Dissertations / Theses on the topic 'Mass spectrometry data'
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Offei, Felix. "Denoising Tandem Mass Spectrometry Data." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3218.
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Protein identification using tandem mass spectrometry (MS/MS) has proven to be an effective way to identify proteins in a biological sample. An observed spectrum is constructed from the data produced by the tandem mass spectrometer. A protein can be identified if the observed spectrum aligns with the theoretical spectrum. However, data generated by the tandem mass spectrometer are affected by errors thus making protein identification challenging in the field of proteomics. Some of these errors include wrong calibration of the instrument, instrument distortion and noise. In this thesis, we present a pre-processing method, which focuses on the removal of noisy data with the hope of aiding in better identification of proteins. We employ the method of binning to reduce the number of noise peaks in the data without sacrificing the alignment of the observed spectrum with the theoretical spectrum. In some cases, the alignment of the two spectra improved.
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Ben-Farag, Suaad Omran S. "Statistical analysis of mass spectrometry data." Thesis, University of Leeds, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659026.
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The research described in this thesis can be broadly described by term "statistical analysis of mass spectrometry data". Bioinformatics is a new science which attempts to amalgamate statistical methodology with bring statistical thinking and the biological understanding to area which have previously been void of such. Mass spectrometry which is used to study proteins and their functions, is a relatively new field of bioinformatics research. In this thesis we explore three main themes, all of which tackle a different statistical learning method which arises in mass spectrometry. The main focus of the first theme of the research is on using statistical methods to study fragmentation patterns of mass spectrometry experiments. The analysis contained in this theme has been loosely split into parts: firstly, we calculate a probability of a process called cleavage as part of our preliminary analysis to determine which combination of fragmentation site residues were likely to break. In part two, we apply statistical models to investigate factors influencing the relative intensity of fragment ions formed in tandem mass spectrometry experiments. Separate models were formulated for different types of ions as it was thought that different factors may influence the formation of each type of fragment ion. Statistical regression methods are applied to two types of datasets of mass spectra data: tryptic and nontryptic peptide sequences. We find that several factors have a highly significant influence on the relative intensity of fragment ions formed in the experiment.
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Handley, Kelly. "Statistical analysis of proteomic mass spectrometry data." Thesis, University of Nottingham, 2007. http://eprints.nottingham.ac.uk/10287/.
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This thesis considers the statistical modelling and analysis of proteomic mass spectrometry data. Proteomics is a relatively new field of study and tried and tested methods of analysis do not yet exist. Mass spectrometry output is high-dimensional and so we firstly develop an algorithm to identify peaks in the spectra in order to reduce the dimensionality of the datasets. We use the results along with a variety of classification methods to examine the classification of new spectra based on a training set. Another method to reduce the complexity of the problem is to fit a parametric model to the data. We model the data as a mixture of Gaussian peaks with parameters representing the peak locations, heights and variances, and apply a Bayesian Markov chain Monte Carlo (MCMC) algorithm to obtain their estimates. These resulting estimates are used to identify m/z values where differences are apparent between groups, where the m/z value of an ion is its mass divided by its charge. A multilevel modelling framework is also considered to incorporate the structure in the data and locations exhibiting differences are again obtained. We consider two mass spectrometry datasets in detail. The first consists of mass spectra from breast cancer cells which either have or have not been treated with the chemotherapeutic agent Taxol. The second consists of mass spectra from melanoma cells classified as stage I or stage IV using the TNM system. Using the MCMC and multilevel techniques described above we show that, in both datasets, small subsets of the available m/z values can be identified which exhibit significant differences in protein expression between groups. Also we see that good classification of new data can also be achieved using a small number of m/z values and that the classification rate does not fall greatly when compared with results from the complete spectra. For both datasets we compare our results with those in the literature which use other techniques on the same data. We conclude by discussing potential areas for further research.
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Wandy, Joe. "Unsupervised Bayesian explorations of mass spectrometry data." Thesis, University of Glasgow, 2017. http://theses.gla.ac.uk/7928/.
