Bibliographies thématiques / Metabolomics and trascriptomics analysis

Littérature scientifique sur le sujet « Metabolomics and trascriptomics analysis »

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Publié le 11 mars 2023

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Articles de revues sur le sujet "Metabolomics and trascriptomics analysis"

Worley, Bradley, et Robert Powers. « Multivariate Analysis in Metabolomics ». Current Metabolomics 1, n^o 1 (1 novembre 2012) : 92–107. http://dx.doi.org/10.2174/2213235x11301010092.

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Worley, Bradley, et Robert Powers. « Multivariate Analysis in Metabolomics ». Current Metabolomics 1, n^o 1 (1 novembre 2012) : 92–107. http://dx.doi.org/10.2174/2213235x130108.

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Chen, Yang, En-Min Li et Li-Yan Xu. « Guide to Metabolomics Analysis : A Bioinformatics Workflow ». Metabolites 12, n^o 4 (15 avril 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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IKEDA, Kazutaka, et Takeshi BAMBA. « Hydrophobic Metabolite Analysis in Metabolomics ». Journal of the Mass Spectrometry Society of Japan 65, n^o 5 (2017) : 199–202. http://dx.doi.org/10.5702/massspec.s17-48.

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Jansen, J. J., H. C. J. Hoefsloot, H. F. M. Boelens, J. van der Greef et A. K. Smilde. « Analysis of longitudinal metabolomics data ». Bioinformatics 20, n^o 15 (15 avril 2004) : 2438–46. http://dx.doi.org/10.1093/bioinformatics/bth268.

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

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Saglik, Ayhan, Ismail Koyuncu, Ataman Gonel, Hamza Yalcin, Fatih Mehmet Adibelli et Muslum Toptan. « Metabolomics analysis in pterygium tissue ». International Ophthalmology 39, n^o 10 (8 janvier 2019) : 2325–33. http://dx.doi.org/10.1007/s10792-018-01069-2.

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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, n^o 8 (août 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, n^o 8 (15 juillet 2016) : 535–48. http://dx.doi.org/10.1002/jms.3780.

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Moseley, Hunter N. B. « ERROR ANALYSIS AND PROPAGATION IN METABOLOMICS DATA ANALYSIS ». Computational and Structural Biotechnology Journal 4, n^o 5 (janvier 2013) : e201301006. http://dx.doi.org/10.5936/csbj.201301006.

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Thèses sur le sujet "Metabolomics and trascriptomics analysis"

GUARNERIO, Chiara Francesca. « Effects of enod40 overexpression in non legume plants ». Doctoral thesis, Università degli Studi di Verona, 2010. http://hdl.handle.net/11562/343215.

