Relevant bibliographies by topics / High-throughput mass spectrometry

Academic literature on the topic 'High-throughput mass spectrometry'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "High-throughput mass spectrometry"

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Want, Elizabeth, Michael Greig, Bruce Compton, Ben Bolaños, and Gary Siuzdak. "Mass spectrometry in high throughput analysis." Spectroscopy 17, no. 4 (2003): 663–80. http://dx.doi.org/10.1155/2003/949412.

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Shen, Luhou, Johnny Zhang, Qian Yang, Nicholas E. Manicke, and Zheng Ouyang. "High throughput paper spray mass spectrometry analysis." Clinica Chimica Acta 420 (May 2013): 28–33. http://dx.doi.org/10.1016/j.cca.2012.10.025.

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Han, Jun, Raju Datla, Sammy Chan, and Christoph H. Borchers. "Mass spectrometry-based technologies for high-throughput metabolomics." Bioanalysis 1, no. 9 (December 2009): 1665–84. http://dx.doi.org/10.4155/bio.09.158.

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Zhang, Hui, Chang Liu, Wenyi Hua, Lucien P. Ghislain, Jianhua Liu, Lisa Aschenbrenner, Stephen Noell, et al. "Acoustic Ejection Mass Spectrometry for High-Throughput Analysis." Analytical Chemistry 93, no. 31 (July 28, 2021): 10850–61. http://dx.doi.org/10.1021/acs.analchem.1c01137.

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Chalk, Rod, Georgina Berridge, Leela Shrestha, Claire Strain-Damerell, Pravin Mahajan, Wyatt Yue, Opher Gileadi, and Nicola Burgess-Brown. "High-Throughput Mass Spectrometry Applied to Structural Genomics." Chromatography 1, no. 4 (October 9, 2014): 159–75. http://dx.doi.org/10.3390/chromatography1040159.

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Maurer, Hans H., and Frank T. Peters. "Toward High-Throughput Drug Screening Using Mass Spectrometry." Therapeutic Drug Monitoring 27, no. 6 (December 2005): 686–88. http://dx.doi.org/10.1097/01.ftd.0000180224.19384.f0.

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Bakhtiar, R., and F. L. S. Tse. "High-throughput chiral liquid chromatography/tandem mass spectrometry." Rapid Communications in Mass Spectrometry 14, no. 13 (2000): 1128–35. http://dx.doi.org/10.1002/1097-0231(20000715)14:13<1128::aid-rcm1>3.0.co;2-5.

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Forbes, Chris D., Joshuaine G. Toth, Can C. Özbal, William A. Lamarr, Jennifer A. Pendleton, Sandra Rocks, Richard W. Gedrich, David G. Osterman, James A. Landro, and Kevin J. Lumb. "High-Throughput Mass Spectrometry Screening for Inhibitors of Phosphatidylserine Decarboxylase." Journal of Biomolecular Screening 12, no. 5 (August 2007): 628–34. http://dx.doi.org/10.1177/1087057107301320.

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A high-throughput mass spectrometry assay to measure the catalytic activity of phosphatidylserine decarboxylase (PISD) is described. PISD converts phosphatidylserine to phosphatidylethanolamine during lipid synthesis. Traditional methods of measuring PISD activity are low throughput and unsuitable for the high-throughput screening of large compound libraries. The high-throughput mass spectrometry assay directly measures phosphatidylserine and phosphatidylethanolamine using the RapidFire™ platform at a rate of 1 sample every 7.5 s. The assay is robust, with an average Z′ value of 0.79 from a screen of 9920 compounds. Of 60 compounds selected for confirmation, 54 are active in dose-response studies. The application of high-throughput mass spectrometry permitted a high-quality screen to be performed for an otherwise intractable target. ( Journal of Biomolecular Screening 2007:628-634)
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Cupp-Sutton, Kellye A., and Si Wu. "High-throughput quantitative top-down proteomics." Molecular Omics 16, no. 2 (2020): 91–99. http://dx.doi.org/10.1039/c9mo00154a.

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Clendinen, Chaevien S., María Eugenia Monge, and Facundo M. Fernández. "Ambient mass spectrometry in metabolomics." Analyst 142, no. 17 (2017): 3101–17. http://dx.doi.org/10.1039/c7an00700k.

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Dissertations / Theses on the topic "High-throughput mass spectrometry"

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Pierce, Carrie. "High throughput mass spectrometry for microbial identification." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/43741.