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In recent years, the large-scale, untargeted studies of the compounds that serve as workers in the cell (proteins) and the small molecules involved in essential life-sustaining chemical processes (metabolites) have provided insights into a wide array of fields, such as medical diagnostics, drug discovery, personalised medicine and many others. Measurements in such studies are routinely performed using liquid chromatography mass spectrometry (LC-MS) instruments. From these measurements, we obtain a set of peaks having mass-to-charge, retention time (RT) and intensity values. Before further analysis is possible, the raw LC-MS data has to be processed in a data pre-preprocessing pipeline. In the alignment step of the pipeline, peaks from multiple LC-MS measurements have to be matched. In the identification step, the identity of unknown compounds in the sample that generate the observed peaks have to be assigned. Using tandem mass spectrometry, fragmentation peaks characteristic to a compound can be obtained and used to help establish the identity of the compound. Alignment and identification are challenging because the true identities of the entire set of compounds in the sample are unknown, and a single compound can produce many observed peaks, each with a potential drift in its retention time value. These observed peaks are not independent as they can be explained as being generated by the same compound. The aim of this thesis is to introduce methods to group these related peaks and to use these groupings to improve alignment and assist in identification during data pre-processing. Firstly, we introduce a generative model to group related peaks by their retention time. This information is used to influence direct-matching alignment, bringing related peak groups closer during matching. Investigations using benchmark datasets reveal that improved alignment performance is obtained from this approach. Next, we also consider mass information in the grouping process, resulting in PrecursorCluster, a model that performs the grouping of related peaks in metabolomics by their explainable mass relationships, RT and intensity values. Through a second-stage process that matches these related peak groups, peak alignment is produced. Experiments on benchmark datasets show that an improved alignment performance is obtained, while uncertainties in matched peaksets can also be extracted from the method. In the next section, we expand upon this two-stage method and introduce HDPAlign, a model that performs the clustering of related peaks within and across multiple LC-MS runs at once. This allows for matched peaksets and their respective uncertainties to be naturally extracted from the model. Finally, we look at fragmentation peaks used for identification and introduce MS2LDA, a topic model to group related fragmentation features. These groups of related fragmentation features potentially correspond to substructures shared by metabolites and can be used to assist data interpretation during identification. This final section corresponds to a work in progress and points to many interesting avenues for future research.
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Tengstrand, Erik. "Data analysis of non-targeted mass spectrometry experiments." Doctoral thesis, Stockholms universitet, Institutionen för miljövetenskap och analytisk kemi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-116820.
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Data processing tools are valuable to the analytical chemist as they can speed up the analysis, and sometimes solve problems that are not feasible to solve in a traditional manner. However, the complexity of many data processing tools can make their use daunting for the inexperienced user. This thesis includes two applications and two tools for data processing. The first application focuses on minimizing the manual input, reducing the time required for a simple task. The second application required more manual input, in the form of parameter selection, but process far more data. The data processing tools both include features that simplify the manual work required. The first by including visual diagnostics tools that helps in setting the parameters. The second via internal validation that makes the tool’s process more robust and reliable, and thereby less sensitive to small changes in the parameters. No matter how good or precise a data processing tool is, if it is so cumbersome that it is not used by the analytical chemists that need it, it is useless. Therefore, the main focus of this thesis is to make data processing easier.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.
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Lee, Wooram. "Protein Set for Normalization of Quantitative Mass Spectrometry Data." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/54554.
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Mass spectrometry has been recognized as a prominent analytical technique for peptide and protein identification and quantitation. With the advent of soft ionization methods, such as electrospray ionization and matrix assisted laser desorption/ionization, mass spectrometry has opened a new era for protein and proteome analysis. Due to its high-throughput and high-resolution character, along with the development of powerful data analysis software tools, mass spectrometry has become the most popular method for quantitative proteomics.
Stable isotope labeling and label-free quantitation methods are widely used in quantitative mass spectrometry experiments. Proteins with stable expression level and key roles in basic cellular functions such as actin, tubulin and glyceraldehyde-3-phosphate dehydrogenase, are frequently utilized as internal controls in biological experiments. However, recent studies have shown that the expression level of such commonly used housekeeping proteins is dependent on cell type, cell cycle or disease status, and that it can change as a result of a biochemical stimulation. Such phenomena can, therefore, substantially compromise the use of these proteins for data validation.
In this work, we propose a novel set of proteins for quantitative mass spectrometry that can be used either for data normalization or validation purposes. The protein set was generated from cell cycle experiments performed with MCF-7, an estrogen receptor positive breast cancer cell line, and MCF-10A, a non-tumorigenic immortalized breast cell line. The protein set was selected from a list of 3700 proteins identified in the different cellular sub-fractions and cell cycle stages of MCF-7/MCF-10A cells, based on the stability of spectral count data (CV<30 %) generated with an LTQ ion trap mass spectrometer. A total of 34 proteins qualified as endogenous standards for the nuclear, and 75 for the cytoplasmic cell fractions, respectively. The validation of these proteins was performed with a complementary, Her2+, SKBR-3 cell line. Based on the outcome of these experiments, it is anticipated that the proposed protein set will find applicability for data normalization/validation in a broader range of mechanistic biological studies that involve the use of cell lines.Master of Science
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NGUYEN, DAI HAI. "Machine Learning for Metabolite Identification with Mass Spectrometry Data." Kyoto University, 2020. http://hdl.handle.net/2433/259022.