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Il gene ENOD40 è una nodulina precoce ed è indotto durante i primi stadi di formazione del nodulo radicale in risposta alle interazioni tra le leguminose ed i batteri simbionti del genere Rhizobia. Omologhi del gene ENOD40 sono stati identificati in diverse specie e la sua espressione, non unicamente correlata alla formazione del nodulo, è stata osservata in tessuti giovani e meristematici. Una caratteristica che accomuna i geni ENOD40 è l’assenza di un lunga open reading frame (ORF); al contrario, molte piccole ORF sono generalmente presenti nei trascritti. Il gene contiene due regioni altamente conservate chiamate box1 e box2. Tra le diverse specie è conservata l’ORF (ORF1) del box1, che sembra codificare per un putativo peptide di 10-13 amminoacidi. Inoltre, il gene contiene regioni corrispondenti a strutture conservate a livello del trascritto. Sei domini sono stati individuati nel mRNA del gene e due di questi domini sono fortemente conservati tra le leguminose e le non leguminose. Nonostante decenni di ricerche, il ruolo del gene ENOD40 non è stato finora completamente chiarito. La natura biologica del gene è tuttora in discussione, infatti se l’attività biologica del gene dipenda dall’ RNA o da entrambi è ancora da chiarire. I due principali obiettivi del mio progetto di ricerca sono: da una parte, indagare la possibile presenza del putativo peptide codificato dal box1 utilizzando cellule BY-2 che overesprimono il gene e dall’altra, studiare il ruolo del gene ENOD40 in piante non leguminose, utilizzando Arabidopsis thaliana. Nella prima parte del lavoro è stata messa appunto una procedura di purificazione per cercare il putativo peptide in cellule BY-2 che overesprimevano il gene ENOD40 di tabacco. Fin ad ora il putativo peptide non è mai stato trovato in vivo; è stato però suggerito da diverse osservazioni che il gene potrebbe, almeno in parte, agire attraverso il peptide codificato dall’ORF1. La procedura messa appunto consiste in un cut-off iniziale, seguita da cromatografia a scambio ionico, estrazione di cambio solido, HPLC-DAD e spettrometria di massa (LC-ESI-MS e MALDI-TOF). Purtroppo, nonostante i diversi tentativi per mettere appunto la procedura di purificazione e le diverse tecniche utilizzate per l'analisi delle frazioni putativamente peptide-arricchite, solo l’analisi MALDI-TOF PSD ha dato un primo indizio sulla possibile presenza del peptide in cellule BY-2 che overesprimevano il gene ENOD40. Nella seconda parte del lavoro, il possibile ruolo del gene è stato indagato mediante l’analisi metabolomica e trascrizionale in piante di Arabidopsis che overesprimevano il gene ENOD40 di soia. I profili metabolici e trascrizionali di tre linee di Arabidopsis trasformate con il gene ENOD40 sono stati acquisiti e confrontati con quelli ottenuti da piante wild type. In seguito, l'analisi dei biomarcatori dei dati ottenuti dalle analisi di metabolomica e trascrittomica è stata utilizzata per identificare i metaboliti e i trascritti che hanno mostrato un maggiore correlazione con l'overespressione del gene. Dai profili metabolici è emerso che le tre linee trasformate sono caratterizzate dalla presenza di glucosinolati, mentre i flavonoidi caratterizzano principalmente le piante wild type. Per quanto riguarda i profili trascrizionali, la maggior parte dei geni indotti nelle tre linee trasformate (12 su 23), sono correlati con processi che avvengono nella parete cellulare. Dato che, la parete cellulare determina la forma delle cellule, il gene ENOD40 potrebbe essere coinvolto in un processo che controlla la composizione e le dinamiche della parete. Precedenti studi morfologici condotti sulle stesse linee trasformate di Arabidopsis hanno dimostrato che queste piante presentano organi con dimensioni normali ma formati da celle più piccole; inoltre protoplasti di Arabidopsis trasfettati con il gene ENOD40 sono caratterizzati da una ridotta espansione. Questi dati hanno suggerito che il gene potrebbe avere un ruolo nel mantenere le cellule in uno stadio giovane e poco differenziato. L'osservazione che le linee trasformate di Arabidopsis accumulino glucosinolati, metabolici tipici di tessuti giovani, suggerisce che, anche dal punto di vista metabolico, le cellule trasformate hanno caratteristiche tipiche di cellule più giovani, mentre le cellule wild type accumulano maggiormente i flavonoidi, metaboliti secondari tipici dello stato differenziato. Per quanto riguarda l'analisi trascrizionale, dal momento che le piante trasformate sono morfologicamente caratterizzate da cellule con dimensioni ridotte, i geni indotti in queste linee, potrebbero essere coinvolti nella prevenzione dell’espansione cellulare. Questo ruolo del gene, atto a mantenere le cellule in uno stadio giovanile, è supportato anche dai profili di espressione del gene riportati in letteratura.
ENOD40 is an Early Nodulin gene that it is know to play a key role in nodule formation in response to interaction of legume plants with symbiotic Rhizobium bacteria. Homologues of ENOD40 genes have been identified in several plant species and its expression is observed during the initiation and development of new organs, such as nodules, lateral roots, young leaves and stipule primordia. ENOD40 gene has an unusual structure: it lacks a long open reading frame, but several short ORFs are present. Moreover, at nucleotide level, two regions, named box1 and box2, are highly conserved among all ENOD40 genes. In box 1 region, a highly conserved ORF (ORF 1) is present and it seems to encode a putative peptide of 10-13 amino acids. Furthermore, the gene contains regions corresponding to conserved secondary structures of the transcript. Six domains were identified in ENOD40 mRNA and two of these domains are strongly conserved among legume and non legume species. Despite several researches, the roles of the ENOD40 gene has not been so far completely elucidated. Moreover, whether the biological activity should be ascribed to RNA or peptide, or both, is still unclear. For this reason, the two main goals of the research are: to investigate the possible presence of the putative peptide encoded by box1 of the ENOD40 gene in BY-2 cells and to investigate the role of ENOD40 gene in non legume plants, using Arabidopsis thaliana. That ENOD40 could act, at least in part, through the peptide encode by box1 is suggested by several observations, but no one have revealed biochemically the putative peptide. In the first part of the work a purification procedure consisting of membrane cut-off, ion exchange chromatography, solid exchange extraction, HPLC-DAD and mass spectrometry (LC-ESI-MS and MALDI-TOF) was set up to search for the putative peptide in BY-2 cells overexpressing NtENOD40 gene. Unfortunately, despite several attempts to set up the purification procedure and the different and sensitive techniques used for the analysis of the putatively peptide-enriched fractions, only MALDI-TOF PSD analysis gave an initial clue of the possible presence of the peptide in ENOD40 overexpressing BY2 cells. In the second part of the work, the possible role of the gene has been investigated through the metabolomics and transcriptomics characterization of ENOD40 overexpressing Arabidopsis plants. Metabolite and transcriptional profiles of the three Arabidopsis lines overexpressing soybean ENOD40 gene were acquired and compared to those obtained from wild type plants. Afterward, biomarker analysis of metabolomic and transcriptomic dataset was used in order to identify the metabolites and transcripts that showed the higher correlation with the overexpression of ENOD40 gene. In the metabolite profiles, glucosinolate metabolites characterized all the three transformed lines compared with the wild type, while flavonoids mainly characterized wild type plants. With regard to transcriptional profiling, most of the genes upregulated in the three transformed lines (twelve out of twenty-three), were correlated with processes occurring in the cell wall. Thus, the cell wall is the mechanical determinant of cell shape and size ENOD40 gene could be involved in a process that controls the composition and the dynamics of the cell wall. In conclusion, previous morphological studies on the same Arabidopsis thaliana ENOD40 transformed lines used in this work have been showed that these plants are characterised by normal organs containing smaller cells, and on ENOD40 transfected Arabidopsis protoplasts are characterized by reduced expansion, suggested that the gene could have some role in keeping the cells in a “young” state . The observation that ENOD40 transformed Arabidopsis lines accumulate high levels of glucosinolates, that are typical of the young tissues, suggests that, also from the metabolic point of view, the transformed cells have features typical of younger cells, whereas wild type cells use their metabolic resources to accumulate flavonoids, another class of secondary metabolites more typical of differentiated state. With regard to transcriptomic analysis, since transformed plants are morphologically characterized by small cell size, the genes upregulated in the transformed lines, involved in cell wall dynamics and composition, could be involved in the prevention of cell expansion. The role of ENOD40 in maintenance of cells in a “young state” is also supported by the expression patterns of ENOD40 genes reported in literature.

