Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99


Any time

2018 ( 8 )

2017 ( 10 )

2016 ( 5 )

2015 ( 8 )

Custom range...

Search Results: 1 - 10 of 184 matches for " metabolomics "
All listed articles are free for downloading (OA Articles)
Page 1 /184
Display every page Item
A Comparison of Various Normalization Methods for LC/MS Metabolomics Data  [PDF]
Jacob E. Wulff, Matthew W. Mitchell
Advances in Bioscience and Biotechnology (ABB) , 2018, DOI: 10.4236/abb.2018.98022
Abstract: In metabolomics data, like other -omics data, normalization is an important part of the data processing. The goal of normalization is to reduce the variation from non-biological sources (such as instrument batch effects), while maintaining the biological variation. Many normalization techniques make adjustments to each sample. One common method is to adjust each sample by its Total Ion Current (TIC), i.e. for each feature in the sample, divide its intensity value by the total for the sample. Because many of the assumptions of these methods are dubious in metabolomics data sets, we compare these methods to two methods that make adjustments separately for each metabolite, rather than for each sample. These two methods are the following: 1) for each metabolite, divide its value by the median level in bridge samples (BRDG); 2) for each metabolite divide its value by the median across the experimental samples (MED). These methods were assessed by comparing the correlation of the normalized values to the values from targeted assays for a subset of metabolites in a large human plasma data set. The BRDG and MED normalization techniques greatly outperformed the other methods, which often performed worse than performing no normalization at all.
A Guideline to Univariate Statistical Analysis for LC/MS-Based Untargeted Metabolomics-Derived Data
Maria Vinaixa,Sara Samino,Isabel Saez,Jordi Duran,Joan J. Guinovart,Oscar Yanes
Metabolites , 2012, DOI: 10.3390/metabo2040775
Abstract: Several metabolomic software programs provide methods for peak picking, retention time alignment and quantification of metabolite features in LC/MS-based metabolomics. Statistical analysis, however, is needed in order to discover those features significantly altered between samples. By comparing the retention time and MS/MS data of a model compound to that from the altered feature of interest in the research sample, metabolites can be then unequivocally identified. This paper reports on a comprehensive overview of a workflow for statistical analysis to rank relevant metabolite features that will be selected for further MS/MS experiments. We focus on univariate data analysis applied in parallel on all detected features. Characteristics and challenges of this analysis are discussed and illustrated using four different real LC/MS untargeted metabolomic datasets. We demonstrate the influence of considering or violating mathematical assumptions on which univariate statistical test rely, using high-dimensional LC/MS datasets. Issues in data analysis such as determination of sample size, analytical variation, assumption of normality and homocedasticity, or correction for multiple testing are discussed and illustrated in the context of our four untargeted LC/MS working examples.
Metabolomics in the identification of biomarkers of dietary intake
Aoife O’Gorman,Helena Gibbons,Lorraine Brennan
Computational and Structural Biotechnology Journal , 2013,
Abstract: Traditional methods for assessing dietary exposure can be unreliable, with under reporting one of the main problems. In an attempt to overcome such problems there is increasing interest in identifying biomarkers of dietary intake to provide a more accurate measurement. Metabolomics is an analytical technique that aims to identify and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics coupled with statistical analysis for the identification of dietary biomarkers, with a number of putative biomarkers identified. This minireview focuses on metabolomics based approaches and highlights some of the key successes.
Metabolomics and Fetal-Neonatal Nutrition: Between “Not Enough” and “Too Much”
Angelica Dessì,Melania Puddu,Giovanni Ottonello,Vassilios Fanos
Molecules , 2013, DOI: 10.3390/molecules181011724
Abstract: Metabolomics is a new analytical technique defined as the study of the complex system of metabolites that is capable of describing the biochemical phenotype of a biological system. In recent years the literature has shown an increasing interest in paediatric obesity and the onset of diabetes and the metabolic syndrome in adulthood. Some studies show that fetal malnutrition, both excessive and insufficient, may permanently alter the metabolic processes of the fetus and increase the risk of future chronic pathologies. At present then, attention is being focused mainly on the formulation of new hypotheses, by means of metabolomics, concerning the biological mechanisms to departure from fetal-neonatal life that may predispose to the development of these diseases.
Metabolomics Analysis of the Responses to Partial Hepatectomy in Hepatocellular Carcinoma Patients  [PDF]
Wan Chan, Shuhai Lin, Stella Sun, Hongde Liu, John M. Luk, Zongwei Cai
American Journal of Analytical Chemistry (AJAC) , 2011, DOI: 10.4236/ajac.2011.22016
Abstract: In this study, liquid chromatography/quadrupole time of flight mass spectrometry (LC/QTOFMS) was employed for investigating the metabolome of the sera collected from hepatocellular carcinoma (HCC) patients before and 3 to 5 months after partial hepatectomy. To investigate the changes in metabolic phenotypes after the hepatic resection, principal components analysis (PCA) and support vector machine (SVM) were performed for the data grouping and classification. Based on the obtained SVM model, mass spectrometry spectra, database searching as well as the confirmation from authentic standards, several differentiating metabolites were tentatively identified. To improve visualization, z-score plot and heat map display were performed, which exhibited the changes in concentration of the metabolites. As a result, depletion of circulating carnitine, reduced amino acid biosynthesis and increased rate of lipid peroxidation were observed. Meanwhile, up-regulation of hypoxanthine indicated that purine metabolism might serve as the salvage pathway. Collectively, the results reflected metabolic responses to surgical operation in HCC patients, suggesting perturbation of energy metabolism may occur in 3 to 5 months after the partial hepatectomy.
Bias of the Random Forest Out-of-Bag (OOB) Error for Certain Input Parameters  [PDF]
Matthew W. Mitchell
Open Journal of Statistics (OJS) , 2011, DOI: 10.4236/ojs.2011.13024
Abstract: Random Forest is an excellent classification tool, especially in the –omics sciences such as metabolomics, where the number of variables is much greater than the number of subjects, i.e., “n << p.” However, the choices for the arguments for the random forest implementation are very important. Simulation studies are performed to compare the effect of the input parameters on the predictive ability of the random forest. The number of variables sampled, m-try, has the largest impact on the true prediction error. It is often claimed that the out-of-bag error (OOB) is an unbiased estimate of the true prediction error. However, for the case where n << p, with the default arguments, the out-of-bag (OOB) error overestimates the true error, i.e., the random forest actually performs better than indicated by the OOB error. This bias is greatly reduced by subsampling without replacement and choosing the same number of observations from each group. However, even after these adjustments, there is a low amount of bias. The remaining bias occurs because when there are trees with equal predictive ability, the one that performs better on the in-bag samples will perform worse on the out-of-bag samples. Cross-validation can be performed to reduce the remaining bias.
Eigenvalues of Jacobian Matrices Report on Steps of Metabolic Reprogramming in a Complex Plant-Environment Interaction  [PDF]
Thomas N?gele, Wolfram Weckwerth
Applied Mathematics (AM) , 2013, DOI: 10.4236/am.2013.48A007