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Bacteria cause significant morbidity and mortality throughout the world, including deadly diseases such as tuberculosis, meningitis, cholera, and pneumonia. Timely and accurate bacterial identification is critical in areas such as clinical diagnostics, environmental monitoring, food safety, water and air quality assessment, and identification of biological threat agents. At present, there is an established need for high throughput, sensitive, selective, and rapid methods for the detection of pathogenic bacteria, as existing methods, while nominally effective, have failed to sufficiently reduce the massive impact of bacterial contamination and infection. The work presented in this thesis focuses on addressing this need and augmenting conventional microorganism research through development of mass spectrometry (MS)-based proteomic applications. MS, a well established tool for addressing biological problems, offers a broad range of laboratory procedures that can be used for taxonomic classification and identification of microorganisms. These methods provide a powerful complement to many of the widely used molecular biology approaches and play critical functions in various fields of science. While implementation of modern biomolecule-identifying instrumentation, such as MS, has long been postulated to have a role in the microbiology laboratory, it has yet to be accepted on a large scale. Described in this document are MS methods that erect strong foundations on which new bacterial diagnostics may be based. A general introduction on key aspects of this work is presented in Chapter 1, where different approaches for detection of pathogenic bacteria are reviewed, and an overview regarding MS and microbial identification is provided. Chapter 2 presents the first implementation of microbial identification via rapid, open air Direct Analysis in Real Time MS (DART MS) to generate ions directly from microbial samples, including the disease-causing bacteria, Coxiella burnetii, Streptococcus pyogenes, and Escherichia coli. Chapter 3 expands on whole cell C. burnetii MS analysis and presents a rapid differentiation method to the strain-level for C. burnetii using mass profiling/fingerprinting matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS and multivariate pattern recognition. Chapter 4 presents a unique "top-down" proteomics approach using 15N-labeled bacteriophage amplification coupled with MALDI-TOF MS as a detector for the rapid and selective identification of Staphylococcus aureus. Chapter 5 extends the idea of using isotopically labeled bacteriophage amplification by implementing a "bottom-up" proteomics approach that not only identifies S. aureus in a sample, but also quantifies the bacterial concentration in the sample using liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI/MS/MS) as a detector. In conclusion, Chapter 6, summarizes and contextualizes the work presented in this dissertation, and outlines how future research can build upon the experimentation detailed in this document.
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Maple, Hannah Jane. "Towards high-throughput fragment screening by mass spectrometry." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.559091.

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Screening for protein-ligand binding interactions is a key step during early stage drug discovery programmes. The fragment-based screening approach has gained popularity in recent years as a highly promising alternative to traditional high-throughput screening (HTS) , which has not yielded the success rate expected in the 'post-genomic' drug discovery era. The increasing use of fragment-based drug discovery (FBDD), however, places additional demands on biophysical screening techniques, and requires that the techniques used are capable of detecting very weak non-covalent interactions (mM Ko values). Existing screerung techniques that have been applied to FBDD, such as nuelear magnetic resonance (NMR), surface plasmon resonance (SPR), isothermal titration calorimetry (ITC) and X-ray crystallography, are all associated with one or more of the following drawbacks: low throughput, high sample consumption and dynamic range limitations. The use of nano- electrospray mass spectrometry (nano-ESI MS) as a means of screening for non-covalent complexes is a relatively recent addition to existing methodologies that shows promise as an orthogonal screening technique. The advantages it offers: high throughput, low sample consumption, generation of stoichiometric information and the ability to determine dissociation constants make this an attractive approach. Presented here is the development and validation of a fully automated screen by nano-ESI MS, capable of detecting fragment binding into the mM KD range. The method was applied for screening against the anti-apoptotic protein target, Bel-XL, and mass spectrometry results were validated using STD-NMR, HSQC-NMR and ITC experiments. Agreement between techniques suggests that mass spectrometry offers a powerful, complementary approach for primary screening. Two alternate, or secondary screerung techniques are also explored. Equilibrium dialysis, combined with nano-ESI MS and STD-NMR, was investigated for the identification of bioactive atropisomers to therapeutic protein targets, Bel-XL and Bcl-2, from mixtures of rotameric compounds. The methodology developed offers an alternative, 'ligand-detect' approach to screening by nano-ESI MS for cases where direct detection of the protein-ligand complex is not possible. Preliminary data are also shown for the application of a microflow capillary NMR probe to automated screening by HSQC NMR experiments. Capillary probes offer excellent mass sensitivity and can be fully automated through interfacing with a liquid handling system. This has the potential to offer a novel fragment screening platform that provides information on the location of compound binding through chemical shift perturbations.
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Boström, Tove. "High-throughput protein analysis using mass spectrometry-based methods." Doctoral thesis, KTH, Proteinteknologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-154513.