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Yet, Idil. « Integrated epigenomics and metabolomics analysis in twins ». Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/integrated-epigenomics-and-metabolomics-analysis-in-twins(4d0fb76b-cc2b-4e31-8950-a7ffb5b91363).html.

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Epigenetics and metabolomics are rapidly growing areas of research, in part due to recent advances in technology that have allowed for a wide coverage of the human genome. Metabolites are small compounds present in cell and body fluids, and are involved in biochemical processes of the cell. Quantitative trait loci associated with levels of individual metabolites (mQTLs) have been identified from numerous metabolome GWAS. Here, I analysed metabolite levels in twins with the aim of identifying genetic variants that influence metabolomic traits (mQTLs) using two different metabolomics platforms, and consequently compared the results to report stable metabolites on both technologies to ultimately enable combining metabolite profiles across these two platforms. DNA methylation is a biochemical process that is vital for mammalian development. It is present throughout the genome and is the most extensively studied epigenetic mark. Previous studies have explored the heritability of DNA methylation and have identified methylation QTLs (meQTL). Here, I identified meQTLs with the goal of assesing the evidence of genetic effects influence not only DNA methylation levels, but also variability by using MZ-twin discordance as a measure of variance. Epigenetic mechanisms and metabolomic profiles have both been shown to play a role in gene expression and susceptibility for complex human disease. Here, I analysed the association between type 2 diabetes and metabolomic and epigenetic datasets and combined the data to identify connections between these levels of biological data at genetic variants linked to type 2 diabetes to gain more insight into the disease susceptibility and progression. Overall, the results confirmed previous findings of strong genetic influences on metabolites and extend current knowledge about genetic effects underlying several biochemical pathways. Additionally, the results also showed genetic influences on DNA methylation, and give insights into mechanisms by which genetic impacts epigenetic processes. Lastly, the findings show that specific genetic susceptibility variants for type 2 diabetes can also impact epigenetic and metabolomics profiles, and can help improve our understanding of the disease etiology.