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He, Ping. "Classification methods and applications to mass spectral data." HKBU Institutional Repository, 2005. http://repository.hkbu.edu.hk/etd_ra/593.
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Aiche, Stephan [Verfasser]. "Inferring Proteolytic Processes from Mass Spectrometry Time Series Data / Stephan Aiche." Berlin : Freie Universität Berlin, 2013. http://d-nb.info/1043480870/34.
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Bielow, Chris [Verfasser]. "Quantification and simulation of liquid chromatography-mass spectrometry data / Chris Bielow." Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1030382883/34.
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Brian, Carrillo. "Optimization of data directed acquisition in tandem mass spectrometry for proteomics." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80003.
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LC-QTOF tandem mass spectrometers behave according to user controlled switching parameters, duty-cycle and repetition rate, which guide the selection of peptides and the timing of their fragmentation. Using a novel algorithm which analyses all spectra simultaneously, it has been found that the majority of available peptides are not fragmented with the current switching scheme. Unfortunately, it is not practical to experiment with the mass spectrometer to determine optimal switching parameters. In this study, simulation coupled with intensity surface analysis was used as a method of evaluating mass spectrometer performance. Algorithms that mimic the mass spectrometer were created in order to simulate its response to various data sets. The simulations resulted in operating curves displaying the trade-off between quality and quantity of fragment spectra. The optimal operating curve demonstrated that the current switching scheme is sub-optimal, and that new switching parameters with fewer duty cycles and fewer repetitions should be selected.
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Hitchcock, Jonathan James. "Automated processing and analysis of gas chromatography/mass spectrometry screening data." Thesis, University of Bedfordshire, 2009. http://hdl.handle.net/10547/134940.
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The work presented is a substantial addition to the established methods of analysing the data generated by gas chromatography and low-resolution mass spectrometry. It has applications where these techniques are used on a large scale for screening complex mixtures, including urine samples for sports drug surveillance. The analysis of such data is usually automated to detect peaks in the chromatograms and to search a library of mass spectra of banned or unwanted substances. The mass spectra are usually not exactly the same as those in the library, so to avoid false negatives the search must report many doubtful matches. Nearly all the samples in this type of screening are actually negative, so the process of checking the results is tedious and time-consuming. A novel method, called scaled subtraction, takes each scan from the test sample and subtracts a mass spectrum taken from a second similar sample. The aim is that the signal from any substance common to the two samples will be eliminated. Provided that the second sample does not contain the specified substances, any which are present in the first sample can be more easily detected in the subtracted data. The spectrum being subtracted is automatically scaled to allow for compounds that are common to both samples but with different concentrations. Scaled subtraction is implemented as part of a systematic approach to preprocessing the data. This includes a new spectrum-based alignment method that is able to precisely adjust the retention times so that corresponding scans of the second sample can be chosen for the subtraction. This approach includes the selection of samples based on their chromatograms. For this, new measures of similarity or dissimilarity are defined. The thesis presents the theoretical foundation for such measures based on mass spectral similarity. A new type of difference plot can highlight significant differences. The approach has been tested, with the encouraging result that there are less than half as many false matches compared with when the library search is applied to the original data. True matches of compounds of interest are still reported by the library search of the subtracted data.
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Nagavaram, Ashish. "Cloud Based Dynamic Workflow with QOS For Mass Spectrometry Data Analysis." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322681210.
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Barton, Sheila Janet. "Statistical analysis of proteomic profile data generated by tandem mass spectrometry." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.536923.
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Kuschner, Karl W. "A Bayesian network approach to feature selection in mass spectrometry data." W&M ScholarWorks, 2009. https://scholarworks.wm.edu/etd/1539623543.