Mathematical modeling of biochemical systems aims at improving the knowledge about complex regulatory networks. The experimental high-throughput measurement of levels of biochemical components, like metabolites and proteins, has become an integral part for characterization of biological systems. Yet, strategies of mathematical modeling to functionally integrate resulting data sets is still challenging. In plant biology, regulatory strategies that determine the metabolic output of metabolism as a response to changes in environmental conditions are hardly traceable by intuition. Mathematical modeling has been shown to be a promising approach to address such problems of plant-environment interaction promoting the comprehensive understanding of plant biochemistry and physiology. In this context, we recently published an inversely calculated solution for first-order partial derivatives, i.e. the Jacobian matrix, from experimental high-throughput data of a plant biochemical model system. Here, we present a biomathematical strategy, comprising 1) the inverse calculation of a biochemical Jacobian; 2) the characterization of the associated eigenvalues and 3) the interpretation of the results with respect to biochemical regulation. Deriving the real parts of eigenvalues provides information about the stability of solutions of inverse calculations. We found that shifts of the eigenvalue real part distributions occur together with metabolic shifts induced by short-term and long-term exposure to low temperature. This indicates the suitability of mathematical Jacobian characterization for recognizing perturbations in the metabolic homeostasis of plant metabolism. Together with our previously published results on inverse Jacobian calculation this represents a comprehensive strategy of mathematical modeling for the analysis of complex biochemical systems and plant-environment interactions from the molecular to the ecosystems level.