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Muhamad, Ali Howbeer. « Metabolomics investigation of microbial cell factories ». Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/metabolomics-investigation-of-microbial-cell-factories(2e2f5f58-d38a-4c77-966b-56ce92aec619).html.

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The stream of new technological advancements and their integration into the field of microbiology have contributed significantly towards our understanding of life in the micro-scale world, making the fields of microbiology and biotechnology shine like never before. Since 1980, the recombinant protein-based therapeutics industry has become one of the fastest growing sectors in the biopharmaceutical market. Nearly 30% of commercially available recombinant proteins are produced in Escherichia coli, making this species one of the most commonly used bacterial expression systems for the production of recombinant biotherapeutics. However, when it comes to the production of enzymes and bioactive secondary metabolites (antibiotic, antifungal, antiviral and immunosuppressant), Streptomyces species remain the major producer within this sector. Meeting the high demand for such products requires a clear and in-depth understanding of the bioprocesses involved to achieve high yield and quality products, whilst keeping the process industrially attractive. It is generally accepted that the metabolome, as a down-stream process to the genome and proteome, may provide a clearer picture of a biological system. Thus, in this thesis a series of metabolomics approaches were adopted to obtain a deeper insight into the metabolic effects of recombinant protein production in E. coli and Streptomyces lividans. Furthermore, a Geobacter-based biomagnetite nanoparticle production system which displayed a prolonged lag phase upon scale-up was investigated by employing metabolic profiling and fingerprinting approaches combined with multivariate analysis strategies, to identify growth-limiting metabolites. The results of this analysis identified nicotinamide as the growth limiting metabolite. Nicotinamide-feeding experiments confirmed the above findings, leading to improved biomass yield whilst restoring the lag phase to bench-scale level. Raman and Fourier transform infrared spectroscopies combined with stable isotopic probing strategies were also employed to demonstrate the application of metabolic fingerprinting in providing detailed biochemical information for quantitative characterisation and differentiation of E. coli cells at community and single-cell levels. The single-cell approach proved promising, offering detailed biochemical information and perhaps accompanying other cultivation-free approaches such as metagenomics for further future investigations. It is hoped that the advances made in these studies have proved the potential applications of metabolomics strategies to aid the optimisation of microbially-driven bioprocesses.

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Klünder, Christina. « Metabolomics for toxicity analysis using the chlorophyte Scenedesmus vacuolatus / ». Leipzig [u.a.], 2009. http://www.ufz.de/data/ufzdiss_2_2009_9947.pdf.

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Gloaguen, Yoann. « Supporting analysis, visualisation and biological interpretation of metabolomics datasets ». Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/8433/.

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Over the past decades, the emerging omics technologies have enabled scientists to take a step further in the investigation of biological systems. From food safety to stratified medicine, omics technologies are now an essential and powerful means to study biological processes. Omics technologies are however at different stages of maturity, and the most recent field of the omics family, metabolomics, is still in its infancy. Metabolomics attempts to catalogue, characterise and quantify all small molecules constitutive of a biological system. Liquid Chromatography - Mass Spectrometry (LCMS) is now the most commonly used technique to generate metabolomics data. The method allows the detection of hundreds of metabolites from a single sample and can provide a rapid assignment of formulae to detected masses using high accuracy mass spectrometers. While analytical methods are well developed, support for linking metabolites to detected features and interpreting the results of a data analysis in a biological context is still poorly developed. Significant challenges also arise from the additional steps required to export the data to third party environments to create a biological context. The study of integrated omics datasets as a single system has also shown to provide greater inferences than the study of each omics separately. Methods to integrate the different omics layers of biological systems are, however, at an early stage of development and no standard approach currently exists to provide a holistic view of organisms systems organisation. The objective of this thesis is to formalise, standardise and unify the data analysis of the metabolomics field, by providing to biologists the tools to support them from planning to analysis to biological impact reporting. The work presented here focuses particularly on untargeted LC-MS metabolomics approaches and attempts to assist non-expert users in performing their own analysis of metabolomics datasets. The project also aims to enable systematic biological interpretation of metabolomics datasets. The first part of the thesis focuses on creating the foundation of a unified environment for LC-MS metabolomics data analysis. Subsequently, the created environment will be expanded to integrate and support the latest technological advances in the field and provide better support for both designing studies and interpreting analysis results in a biological context. Finally, the last part of this thesis concentrates on integrating metabolomics data with other omics datasets in an attempt to provide a holistic view of a biological system.

