances, metabolomics can now be used widely as an analytical high-throughput technology in drug testing and epidemiological metabolome and genome wide association studies. Analogous to chip-based gene expression analyses, the enormous amount of data produced by modern kit-based metabolomics experiments poses new challenges regarding their biological interpretation in the context of various sample phenotypes. We developedmetaP-serverto facilitate data interpretation.metaP-serverprovides automated and standardized data analysis for quantitative metabolomics data, covering the following steps from data acquisition to biological interpretation: (i) data quality checks, (ii) estimation of reproducibility and batch effects, (iii) hypothesis tests for multiple categorical phenotypes, (iv) correlation tests for metric phenotypes, (v) optionally including all possible pairs of metabolite concentration ratios, (vi) principal component analysis (PCA), and (vii) mapping of metabolites onto colored KEGG pathway maps. Graphical output is clickable and cross-linked to sample and metabolite identifiers. Interactive coloring of PCA and bar plots by phenotype facilitates on-line data exploration. For users of commercial metabolomics kits, cross-references to the HMDB, LipidMaps, KEGG, PubChem, and CAS databases are provided.metaP-serveris freely accessible athttp://metabolomics.helmholtz-muenchen.de/metap2/.

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Kumar, Nishith, Md Aminul Hoque, Md Shahjaman, S. M. Shahinul Islam, and Md Nurul Haque Mollah. "A New Approach of Outlier-robust Missing Value Imputation for Metabolomics Data Analysis." Current Bioinformatics 14, no. 1 (December 6, 2018): 43–52. http://dx.doi.org/10.2174/1574893612666171121154655.

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Background: Metabolomics data generation and quantification are different from other types of molecular “omics” data in bioinformatics. Mass spectrometry (MS) based (gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), etc.) metabolomics data frequently contain missing values that make some quantitative analysis complex. Typically metabolomics datasets contain 10% to 20% missing values that originate from several reasons, like analytical, computational as well as biological hazard. Imputation of missing values is a very important and interesting issue for further metabolomics data analysis. </P><P> Objective: This paper introduces a new algorithm for missing value imputation in the presence of outliers for metabolomics data analysis. </P><P> Method: Currently, the most well known missing value imputation techniques in metabolomics data are knearest neighbours (kNN), random forest (RF) and zero imputation. However, these techniques are sensitive to outliers. In this paper, we have proposed an outlier robust missing imputation technique by minimizing twoway empirical mean absolute error (MAE) loss function for imputing missing values in metabolomics data. Results: We have investigated the performance of the proposed missing value imputation technique in a comparison of the other traditional imputation techniques using both simulated and real data analysis in the absence and presence of outliers. Conclusion: Results of both simulated and real data analyses show that the proposed outlier robust missing imputation technique is better performer than the traditional missing imputation methods in both absence and presence of outliers.

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Considine, Elizabeth, and Reza Salek. "A Tool to Encourage Minimum Reporting Guideline Uptake for Data Analysis in Metabolomics." Metabolites 9, no. 3 (March 5, 2019): 43. http://dx.doi.org/10.3390/metabo9030043.

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Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

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Roth, Heidi E., and Robert Powers. "Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics." Cancers 14, no. 16 (August 18, 2022): 3992. http://dx.doi.org/10.3390/cancers14163992.

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Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these multiple studies allowed for the validation of potential clinical biomarkers of PDAC while also highlighting variations in study protocols that may explain poor reproducibility. Our meta-analysis identified 10 metabolites that may serve as PDAC biomarkers and warrant further investigation. However, 87% of the 655 metabolites identified as potential biomarkers were identified in single studies. Differences in cohort size and demographics, p-value choice, fold-change significance, sample type, handling and storage, data collection, and analysis were all factors that likely contributed to this apparently large false positive rate. Our meta-analysis demonstrated the need for consistent experimental design and normalized practices to accurately leverage clinical metabolomics data for reliable and reproducible biomarker discovery.

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Considine, Elizabeth C. "The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective." Metabolites 9, no. 7 (July 2, 2019): 126. http://dx.doi.org/10.3390/metabo9070126.

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Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

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Belacel, Nabil, and Miroslava Cuperlovic-Culf. "PROAFTN Classifier for Feature Selection with Application to Alzheimer Metabolomics Data Analysis." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 11 (October 2019): 1940013. http://dx.doi.org/10.1142/s0218001419400135.

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Early and accurate Alzheimer’s disease (AD) diagnosis remains a challenge. Recently, increasing efforts have been focused towards utilization of metabolomics data for the discovery of biomarkers for screening and diagnosis of AD. Several machine learning approaches were explored for classifying the blood metabolomics profiles of cognitively healthy and AD patients. Differentiation between AD, mild cognitive impairment (MCI) and cognitively healthy subjects remains difficult. In this paper, we propose a new machine learning approach for the selection of a subset of features that provide an improvement in classification rates between these three levels of cognitive disorders. Our experimental results demonstrate that utilization of these selected metabolic markers improves the performance of several classifiers in comparison to the classification accuracy obtained for the complete metabolomics dataset. The obtained results indicate that our algorithms are effective in discovering a panel of biomarkers of AD and MCI from metabolomics data suggesting the possibility to develop a noninvasive blood diagnostic technique of AD and MCI.

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Trifonova, Oxana P., Dmitry L. Maslov, Elena E. Balashova, and Petr G. Lokhov. "Current State and Future Perspectives on Personalized Metabolomics." Metabolites 13, no. 1 (January 1, 2023): 67. http://dx.doi.org/10.3390/metabo13010067.

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Metabolomics is one of the most promising ‘omics’ sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

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