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In the field of proteomics, proteins are analyzed and quantified in high numbers. Protein analysis is of great importance and can for example generate information regarding protein function and involvement in disease. Different strategies for protein analysis and quan- tification have emerged, suitable for different applications. The focus of this thesis lies on protein identification and quantification using different setups and method development has a central role in all included papers. The presented research can be divided into three parts. Part one describes the develop- ment of two different screening methods for His6-tagged recombinant protein fragments. In the first investigation, proteins were purified using immobilized metal ion affinity chro- matography in a 96-well plate format and in the second investigation this was downscaled to nanoliter-scale using the miniaturized sample preparation platform, integrated selective enrichment target (ISET). The aim of these investigations was to develop methods that could work as an initial screening step in high-throughput protein production projects, such as the Human Protein Atlas (HPA) project, for more efficient protein production and purification. In the second part of the thesis, focus lies on quantitative proteomics. Protein fragments were produced with incorporated heavy isotope-labeled amino acids and used as internal standards in absolute protein quantification mass spectrometry experiments. The aim of this investigation was to compare the protein levels obtained using quanti- tative mass spectrometry to mRNA levels obtained by RNA sequencing. Expression of 32 different proteins was studied in six different cell lines and a clear correlation between protein and mRNA levels was observed when analyzing genes on an individual level. The third part of the thesis involves the antibodies generated within the HPA project. In the first investigation a method for validation of antibodies using protein immunoenrichment coupled to mass spectrometry was described. In a second study, a method was developed where antibodies were used to capture tryptic peptides from a digested cell lysate with spiked in heavy isotope-labeled protein fragments, enabling quantification of 20 proteins in a multiplex format. Taken together, the presented research has expanded the pro- teomics toolbox in terms of available methods for protein analysis and quantification in a high-throughput format.

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MENG, ZHAOJING. "TOWARDS HIGH-THROUGHPUT ANALYSIS OF RNA USING MASS SPECTROMETRY." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1098054876.

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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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Li, You. "High throughput mass spectrometry based peptide identification search engine by GPUs." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/261.

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Mass spectrometry (MS)based protein and peptide identification has become a solid method in proteomics. In high-throughput proteomics research, the “shotgun method has been widely applied. Database searching is currently the main method of tandem mass spectrometrybased protein identification in shotgun proteomics. The most widely used traditional search engines search for spectra against a database of identified protein sequences. The search engine is evaluated for its efficiency and effectiveness. With the development of proteomics, both the scale and the complexity of the related data are increasing steadily. As a result, the existing search engines face serious challenges. First, the sizes of protein sequence databases are ever increasing. From IPI.Human.v3.22 to IPI.Human.v3.49, the number of protein sequences has increased by nearly one third. Second, the increasing demand of searches against semispecific or nonspecific peptides results in a search space that is approximately 10 to 100 times larger. Finally, posttranslational modifications (PTMs) produce exponentially more modified peptides. The Unimod database (http://www.unimod.org) currently includes more than 1000 types of PTMs. We analyzed the entire identification workflow and discovered three things. First, most search engines spend 50% to 90% of their total time on the scoring module, the most widely used of which is the spectrum dot product (SDP)based scoring module. Second, nearly half of the scoring operations are redundant, which costs more time but does not increase effectiveness. Third, more than half of the spectra cannot be identified via a database search alone, but the identified spectra have a connection with the unidentified ones, which can be clustered by their distances. Based on the above observations, we designed and implemented a new search engine for protein and peptide identification that includes three key modules. First, a parallel index system, based on GPU, organizes the protein database and the spectra with no redundant data, low search computation complexity, and no limitation of the protein database scale. Second, the graphics processing unit (GPU)based SDP module adopts GPUs to accelerate the most time-consuming step in the process. Third, a k-meansbased spectrum-clustering module classifies the unidentified spectra to the identified spectra for further analysis. As general-purpose high-performance parallel hardware, GPUs are promising platforms for the acceleration of database searches in the protein identification process. We designed a parallel index system that accelerated the entire identification process two to five times with no loss of effectiveness, and achieved around 80% linear speedup effect on the cluster. The index system also can be easily adopted by other search engines. We also designed and implemented a parallel SDP-based scoring module on GPUs that exploits the efficient use of GPU registers and shared memory. A single GPU was 30 to 60 times faster than the central processing unit (CPU)based version. We also implemented our algorithm on a GPU cluster and achieved approximately linear acceleration. In addition, a k-meansbased spectrum-clustering module with GPUs can classify the unidentified spectra to the identified spectra at 20 times the speed of the normal k-means spectrum-clustering algorithm.
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Shi, Wunan. "High-Throughput De Novo Sequencing of Transfer RNAs Using Liquid Chromatography-Tandem Mass Spectrometry." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1378197247.