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Abdelrazig, Salah M. A. « Mass spectrometry for high-throughput metabolomics analysis of urine ». Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/30600/.

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Direct electrospray ionisation-mass spectrometry (direct ESI-MS), by omitting the chromatographic step, has great potential for application as a high-throughput approach for untargeted urine metabolomics analysis compared to liquid chromatography-mass spectrometry (LC-MS). The rapid development and technical innovations revealed in the field of ambient ionisation MS such as nanoelectrospray ionisation (nanoESI) chip-based infusion and liquid extraction surface analysis mass spectrometry (LESA-MS) suggest that they might be suitable for high-throughput metabolomics analysis. In this thesis, LC-MS and high-throughput direct ESI-MS methods using high resolution orbital trap mass spectrometer were developed and validated for untargeted metabolomics of human urine. Three different direct ESI-MS techniques were explored and compared with LC-MS: flow injection electrospray ionisation-MS (FIE-MS), chip-based infusion and LESA-MS of dried urine spots on a cell culture slide. A high-throughput sample preparation protocol was optimised using in-house artificial urine. Urine samples after consumption of green tea and healthy controls were used as a model to explore the performance and classification ability of the direct ESI-MS. High-throughput data pre-processing and multivariate analysis protocols were established for each method. The developed methods were finally applied for the analysis of clinical urine samples for biomarker discovery and to investigate the metabolic changes in osteoarthritis and malaria. Also, the methods were applied to study the effect of oligofructose diet on the gut microbial community of healthy subjects. The analytical performance of the methods for urine metabolomics was validated using quality control (QC) and principal component analysis (PCA) approaches. Rigorous validation including cross-validation, permutation test, prediction models and area under receiver operating characteristic (ROC) curve (AUC) was performed across the generated datasets using the developed methods. Analysis of green tea urine samples generated 4128, 748, 1064 and 1035 ions from LC-MS, FIE-MS, chip-based infusion and LESA-MS analysis, respectively. A selected set of known green tea metabolites in urine were used to evaluate each method for detection sensitivity. 15 metabolites were found with LC-MS compared to 8, 5 and 6 with FIE-MS, chip-based infusion and LESA, respectively. The developed methods successfully differentiated between the metabolic profiles of osteoarthritis active patients and healthy controls (Q2 0.465 (LC-MS), 0.562 (FIE-MS), 0.472 (chip-based infusion) and 0.493 (LESA-MS)). The altered level of metabolites detected in osteoarthritis patients showed a perturbed activity in TCA cycle, pyruvate metabolism, -oxidation pathway, amino acids and glycerophospholipids metabolism, which may provide evidence of mitochondrial dysfunction, inflammation, oxidative stress, collagen destruction and use of lipolysis as an alternative energy source in the cartilage cells of osteoarthritis patients. FIE-MS, chip-based infusion and LESA-MS increased the analysis throughput and yet they were able to provide 33%, 44% and 44%, respectively, of the LC-MS information, indicating their great potential for diagnostic application in osteoarthritis. Malaria samples datasets generated 9,744 and 576 ions from LC-MS and FIE-MS, respectively. Supervised multivariate analysis using OPLS-DA showed clear separation and clustering of malaria patients from controls in both LC-MS and FIE-MS methods. Cross-validation R2Y and Q2 values obtained by FIE-MS were 0.810 and 0.538, respectively, which are comparable to the values of 0.993 and 0.583 achieved by LC-MS. The sensitivity and specificity were 80% and 77% for LC-MS and FIE-MS, respectively, indicating valid, reliable and comparable results of both methods. With regards to biomarker discovery, altered level of 30 and 17 metabolites were found by LC-MS and FIE-MS, respectively, in the urine of malaria patients compared to healthy controls. Among these metabolites, pipecolic acid, taurine, 1,3-diacetylpropane, N-acetylspermidine and N-acetylputrescine may have the potential of being used as biomarkers of malaria. LC-MS and FIE-MS were able to separate urine samples of healthy subjects on oligofructose diet from controls (specificity/sensitivity 80%/88% (LC-MS) and 71%/64% (FIE-MS)). An altered level of short chain fatty acids (SCFAs), fatty acids and amino acids were observed in urine as a result of oligofructose intake, suggesting an increased population of the health-promoting Bifidobacterium and a decreased Lactobacillus and Enterococcus genera in the colon. In conclusion, the developed direct ESI-MS methods demonstrated the ability to differentiate between inherent types of urine samples in disease and health state. Therefore they are recommended to be used as fast diagnostic tools for clinical urine samples. The developed LC-MS method is necessary when comprehensive biomarker screening is required.