Metabolomics Profile of Potato Tubers after Phosphite Treatment  [PDF]
Xingxi Gao, Steven Locke, Junzeng Zhang, Jyoti Joshi, Gefu Wang-Pruski
American Journal of Plant Sciences (AJPS) , 2018, DOI: 10.4236/ajps.2018.94065
Abstract: Phosphite (Phi)-based fungicides are used to control the oomycete Phytophthora infestans which causes late blight disease, the most devastating disease in potatoes. In order to examine the effects of Phi-based fungicides on potato tubers through foliar or post-harvest application, a metabolite profiling approach based on gas chromatography coupled to mass spectrometry (GC-MS) has been established. A total of 132 metabolites were detected using the GC-MS approach. Among these, 34 metabolites were identified after normalization and annotated with a compound name with standard mass spectral library. Metabolomic analysis of Phi-treated plants showed significant differences in the levels of many metabolites especially amino acids. Multivariate statistical approaches, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), were employed to explore the relationships between metabolites to detect group differences. A good discrimination between the control and the Phi-treated plants was observed, which demonstrated that significant changes in the metabolite profile have been caused by the two different Phi applications (foliar or post-harvest). This finding suggests that the alteration of specific metabolite levels by accumulation of Phi can lead to resistance against the pathogen.
Deep Exploration of Bifidobacteria through Metabolomics Study  [PDF]
Juan Li, Yatao Jiang, Yihao Shen, Qingzhi Li, Zhongke Sun
Journal of Biosciences and Medicines (JBM) , 2018, DOI: 10.4236/jbm.2018.65008
Abstract: Bifidobacteria are probiotic bacteria with multiple health-promoting properties for human being. The global market for probiotics, especially for bifidobacteria is booming. However, the entire market is still at an early stage as there is nearly no fine products developed yet except the whole bacterial cells. The maturation of metabolomics technologies make it possible to study complex mixture with high-throughput, comprehensive maps and libraries. Therefore, we prospect that metabolomics studies mainly based on liquid/gas chromatography-mass spectrometry (LC/GC-MS) can deepen our understanding in detail during the study of metabolic mechanisms of bifidobacteria. These studies can be conducted at three phases, including non-targeted, targeted metabolomic analysis of bifidobacteria, and specific metabolites production through metabolic engineering and fermentation. Metabolomic studies of bifidobacteria will allow us to fully explore their metabolic mechanisms and to utilize metabolites that contribute to human health. In particular, bifidobacteria derived conjugated linoleic acids and bacteriocins are two kinds of fined products that may have great potentials in the future and can be used as food additives.
Symbiodinium—Invertebrate Symbioses and the Role of?Metabolomics
Benjamin R. Gordon,William Leggat
Marine Drugs , 2010, DOI: 10.3390/md8102546
Abstract: Symbioses play an important role within the marine environment. Among the most well known of these symbioses is that between coral and the photosynthetic dinoflagellate, Symbiodinium spp. Understanding the metabolic relationships between the host and the symbiont is of the utmost importance in order to gain insight into how this symbiosis may be disrupted due to environmental stressors. Here we summarize the metabolites related to nutritional roles, diel cycles and the common metabolites associated with the invertebrate- Symbiodinium relationship. We also review the more obscure metabolites and toxins that have been identified through natural products and biomarker research. Finally, we discuss the key role that metabolomics and functional genomics will play in understanding these important symbioses.
Page 1 /184
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.