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Delabrière, Alexis. "New approaches for processing and annotations of high-throughput metabolomic data obtained by mass spectrometry." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS359/document.

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La métabolomique est une approche de phénotypage présentant des perspectives prometteuses pour le diagnostic et le suivi de plusieurs pathologies. La technique d'observation la plus utilisée en métabolomique est la spectrométrie de masse (MS). Des développements technologiques récents ont considérablement accru la taille et la complexité des données. Cette thèse s'est concentrée sur deux verrous du traitement de ces données, l'extraction de pics des données brutes et l'annotation des spectres. La première partie de la thèse a porté sur le développement d'un nouvel algorithme de détection de pics pour des données d'analyse par injection en flot continue (Flow Injection Analysis ou FIA), une technique haut-débit. Un modèle dérivé de la physique de l'instrument de mesure prenant en compte la saturation de l'appareil a été proposé. Ce modèle inclut notamment un pic commun à tous les métabolites et un phénomène de saturation spécifique pour chaque ion. Ce modèle a permis de créer une workow qui estime ce pic commun sur des signaux peu bruités, puis l'utilise dans un filtre adapté sur tous les signaux. Son efficacité sur des données réelles a été étudiée et il a été montré que proFIA était supérieur aux algorithmes existants, avait une bonne reproductibilité et était très proche des mesures manuelles effectuées par un expert sur plusieurs types d'appareils. La seconde partie de cette thèse a porté sur le développement d'un outil de détection des similarités structurales d'un ensemble de spectre de fragmentation. Pour ce faire une nouvelle représentation sous forme de graphe a été proposée qui ne nécessite pas de connaître la composition atomique du métabolite. Ces graphes sont de plus une représentation naturelle des spectres MS/MS. Certaines propriétés de ces graphes ont ensuite permis de créer un algorithme efficace de détection des sous graphes fréquents (FSM) basé sur la génération d'arbres couvrants de graphes. Cet outil a été testé sur deux jeux de données différents et a prouvé sa vitesse et son interprétabilité comparé aux algorithmes de l'état de l'art. Ces deux algorithmes ont été implémentés dans des package R, proFIA et mineMS2 disponibles à la communauté
Metabolomics is a phenotyping approach with promising prospects for the diagnosis and monitoring of several diseases. The most widely used observation technique in metabolomics is mass spectrometry (MS). Recent technological developments have significantly increased the size and complexity of data. This thesis focused on two bottlenecks in the processing of these data, the extraction of peaks from raw data and the annotation of MS/MS spectra. The first part of the thesis focused on the development of a new peak detection algorithm for Flow Injection Analysis (FIA) data, a high-throughput metabolomics technique. A model derived from the physics of the mass spectrometer taking into account the saturation of the instrument has been proposed. This model includes a peak common to all metabolites and a specific saturation phenomenon for each ion. This model has made it possible to create a workflow that estimates the common peak on well-behaved signals, then uses it to perform matched filtration on all signals. Its effectiveness on real data has been studied and it has been shown that proFIA is superior to existing algorithms, has good reproducibility and is very close to manual measurements made by an expert on several types of devices. The second part of this thesis focused on the development of a tool for detecting the structural similarities of a set of fragmentation spectra. To do this, a new graphical representation has been proposed, which does not require the metabolite formula. The graphs are also a natural representation of MS/MS spectra. Some properties of these graphs have then made it possible to create an efficient algorithm for detecting frequent subgraphs (FSM) based on the generation of trees covering graphs. This tool has been tested on two different data sets and has proven its speed and interpretability compared to state-of-the-art algorithms. These two algorithms have been implemented in R, proFIA and mineMS2 packages available to the community
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Emanuele, Vincent A. II. "Advancements in high throughput protein profiling using surface enhanced laser desorption/ionization time of flight mass spectrometry." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37287.