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One of the key goals of current cancer research is the identification of biologic molecules that allow non-invasive detection of existing cancers or cancer precursors. One way to begin this process of biomarker discovery is by using time-of-flight mass spectroscopy to identify proteins or other molecules in tissue or serum that correlate to certain cancers. However, there are many difficulties associated with the output of such experiments. The distribution of protein abundances in a population is unknown, the mass spectroscopy measurements have high variability, and high correlations between variables cause problems with popular methods of data mining. to mitigate these issues, Bayesian inductive methods, combined with non-model dependent information theory scoring, are used to find feature sets and build classifiers for mass spectroscopy data from blood serum Such methods show improvement over existing measures, and naturally incorporate measurement uncertainties. Resulting Bayesian network models are applied to three blood serum data sets: one artificially generated, one from a 2004 leukemia study, and another from a 2007 prostate cancer study. Feature sets obtained appear to show sufficient stability under cross-validation to provide not only biomarker candidates but also families of features for further biochemical analysis.
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Sniatynski, Matthew John. "Data analysis in proteomics novel computational strategies for modeling and interpreting complex mass spectrometry data." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1497.
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Contemporary proteomics studies require computational approaches to deal with both the complexity of the data generated, and with the volume of data produced. The amalgamation of mass spectrometry -- the analytical tool of choice in proteomics -- with the computational and statistical sciences is still recent, and several avenues of exploratory data analysis and statistical methodology remain relatively unexplored. The current study focuses on three broad analytical domains, and develops novel exploratory approaches and practical tools in each. Data transform approaches are the first explored. These methods re-frame data, allowing for the visualization and exploitation of features and trends that are not immediately evident. An exploratory approach making use of the correlation transform is developed, and is used to identify mass-shift signals in mass spectra. This approach is used to identify and map post-translational modifications on individual peptides, and to identify SILAC modification-containing spectra in a full-scale proteomic analysis. Secondly, matrix decomposition and projection approaches are explored; these use an eigen-decomposition to extract general trends from groups of related spectra. A data visualization approach is demonstrated using these techniques, capable of visualizing trends in large numbers of complex spectra, and a data compression and feature extraction technique is developed suitable for use in spectral modeling. Finally, a general machine learning approach is developed based on conditional random fields (CRFs). These models are capable of dealing with arbitrary sequence modeling tasks, similar to hidden Markov models (HMMs), but are far more robust to interdependent observational features, and do not require limiting independence assumptions to remain tractable. The theory behind this approach is developed, and a simple machine learning fragmentation model is developed to test the hypothesis that reproducible sequence-specific intensity ratios are present within the distribution of fragment ions originating from a common peptide bond breakage. After training, the model shows very good performance associating peptide sequences and fragment ion intensity information, lending strong support to the hypothesis.
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Song, Zhao Xu Dong. "Bioinformatics methods for protein identification using peptide mass fingerprinting data." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6125.
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Title from PDF of title page (University of Missouri--Columbia, viewed on Feb 16, 2010). The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Dissertation advisor: Dr. Dong Xu. Vita. Includes bibliographical references.
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Hornby, Sarah Elizabeth. "Characterisation of pyrolysis mass spectrometry for use in marine algal systematics." Thesis, University of Newcastle Upon Tyne, 2000. http://hdl.handle.net/10443/948.
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Pyrolysis mass spectrometry (PyMS) is a rapid, automated analytical technique that is used for chemical and biological characterisation of organisms. It has been limited in its use outside the discipline of microbiology and has rarely been applied to the analysis of multi-cellular organisms. This study aimed to investigate the potential of using PyMS as a routine analytical tool to resolve problems in marine algal systematics. The technical constraints of PyMS were also examined. The effect of sample concentration proved to be an important consideration for the production of meaningful results. PyMS analysis of macroalgae from the order Fucales demonstrated that this technique was robust to the influence of environmental variability and challenged the assertion that it is limited to use as a phenotypic technique only. Characterisation of samples was also possible at the sub-species level. Experimentally induced variation among cultures of the diatom Skeletonema costatum, including silicate limitation, low salinity and reduced irradiance, was detectable by PyMS. PyMS is subject to technical limitations including day to day variability among spectral data and does not produce a permanent classification. This study showed that PyMS is a highly discriminatory, sensitive technique that is capable of resolving chemical and biological variability among marine algae.
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Domingo, Almenara Xavier. "Automated mass spectrometry-based metabolomics data processing by blind source separation methods." Doctoral thesis, Universitat Rovira i Virgili, 2016. http://hdl.handle.net/10803/397799.