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Beisken, Stephan Andreas. « Informatics for tandem mass spectrometry-based metabolomics ». Thesis, University of Cambridge, 2014. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708325.

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Duffy, Kate I. « Application of metabolomics to the analysis of ancient organic residues ». Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/5670/.

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The grape is arguably one of the oldest cultivated products in human history and the analysis of its main product, wine, reveals clues to trade and associations of previous civilizations. In ancient times, wine was stored in clay amphorae, which, if not properly sealed with resin or pitch allowed the wine to wick into clay matrices, dry, and polymerize producing insoluble, intractable materials that may remain within the matrix for several thousand years. Presently, identification of wine residue is based upon the extraction of these polymeric materials from the ceramic matrix and analyzing/identifying the chemical fingerprints. Two main biomarkers have historically been employed for the identification of wine residue: tartaric and syringic acids. In some cases, the presence of one of these biomarkers has been designated as the confirmatory signature of wine often leading to false positives as amphorae were re-used in antiquity. Herein, a novel approach utilizing metabolomics has been applied to archaeological objects in order to further mine possible biomarkers for a more accurate assessment of the original foodstuff. An untargeted metabolic profiling method was combined with a targeted analytical method resulting in the successful validation of eight representative biomarkers in two separate archaeological sites.

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Daub, Carsten O. « Analysis of integrated transcriptomics and metabolomics data a systems biology approach / ». [S.l. : s.n.], 2004. http://pub.ub.uni-potsdam.de/2004/0025/daub.pdf.

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Daub, Carsten Oliver. « Analysis of integrated transcriptomics and metabolomics data : a systems biology approach ». Phd thesis, Universität Potsdam, 2004. http://opus.kobv.de/ubp/volltexte/2005/138/.

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Moderne Hochdurchsatzmethoden erlauben die Messung einer Vielzahl von komplementären Daten und implizieren die Existenz von regulativen Netzwerken auf einem systembiologischen Niveau. Ein üblicher Ansatz zur Rekonstruktion solcher Netzwerke stellt die Clusteranalyse dar, die auf einem Ähnlichkeitsmaß beruht.
Wir verwenden das informationstheoretische Konzept der wechselseitigen Information, das ursprünglich für diskrete Daten definiert ist, als Ähnlichkeitsmaß und schlagen eine Erweiterung eines für gewöhnlich für die Anwendung auf kontinuierliche biologische Daten verwendeten Algorithmus vor. Wir vergleichen unseren Ansatz mit bereits existierenden Algorithmen. Wir entwickeln ein geschwindigkeitsoptimiertes Computerprogramm für die Anwendung der wechselseitigen Information auf große Datensätze. Weiterhin konstruieren und implementieren wir einen web-basierten Dienst fuer die Analyse von integrierten Daten, die durch unterschiedliche Messmethoden gemessen wurden. Die Anwendung auf biologische Daten zeigt biologisch relevante Gruppierungen, und rekonstruierte Signalnetzwerke zeigen Übereinstimmungen mit physiologischen Erkenntnissen.
Recent high-throughput technologies enable the acquisition of a variety of complementary data and imply regulatory networks on the systems biology level. A common approach to the reconstruction of such networks is the cluster analysis which is based on a similarity measure.
We use the information theoretic concept of the mutual information, that has been originally defined for discrete data, as a measure of similarity and propose an extension to a commonly applied algorithm for its calculation from continuous biological data. We compare our approach to previously existing algorithms. We develop a performance optimised software package for the application of the mutual information to large-scale datasets. Furthermore, we design and implement a web-based service for the analysis of integrated data measured with different technologies. Application to biological data reveals biologically relevant groupings and reconstructed signalling networks show agreements with physiological findings.