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Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI)is one of several proteomics technologies that can be used in biomarker discovery studies. Such studies often have the goal of finding protein markers that predict early onset of cancers such as cervical cancer. The reproducibility of SELDI has been shown to be an issue in the literature. There are numerous sources of error in a SELDI experiment starting with sample collection from patients to the signal processing steps used to estimate the protein mass and abundance values present in a sample. This dissertation is concerned with all aspects of signal processing related to SELDI's use in biomarker discovery projects. In chapter 2, we perform a comprehensive study of the most popular preprocessing algorithms available. Next, in chapter 3, we study the basic statistics of SELDI data acquisition. From here, we propose a quadratic variance measurement model for buffer+matrix only spectra. This model leads us to develop a modified Antoniadis-Sapatinas wavelet denoising algorithm that demonstrates superior performance when compared to MassSpecWavelet, one of the leading techniques for preprocessing SELDI data. In chapter 4, we show that the quadratic variance model 1) extends to real pooled cervical mucus QC data from a clinical study, 2) predicts behavior and reproducibility of peak heights, and 3) finds four times as many reproducible peaks as the vendor-supplied preprocessing programs. The quadratic variance measurement model for SELDI data is fundamental and promises to lead to improved techniques for analyzing the data from clinical studies using this instrument.
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Song, Wei. "MASS SPECTROMETRY-BASED HIGH THROUGHPUT APPROACH FOR IDENTIFICATION OF MOLECULAR MODIFICATION OF OXIDATIVE PROCESS IN RESPIRATORY." Cleveland State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=csu1226685494.

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Books on the topic "High-throughput mass spectrometry"

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1960-, Lee Mike S., ed. Integrated strategies for drug discovery using mass spectrometry. Hoboken, N.J: Wiley-Interscience, 2005.

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Liu, Chang, and Hui Zhang. High-Throughput Mass Spectrometry in Drug Discovery. Wiley & Sons, Incorporated, John, 2022.

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Liu, Chang, and Hui Zhang. High-Throughput Mass Spectrometry in Drug Discovery. Wiley & Sons, Incorporated, John, 2022.

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Liu, Chang, and Hui Zhang. High-Throughput Mass Spectrometry in Drug Discovery. Wiley & Sons, Incorporated, John, 2022.

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Liu, Chang, and Hui Zhang. High-Throughput Mass Spectrometry in Drug Discovery. Wiley & Sons, Incorporated, John, 2022.

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Book chapters on the topic "High-throughput mass spectrometry"

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Zarini, Simona, Robert M. Barkley, Miguel A. Gijón, and Robert C. Murphy. "Overview of Lipid Mass Spectrometry and Lipidomics." In High-Throughput Metabolomics, 81–105. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_6.

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Paglia, Giuseppe, and Giuseppe Astarita. "Traveling Wave Ion Mobility Mass Spectrometry: Metabolomics Applications." In High-Throughput Metabolomics, 39–53. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_4.

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Nemkov, Travis, Julie A. Reisz, Sarah Gehrke, Kirk C. Hansen, and Angelo D’Alessandro. "High-Throughput Metabolomics: Isocratic and Gradient Mass Spectrometry-Based Methods." In High-Throughput Metabolomics, 13–26. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_2.

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Reinhold, Dominik, Harrison Pielke-Lombardo, Sean Jacobson, Debashis Ghosh, and Katerina Kechris. "Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data." In High-Throughput Metabolomics, 323–40. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_20.

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López-Gonzálvez, Ángeles, Joanna Godzien, Antonia García, and Coral Barbas. "Capillary Electrophoresis Mass Spectrometry as a Tool for Untargeted Metabolomics." In High-Throughput Metabolomics, 55–77. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_5.

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González-Domínguez, Raúl, Álvaro González-Domínguez, Carmen Segundo, Mónica Schwarz, Ana Sayago, Rosa María Mateos, Enrique Durán-Guerrero, Alfonso María Lechuga-Sancho, and Ángeles Fernández-Recamales. "High-Throughput Metabolomics Based on Direct Mass Spectrometry Analysis in Biomedical Research." In High-Throughput Metabolomics, 27–38. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_3.