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Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A
més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes
caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar
estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes
existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu
d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en
metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a
resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser
desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir
automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i
adquirir nous coneixements biològics.Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A
més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes
caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar
estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes
existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu
d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en
metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a
resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser
desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir
automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i
adquirir nous coneixements biològics.Una de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.
One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights.
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Bäckström, Daniel. "Managing and Exploring Large Data Sets Generated by Liquid Separation - Mass Spectrometry." Doctoral thesis, Uppsala University, Analytical Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8223.
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A trend in natural science and especially in analytical chemistry is the increasing need for analysis of a large number of complex samples with low analyte concentrations. Biological samples (urine, blood, plasma, cerebral spinal fluid, tissue etc.) are often suitable for analysis with liquid separation mass spectrometry (LS-MS), resulting in two-way data tables (time vs. m/z). Such biological 'fingerprints' taken for all samples in a study correspond to a large amount of data. Detailed characterization requires a high sampling rate in combination with high mass resolution and wide mass range, which presents a challenge in data handling and exploration. This thesis describes methods for managing and exploring large data sets made up of such detailed 'fingerprints' (represented as data matrices).
The methods were implemented as scripts and functions in Matlab, a wide-spread environment for matrix manipulations. A single-file structure to hold the imported data facilitated both easy access and fast manipulation. Routines for baseline removal and noise reduction were intended to reduce the amount of data without loosing relevant information. A tool for visualizing and exploring single runs was also included. When comparing two or more 'fingerprints' they usually have to be aligned due to unintended shifts in analyte positions in time and m/z. A PCA-like multivariate method proved to be less sensitive to such shifts, and an ANOVA implementation made it easier to find systematic differences within the data sets.
The above strategies and methods were applied to complex samples such as plasma, protein digests, and urine. The field of application included urine profiling (paracetamole intake; beverage effects), peptide mapping (different digestion protocols) and search for potential biomarkers (appendicitis diagnosis) . The influence of the experimental factors was visualized by PCA score plots as well as clustering diagrams (dendrograms).
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Fredriksson, Mattias. "Efficient algorithms for highly automated evaluation of liquid chromatography - mass spectrometry data." Doctoral thesis, Mittuniversitetet, Institutionen för naturvetenskap, teknik och matematik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-12991.
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Liquid chromatography coupled to mass spectrometry (LC‐MS) has due to its superiorresolving capabilities become one of the most common analytical instruments fordetermining the constituents in an unknown sample. Each type of sample requires a specificset‐up of the instrument parameters, a procedure referred to as method development.During the requisite experiments, a huge amount of data is acquired which often need to bescrutinised in several different ways. This thesis elucidates data processing methods forhandling this type of data in an automated fashion.The properties of different commonly used digital filters were compared for LC‐MS datade‐noising, of which one was later selected as an essential data processing step during adeveloped peak detection step. Reconstructed data was further discriminated into clusterswith equal retention times into components by an adopted method. This enabled anunsupervised and accurate comparison and matching routine by which components fromthe same sample could be tracked during different chromatographic conditions.The results show that the characteristics of the noise have an impact on the performanceof the tested digital filters. Peak detection with the proposed method was robust to thetested noise and baseline variations but functioned optimally when the analytical