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Livres sur le sujet "Metabolomics and trascriptomics analysis"

K, Saito, Dixon Richard A. 1951- et Willmitzer Lothar, dir. Plant metabolomics. Berlin : Springer, 2006.

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Xia, Yinglin, Jun Sun et Xiaotao Shen, Ph.D., Stanford University School of Medicine. Statistical Data Analysis of Microbiomes and Metabolomics. Washington, DC, USA : American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5035.

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Li, Shuzhao, dir. Computational Methods and Data Analysis for Metabolomics. New York, NY : Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0239-3.

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Wolfram, Weckwerth, dir. Metabolomics : Methods and protocols. Totowa, N.J : Humana Press, 2007.

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Datta, Susmita, et Bart J. A. Mertens, dir. Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45809-0.

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Roessner, Ute, et Daniel Anthony Dias. Metabolomics tools for natural product discovery : Methods and protocols. New York : Humana Press, 2013.

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Bagchi, Debasis. Genomics, proteomics, and metabolomics in nutraceuticals and functional foods. Ames, Iowa : Wiley-Blackwell, 2010.

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Bagchi, Debasis, Anand Swaroop et Manashi Bagchi. Genomics, proteomics and metabolomics in nutraceuticals and functional foods. Chichester, West Sussex : John Wiley & Sons, Inc., 2015.

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Armitage, Emily G. Correlation-based network analysis of cancer metabolism : A new systems biology approach in metabolomics. New York : Springer, 2014.

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Debasis, Bagchi, Lau Francis et Bagchi Manashi, dir. Genomics, proteomics, and metabolomics in nutraceuticals and functional foods. Ames, Iowa : Wiley-Blackwell, 2010.

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Chapitres de livres sur le sujet "Metabolomics and trascriptomics analysis"

Scholz, Matthias, et Joachim Selbig. « Visualization and Analysis of Molecular Data ». Dans Metabolomics, 87–104. Totowa, NJ : Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_6.

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Schuster, Stefan, Axel Kamp et Mikhail Pachkov. « Understanding the Roadmap of Metabolism by Pathway Analysis ». Dans Metabolomics, 199–226. Totowa, NJ : Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_12.

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Steuer, Ralf, Katja Morgenthal, Wolfram Weckwerth et Joachim Selbig. « A Gentle Guide to the Analysis of Metabolomic Data ». Dans Metabolomics, 105–26. Totowa, NJ : Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_7.

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Tolstikov, Vladimir V., Oliver Fiehn et Nobuo Tanaka. « Application of Liquid Chromatography-Mass Spectrometry Analysis in Metabolomics ». Dans Metabolomics, 141–55. Totowa, NJ : Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-244-1_9.

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Lin, Shili, Denise Scholtens et Sujay Datta. « Metabolomics Data Analysis ». Dans Bioinformatics Methods, 211–32. Boca Raton : Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315153728-10.

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Wu, Jun-Fang, et Yulan Wang. « Multivariate Analysis of Metabolomics Data ». Dans Plant Metabolomics, 105–22. Dordrecht : Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9291-2_5.

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Wittmann, Christoph, et Jean-Charles Portais. « Metabolic Flux Analysis ». Dans Metabolomics in Practice, 285–312. Weinheim, Germany : Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527655861.ch12.

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Lubbe, Andrea, Kashif Ali, Robert Verpoorte et Young Hae Choi. « NMR-Based Metabolomics Analysis ». Dans Metabolomics in Practice, 209–38. Weinheim, Germany : Wiley-VCH Verlag GmbH & Co. KGaA, 2013. http://dx.doi.org/10.1002/9783527655861.ch9.

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Violante, Sara, Mirela Berisa, Tiffany H. Thomas et Justin R. Cross. « Stable Isotope Tracers for Metabolic Pathway Analysis ». Dans High-Throughput Metabolomics, 269–83. New York, NY : Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_17.