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Rudd, Pauline M., Cristina Colominas, Louise Royle, Neil Murphy, Edmund Hart, Anthony H. Merry, Holger F. Heberstreit, and Raymond A. Dwek. "Glycoproteomics: High-Throughput Sequencing of Oligosaccharide Modifications to Proteins." In Proteome Research: Mass Spectrometry, 207–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56895-4_11.

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Reisz, Julie A., Connie Zheng, Angelo D’Alessandro, and Travis Nemkov. "Untargeted and Semi-targeted Lipid Analysis of Biological Samples Using Mass Spectrometry-Based Metabolomics." In High-Throughput Metabolomics, 121–35. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_8.

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Soni, Rajesh Kumar. "High-Throughput Plasma Proteomic Profiling." In Clinical Applications of Mass Spectrometry in Biomolecular Analysis, 411–20. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2565-1_36.

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Agrawal, Shubhra, Sahil Kumar, Raghav Sehgal, Sabu George, Rishabh Gupta, Surbhi Poddar, Abhishek Jha, and Swetabh Pathak. "El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics." In High-Throughput Metabolomics, 301–21. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9236-2_19.

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Conference papers on the topic "High-throughput mass spectrometry"

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Shuangshuang Jin, Atef Suleiman, Donald Daly, David Springer, and John Miller. "Pathway discovery by genome-wide, high-throughput, quantitative mass spectrometry." In 2008 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2008. http://dx.doi.org/10.1109/gensips.2008.4555654.

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Corso, Thomas N., Colleen K. Van Pelt, Sheng Zhang, Simon J. Prosser, and Gary A. Schultz. "Integrated microchip-based nanoelectrospray device for high-throughput mass spectrometry." In BiOS 2001 The International Symposium on Biomedical Optics, edited by Raymond P. Mariella, Jr. and Dan V. Nicolau. SPIE, 2001. http://dx.doi.org/10.1117/12.427960.

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Kumar, Sumesh, and Fahad Saeed. "Real-time peptide identification from high-throughput mass-spectrometry data." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459930.3470856.

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Aderogba, Samuel, J. Mark Meacham, F. Levent Degertekin, and Andrei G. Fedorov. "Micromachined Ultrasonic ElectroSpray Source Array for High Throughput Mass Spectrometry." In ASME 2004 3rd Integrated Nanosystems Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/nano2004-46086.

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According to the recent Laboratory News’ Proteomics Special article Mass Spectroscopy (MS) has become the technology of choice to meet today’s unprecedented demand for accurate bioanalytical measurements, including protein identification. Although MS can be used to analyze any biological sample, it must be first converted to gas-phase ions before it can be introduced into a mass spectrometer for analysis. It is transfer of a very small liquid sample (proteins are very expensive and often very difficult to produce in sizable quantities) into a gas-phase ions that is currently considered to be a bottleneck to high throughput proteomics. Electrospray ionization (ESI) is a technique developed in early 1990th to generate a spray gas-phase ions by applying high voltage (from several hundreds volts and up to a few thousands kilovolts relative to the ground electrode of the MS interface) to a small capillary through which the liquid solution is pumped. The high electric field ionizes the fluid forming the converging Taylor cone of the exiting jet which eventually breaks into many small droplets when the repulsive Coulombic forces overcome the surface tension. Because of the focusing effect associated with the spraying the electrically charged fluid, the size of the electrospray cone and thus of the formed droplets is in a few tens of nanometers range although the inner diameter of the capillary is in the micrometer range.
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WANG, PEI, HUA TANG, HEIDI ZHANG, JEFFREY WHITEAKER, AMANDA G. PAULOVICH, and MARTIN MCINTOSH. "NORMALIZATION REGARDING NON-RANDOM MISSING VALUES IN HIGH-THROUGHPUT MASS SPECTROMETRY DATA." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812701626_0029.

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Chen, C. H. Winston, Valeri V. Golovlev, N. I. Taranenko, S. L. Allman, Narayana R. Isola, N. T. Potter, K. J. Matteson, and Linus Y. Chang. "Laser desorption mass spectrometry for high-throughput DNA analysis and its applications." In BiOS '99 International Biomedical Optics Symposium, edited by Joseph R. Lakowicz, Steven A. Soper, and Richard B. Thompson. SPIE, 1999. http://dx.doi.org/10.1117/12.347532.