peaks hada frequency band different from the uninformative parts of the signal. The algorithm couldeasily be tuned to handle adjacent peaks with lower resolution. It was possible to assignpeaks into components without typical rotational and intensity ambiguities associated tocommon curve resolution methods, which are an alternative approach. The underlyingfunctions for matching components between different experiments yielded satisfactoryresults. The methods have been tested on various experimental data with a high successrate.De analysinstrument som används för att ta reda på vad ett prov innehåller(och till vilken mängd) måste vanligtvis ställas in för det specifika fallet, för attfungera optimalt. Det finns ofta en mängd olika variabler att undersöka som harmer eller mindre inverkan på resultatet och när provet är okänt kan man oftast inteförutspå de optimala inställningarna i förtid.En vätskekromatograf med en masspektrometer som detektor är ett sådantinstrument som är utvecklat för att separera och identifiera organiska ämnen lösta ivätska. Med detta mycket potenta system kan man ofta med rätt inställningar delaupp de ingående ämnena i provet var för sig och samtidigt erhålla mått som kanrelateras till dess massa och mängd. Detta system används flitigt av analytiskalaboratorer inom bl.a. läkemedelsindustrin för att undersöka stabilitet och renhethos potentiella läkemedel. För att optimera instrumentet för det okända provetkrävs dock att en hel del försök utförs där inställningarna varieras. Syftet är attmed en mindre mängd designade försök bygga en modell som klarar av att peka åtvilket håll de optimala inställningarna finns. Data som genereras från instrumentetför denna typ av applikation är i matrisform då instrumentet scannar och spararintensiteten av ett intervall av massor varje tidpunkt en mätning sker. Om enanalyt når detektorn vid aktuell tidpunkt återges det som en eller flera överlagdanormalfördelade toppar som ett specifikt mönster på en annars oregelbundenbakgrundssignal. Förutom att alla topparna i det färdiga datasetet helst ska varavälseparerade och ha den rätta formen, så ska tiden analysen pågår vara så kortsom möjlig. Det är ändå inte ovanligt att ett färdigt dataset består av tiotalsmiljoner uppmätta intensiteter och att det kan krävas runt 10 försök med olikabetingelser för att åstadkomma ett godtagbart resultat.Dataseten kan dock till mycket stor del innehålla brus och andra störandesignaler vilket gör de extra krångligt att tolka och utvärdera. Eftersom man ävenofta får att komponenterna byter plats i ett dataset när betingelserna ändras kan enmanuell utvärdering ta mycket lång tid.Syftet med denna avhandling har varit att hitta metoder som kan vara till nyttaför den som snabbt och automatiskt behöver jämföra dataset analyserade medolika kromatografiska betingelser, men med samma prov. Det slutgiltiga målet harfrämst varit att identifiera hur olika komponenter i provet har rört sig mellan deolika dataseten, men de steg som ingår kan även nyttjas till andra applikationer.
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The, Matthew. "Statistical and machine learning methods to analyze large-scale mass spectrometry data." Licentiate thesis, KTH, Genteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-185149.
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As in many other fields, biology is faced with enormous amounts ofdata that contains valuable information that is yet to be extracted. The field of proteomics, the study of proteins, has the luxury of having large repositories containing data from tandem mass-spectrometry experiments, readily accessible for everyone who is interested. At the same time, there is still a lot to discover about proteins as the main actors in cell processes and cell signaling. In this thesis, we explore several methods to extract more information from the available data using methods from statistics and machine learning. In particular, we introduce MaRaCluster, a new method for clustering mass spectra on large-scale datasets. This method uses statistical methods to assess similarity between mass spectra, followed by the conservative complete-linkage clustering algorithm.The combination of these two resulted in up to 40% more peptide identifications on its consensus spectra compared to the state of the art method. Second, we attempt to clarify and promote protein-level false discovery rates (FDRs). Frequently, studies fail to report protein-level FDRs even though the proteins are actually the entities of interest. We provided a framework in which to discuss protein-level FDRs in a systematic manner to open up the discussion and take away potential hesitance. We also benchmarked some scalable protein inference methods and included the best one in the Percolator package. Furthermore, we added functionality to the Percolator package to accommodate the analysis of studies in which many runs are aggregated. This reduced the run time for a recent study regarding a draft human proteome from almost a full day to just 10 minutes on a commodity computer, resulting in a list of proteins together with their corresponding protein-level FDRs.QC 20160412
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Faull, Peter Allen. "Exploring gas-phase protein conformations by ion mobility-mass spectrometry." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3851.