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Markley, John L., Hesam Dashti, Jonathan R. Wedell, William M. Westler et Hamid R. Eghbalnia. « Tools for Enhanced NMR-Based Metabolomics Analysis ». Dans NMR-Based Metabolomics, 413–27. New York, NY : Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9690-2_23.

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Actes de conférences sur le sujet "Metabolomics and trascriptomics analysis"

Porokhin, Vladimir, Xinmeng Li et Soha Hassoun. « Pathway Enrichment Analysis for Untargeted Metabolomics ». Dans BCB '17 : 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3107411.3108233.

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Hu, Ting, Weidong Zhang, Zhaozhi Fan, Guang Sun, Sergei Likhodi, Edward Randell et Guangju Zhai. « METABOLOMICS DIFFERENTIAL CORRELATION NETWORK ANALYSIS OF OSTEOARTHRITIS ». Dans Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2015. http://dx.doi.org/10.1142/9789814749411_0012.

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Ullah, Ehsan, Raghvendra Mall, Reda Rawi et Halima Bensmail. « Statistical and Network Analysis of Metabolomics Data ». Dans BCB '16 : ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2975167.2985683.

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Livengood, Philip, Ross Maciejewski, Wei Chen et David S. Ebert. « A visual analysis system for metabolomics data ». Dans 2011 IEEE Symposium on Biological Data Visualization (BioVis). IEEE, 2011. http://dx.doi.org/10.1109/biovis.2011.6094050.

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Cardoso, Sara, Miguel Rocha, Telma Afonso et Marcelo Maraschin. « WebSpecmine : a website for metabolomics data analysis and mining. » Dans 3rd International Electronic Conference on Metabolomics. Basel, Switzerland : MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05842.

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Laatikainen, Reino, Pekka Laatikainen et Elias Hakalehto. « QUANTITATIVE QUANTUM MECHANICAL NMR ANALYSIS : THE SUPERIOR TOOL FOR ANALYSIS OF BIOFLUIDS ». Dans The 1st International Electronic Conference on Metabolomics. Basel, Switzerland : MDPI, 2016. http://dx.doi.org/10.3390/iecm-1-c005.

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Yuan, P. « Lipid Metabolomics Analysis of Pulmonary Veno-Occlusive Disease ». Dans American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a1189.

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González-Domínguez, Raúl, Ana Sayago et Ángeles Fernández-Recamales. « Comparison of complementary statistical analysis approaches in metabolomic food traceability ». Dans 3rd International Electronic Conference on Metabolomics. Basel, Switzerland : MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05839.

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Theodoridis, Georgios, Olga Begou, Olga Deda, Helen Gika, Ioannis Taitzoglou, Nikolaos Raikos et Agapios Agapiou. « URINE AND FECES METABOLOMICS-BASED ANALYSIS OF CAROB TREATED RATS ». Dans The 2nd International Electronic Conference on Metabolomics. Basel, Switzerland : MDPI, 2017. http://dx.doi.org/10.3390/iecm-2-04992.

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Laatikainen, Reino, Pekka Laatikainen et Hannu Maaheimo. « Quantitative Quantum Mechanical Spectral Analysis (qQMSA) of Spectra of 1000+1 Chemical Shifts and Other Biological Systems ». Dans 3rd International Electronic Conference on Metabolomics. Basel, Switzerland : MDPI, 2018. http://dx.doi.org/10.3390/iecm-3-05844.

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Rapports d'organisations sur le sujet "Metabolomics and trascriptomics analysis"

Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer et Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, juillet 2013. http://dx.doi.org/10.32747/2013.7593400.bard.

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Fruit quality is determined by numerous genetic factors that affect taste, aroma, ‎color, texture, nutritional value and shelf life. To unravel the genetic components ‎involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.

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Rabinowitz, Joshua D. Final Technical Report--Quantitative analysis of metabolic regulation by integration of metabolomics, proteomics, and fluxomics. Office of Scientific and Technical Information (OSTI), décembre 2018. http://dx.doi.org/10.2172/1487155.

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Lidstrom, Mary E., Ludmila Chistoserdova, Marina G. Kalyuzhnaya, Victoria J. Orphan et David A. Beck. Systems level insights into alternate methane cycling modes in a freshwater lake via community transcriptomics, metabolomics and nano-SIMS analysis. Office of Scientific and Technical Information (OSTI), août 2014. http://dx.doi.org/10.2172/1149958.

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