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Li, X., M. J. Brown, C. A. Smith, G. Cooper, X. Liu, A. Dossang, V. Pawate, A. Bridges, D. Holmes, and B. Leavens. "Picodroplet Mass Spectrometry for Miniaturized High Throughput Analysis of Synthetic Biology Microbial Clones." In IET/SynbiCITE Engineering Biology Conference. Institution of Engineering and Technology, 2016. http://dx.doi.org/10.1049/cp.2016.1249.

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Holcapek, Michal. "Potential of lipid class separation- mass spectrometry approaches for high-throughput lipidomic quantitation." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/djkv2622.

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The coupling of liquid-phase separation techniques and mass spectrometry (MS) is the prevailing analytical strategy in lipidomic analysis [1]. Two basic chromatographic approaches are available, lipid class separation and lipid species separation. The lipid class separation is represented by hydrophilic liquid chromatography (HILIC) and ultrahigh-performance supercritical fluid chromatography (UHPSFC) and offers advantages for high-throughput quantitation due to the coelution of lipid class internal standards and analytes from the same class [2]. Multiple lipid classes from phospholipid, sphingolipid, glycerolipid, fatty acyl, and sterol categories can be quantified in biological samples, but the main issue in lipidomic quantitation is the long-term stability of measured data [3]. The development of the LipidQuant 1.0 tool for automated processing of lipidomic data [4] was essential for high-throughput analysis of large clinical cohorts. The comprehensive MS determination of a wide range of blood lipids reveals statistically significant differences between various types of cancer patients and healthy controls visualized by multivariate data analysis, such as nonsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The most extensive results are obtained for pancreatic cancer [5] but similar patterns of dysregulation were observed for kidney, breast, and prostate cancers as well [6]. The most dysregulated lipids are very long chain monounsaturated sphingomyelins, ceramides, LPC 18:2, and some other phospholipids. The sensitivity and specificity to diagnose pancreatic cancer were more than 90%.References[1] Holčapek, M.; Liebisch, G.; Ekroos, K. Anal. Chem. 2018, 90, 4249-4257.[2] Wolrab, D.; Chocholoušková, M.; Jirásko, R.; Peterka, O.; Holčapek, M; Anal. Bioanal. Chem. 2020, 412, 2375.[3] Wolrab, D. et al., Anal. Chim. Acta 2020, 1137, 74-84.[4] Wolrab, D. et al., Bioinformatics 2021, 37, 4591-4592.[5] Wolrab, D. et al., Nat. Com. 2021, 13, 124.[6] Wolrab, D. et al., Sci. Rep. 2021, 11, 20322.
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Sannes-Lowery, Kristin A., Jared J. Drader, Richard H. Griffey, and Steven A. Hofstadler. "High-performance mass spectrometry as a drug discovery tool: a high-throughput screening assay to identify RNA-binding ligands." In BiOS 2001 The International Symposium on Biomedical Optics, edited by Ramesh Raghavachari and Weihong Tan. SPIE, 2001. http://dx.doi.org/10.1117/12.424586.

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Shah, Anuj R., Navdeep Jaitly, Nino Zuljevic, Matthew E. Monroe, Andrei Liyu, Ashoka D. Polpitiya, Joshua N. Adkins, et al. "An Architecture for Real Time Data Acquisition and Online Signal Processing for High Throughput Tandem Mass Spectrometry." In 2009 5th IEEE International Conference on e-Science (e-Science). IEEE, 2009. http://dx.doi.org/10.1109/e-science.2009.21.

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Reports on the topic "High-throughput mass spectrometry"

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Malik, Gunjan. Identification and Characterization of Prostate Cancer Associated Protein Biomarkers Using High-Throughput Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada462488.

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Wall, Andrew J., Rosemary C. Capo, Brian W. Stewart, Thai T. Phan, Jinesh C. Jain, Alexandra Hakala, and George D. Guthrie. High-Throughput Method for Strontium Isotope Analysis by Multi-Collector-Inductively Coupled Plasma-Mass Spectrometer. Office of Scientific and Technical Information (OSTI), September 2016. http://dx.doi.org/10.2172/1361494.

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Hakala, Jacqueline Alexandra. High-Throughput Method for Strontium Isotope Analysis by Multi-Collector-Inductively Coupled Plasma-Mass Spectrometer. Office of Scientific and Technical Information (OSTI), November 2016. http://dx.doi.org/10.2172/1337543.

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Altstein, Miriam, and Ronald Nachman. Rationally designed insect neuropeptide agonists and antagonists: application for the characterization of the pyrokinin/Pban mechanisms of action in insects. United States Department of Agriculture, October 2006. http://dx.doi.org/10.32747/2006.7587235.bard.