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Analysis and characterisation of biomolecules using mass spectrometry has advanced over the past decade due to improvements in instrument design and capability; relevant use of complementary techniques; and available experimental and in silico data for comparison with cutting-edge research. This thesis presents ion mobility data, collected on an in-house modified QToF mass spectrometer (the MoQTOF), for a number of protein systems. Two haemoproteins, cytochrome c and haemoglobin, have been characterised and rotationally-averaged collision cross-sections for a number of multimeric species are presented. Intact multiply-charged multimers of the form [xCyt c + nH]z+ where x = 1 (monomer), x = 2 (dimer) and x = 3 (trimer) for cytochrome c have been elucidated and for species with x ≥ 2, reported for the first time. Fragment ions possibly attributed to a novel fragmentation mechanism, native electron capture dissociation, are reported with a brief discussion into their possible production from the dissociation of the gas-phase dimer species. Haemoglobin monomer globin subunits, dimers and intact tetramer have been successfully transferred to the gas phase, and their cross-sections elucidated. Comparisons with in silico computational data have been made and a discussion of the biologically-active tetramer association/dissociation technique is presented. Three further proteins have been studied and their gas-phase collision cross-sections calculated. Two regions of the large Factor H (fH) complement glycoprotein, fH 10-15 and fH 19-20, have been characterised for the first time by ion mobility-mass spectrometry. Much work using nuclear magnetic resonance spectroscopy has previously been achieved to produce structural information of these protein regions, however further biophysical characterisation using mass spectrometry may aid in greater understanding of the interactions these two specific regions have with other biomolecules. The DNA-binding core domain of the tumour suppressor p53 has been characterised and cross-sections produced in the presence and absence of the zinc metal ion that may control the domain’s biological activity. Within this core domain, p53 inactivation mutations have been shown to occur in up to 50% of human cancers, therefore the potential exists to further cancer-fighting activity through research on this region. Anterior Gradient-2 (AGR2) protein facilitates downregulation of p53 in an as yet unclear mechanism. Recent work using peptide aptamers has demonstrated that this downregulation can be disrupted and levels of p53 restored. Collision cross-sections for six peptide aptamers have been calculated, as well as cross-sections for multimers of AGR2 protein. A complex between one aptamer with the protein has also been elucidated. Use of the commercially available Synapt HDMS ion mobility-mass spectrometer at Waters MS Technologies Centre (Manchester, UK) allowed data to be collected for both Factor H protein regions and for the DNA-binding core domain of p53. Data are compared in the appropriate chapters with data collected using the MoQTOF.
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Kumar, Chanchal. "Bioinformatics methods and applications for functional analysis of mass spectrometry based proteomics data." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-124512.
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Ammar, Constantin [Verfasser], and Ralf [Akademischer Betreuer] Zimmer. "Context-based analysis of mass spectrometry proteomics data / Constantin Ammar ; Betreuer: Ralf Zimmer." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2020. http://d-nb.info/1221524488/34.
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Hauschild, Jennifer M. "Fourier transform ion cyclotron resonance mass spectrometry for petroleomics." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:8604a373-fb6b-4bc0-8dc1-464a191b1fac.
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The past two decades have witnessed tremendous advances in the field of high accuracy, high mass resolution data acquisition of complex samples such as crude oils and the human proteome. With the development of Fourier transform ion cyclotron resonance mass spectrometry, the rapidly growing field of petroleomics has emerged, whose goal is to process and analyse the large volumes of complex and often poorly understood data on crude oils generated by mass spectrometry. As global oil resources deplete, oil companies are increasingly moving towards the extraction and refining of the still plentiful reserves of heavy, carbon rich and highly contaminated crude oil. It is essential that the oil industry gather the maximum possible amount of information about the crude oil prior to setting up the drilling infrastructure, in order to reduce processing costs. This project describes how machine learning can be used as a novel way to extract critical information from complex mass spectra which will aid in the processing of crude oils. The thesis discusses the experimental methods involved in acquiring high accuracy mass spectral data for a large and key industry-standard set of crude oil samples. These data are subsequently analysed to identify possible links between the raw mass spectra and certain physical properties of the oils, such as pour point and sulphur content. Methods including artificial neural networks and self organising maps are described and the use of spectral clustering and pattern recognition to classify crude oils is investigated. The main focus of the research, the creation of an original simulated annealing genetic algorithm hybrid technique (SAGA), is discussed in detail and the successes of modelling a number of different datasets using all described methods are outlined. Despite the complexity of the underlying mass spectrometry data, which reflects the considerable chemical diversity of the samples themselves, the results show that physical properties can be modelled with varying degrees of success. When modelling pour point temperatures, the artificial neural network achieved an average prediction error of less than 10% while SAGA predicted the same values with an average accuracy of more than 85%. It did not prove possible to model any of the other properties with such statistical significance; however improvements to feature extraction and pre-processing of the spectral data as well as enhancement of the modelling techniques should yield more consistent and statistically reliable results. These should in due course lead to a comprehensive model which the oil industry can use to process crude oil data using rapid and cost effective analytical methods.
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Hellner, Joakim. "Introducing quality assessment and efficient management of cellular thermal shift assay mass spectrometry data." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-311792.
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Recent advances in molecular biology has led to the discovery of many new potential drugs. However, difficulties with in situ analysis of ligand binding prevents quick advancement in clinical trials, which stresses the need for better direct methods. A relatively new methodology, called Cellular Thermal Shift Assay (CETSA), allows for detection of ligand binding in a cells natural environment and can be used in combination with Mass Spectrometry (MS) for readout. With help from the Pelago Bioscience team, I developed a pipeline for processing of CETSA MS data and a web based system for viewing the results. The system, called CETSA Analytics, also evaluates the results relevance and helps its users to locate information efficiently. CETSA Analytics is currently being tested by Pelago Bioscience AB as a tool for experimental data distribution.