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The general objective of this BARD project focused on rationally designed insect neuropeptide (NP) agonists and antagonists, their application for the characterization of the mechanisms of action of the pyrokinin/PBAN (PK-PBAN) family and the development of biostable, bioavailable versions that can provide the basis for development of novel, environmentally-friendly pest insect control agents. The specific objectives of the study, as originally proposed, were to: (i) Test stimulatory potencies of rationally designed backbone cyclic (BBC) peptides on pheromonotropic, melanotropic, myotropic and pupariation activities; (ii) Test the inhibitory potencies of the BBC compounds on the above activities evoked either by synthetic peptides (PBAN, LPK, myotropin and pheromonotropin) or by the natural endogenous mechanism; (iii) Determine the bioavailability of the most potent BBC compounds that will be found in (ii); (iv) Design, synthesize and examine novel PK/PBAN analogs with enhanced bioavailability and receptor binding; (v) Design and synthesize ‘magic bullet’ analogs and examine their ability to selectively kill cells expressing the PK/PBAN receptor. To achieve these goals the agonistic and antagonistic activities/properties of rationally designed linear and BBC neuropeptide (NP) were thoroughly studied and the information obtained was further used for the design and synthesis of improved compounds toward the design of an insecticide prototype. The study revealed important information on the structure activity relationship (SAR) of agonistic/antagonistic peptides, including definitive identification of the orientation of the Pro residue as trans for agonist activity in 4 PK/PBANbioassays (pheromonotropic, pupariation, melanotropic, & hindgut contractile) and a PK-related CAP₂b bioassay (diuretic); indications that led to the identification of a novel scaffold to develop biostbiostable, bioavailable peptidomimetic PK/PBANagonists/antagonists. The work led to the development of an arsenal of PK/PBAN antagonists with a variety of selectivity profiles; whether between different PKbioassays, or within the same bioassay between different natural elicitors. Examples include selective and non-selective BBC and novel amphiphilic PK pheromonotropic and melanotropic antagonists some of which are capable of penetrating the moth cuticle in efficacious quantities. One of the latter analog group demonstrated unprecedented versatility in its ability to antagonize a broad spectrum of pheromonotropic elicitors. A novel, transPro mimetic motif was proposed & used to develop a strong, selective PK agonist of the melanotropic bioassay in moths. The first antagonist (pure) of PK-related CAP₂b diuresis in flies was developed using a cisPro mimetic motif; an indication that while a transPro orientation is associated with receptor agonism, a cisPro orientation is linked with an antagonist interaction. A novel, biostablePK analog, incorporating β-amino acids at key peptidase-susceptible sites, exhibited in vivo pheromonotropic activity that by far exceeded that of PBAN when applied topically. Direct analysis of neural tissue by state-of-the-art MALDI-TOF/TOF mass spectrometry was used to identify specific PK/PK-related peptides native to eight arthropod pest species [house (M. domestica), stable (S. calcitrans), horn (H. irritans) & flesh (N. bullata) flies; Southern cattle fever tick (B. microplus), European tick (I. ricinus), yellow fever mosquito (A. aegypti), & Southern Green Stink Bug (N. viridula)]; including the unprecedented identification of mass-identical Leu/Ile residues and the first identification of NPs from a tick or the CNS of Hemiptera. Evidence was obtained for the selection of Neb-PK-2 as the primary pupariation factor of the flesh fly (N. bullata) among native PK/PK-related candidates. The peptidomic techniques were also used to map the location of PK/PK-related NP in the nervous system of the model fly D. melanogaster. Knowledge of specific PK sequences can aid in the future design of species specific (or non-specific) NP agonists/antagonists. In addition, the study led to the first cloning of a PK/PBAN receptor from insect larvae (S. littoralis), providing the basis for SAR analysis for the future design of 2ⁿᵈgeneration selective and/or nonselective agonists/antagonists. Development of a microplate ligand binding assay using the PK/PBAN pheromone gland receptor was also carried out. The assay will enable screening, including high throughput, of various libraries (chemical, molecular & natural product) for the discovery of receptor specific agonists/antagonists. In summary, the body of work achieves several key milestones and brings us significantly closer to the development of novel, environmentally friendly pest insect management agents based on insect PK/PBANNPs capable of disrupting critical NP-regulated functions.
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