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Li, Fang Owens Kevin G. "Development of a genetic algorithm-correlation analysis (GA/CA) program for classification of chemical compounds using mass spectral data /." Philadelphia, Pa. : Drexel University, 2008. http://hdl.handle.net/1860/2803.
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Settelmeier, Jens. "Theoretical Fundamentals of Computational Proteomics and Deep Learning- Based Identification of Chimeric Mass Spectrometry Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-294322.
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A complicating factor for peptide identification by MS/MS experiments is the presence of “chimeric” spectra where at least two precursor ions with similar retention time and mass co- elute in the mass spectrometer. This results in a spectrum that is a superposition of the spectra of the individual peptides. These chimeric spectra make peptide identification more difficult, so chimeric detection tools are needed to improve peptide identification rates. GLEAMS is a learned embedding algorithm for efficient joint analysis of millions of mass spectra. In this work, we first simulate chimeric spectra. Then we present a deep neural network- based classifier that learns to distinguish between chimeras and pure spectra. The result shows that GLEAMS captures a spectrum’s chimericness, which can lead to a higher protein identification rate in samples or support biomarker development processes and the like.En komplicerande faktor för peptididentifiering genom MS / MS- experiment är närvaron av “chimära” spektra eller “chimera”, där åtminstone två föregångare med liknande retentionstid och massa sameluerar in i masspektrometern och resulterar i ett spektrum som är en superposition av individuella spektra. Eftersom dessa chimära spektra gör identifieringen av peptider mer utmanande behövs ett detekteringsverktyg för att förbättra identifieringsgraden för peptider. I detta arbete fokuserade vi på GLEAMS, en lärd inbäddning för effektiv gemensam analys av miljontals masspektrum. Först simulerade vi chimära spektra. Sedan presenterar vi en ensembleklassificering baserad på olika maskininlärnings- och djupinlärningsmetoder som lär sig att skilja på simulerad chimera och rena spektra. Resultatet visar att GLEAM fångar “chimärheten” i ett spektrum, vilket kan leda till högre identifieringsgrad av protein samt ge stöd till utvecklingsprocesser för biomarkörer.
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Meier, Florian [Verfasser], and Matthias [Akademischer Betreuer] Mann. "Data acquisition methods for next-generation mass spectrometry-based proteomics / Florian Meier ; Betreuer: Matthias Mann." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2018. http://d-nb.info/1175381322/34.
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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 communautéMetabolomics 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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Wang, Minkun. "Topic Model-based Mass Spectrometric Data Analysis in Cancer Biomarker Discovery Studies." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78201.
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Identification of disease-related alterations in molecular and cellular mechanisms may reveal useful biomarkers for human diseases including cancers. High-throughput omic technologies for identifying and quantifying multi-level biological molecules (e.g., proteins, glycans, and metabolites) have facilitated the advances in biological research in recent years. Liquid (or gas) chromatography coupled with mass spectrometry (LC/GC-MS) has become an essential tool in such large-scale omic studies. Appropriate LC/GC-MS data preprocessing pipelines are needed to detect true differences between biological groups. Challenges exist in several aspects of MS data analysis. Specifically for biomarker discovery, one fundamental challenge in quantitation of biomolecules is owing to the heterogeneous nature of human biospecimens. Although this issue has been a subject of discussion in cancer genomic studies, it has not yet been rigorously investigated in mass spectrometry based omic studies. Purification of mass spectometric data is highly desired prior to subsequent differential analysis.
In this research dissertation, we majorly target at addressing the purification problem through probabilistic modeling. We propose an intensity-level purification model (IPM) to computationally purify LC/GC-MS based cancerous data in biomarker discovery studies. We further extend IPM to scan-level purification model (SPM) by considering information from extracted ion chromatogram (EIC, scan-level feature). Both IPM and SPM belong to the category of topic modeling approach, which aims to identify the underlying "topics" (sources) and their mixture proportions in composing the heterogeneous data. Additionally, denoise deconvolution model (DMM) is proposed to capture the noise signals in samples based on purified profiles. Variational expectation-maximization (VEM) and Markov chain Monte Carlo (MCMC) methods are used to draw inference on the latent variables and estimate the model parameters. Before we come to purification, other research topics in related to mass spectrometric data analysis for cancer biomarker discovery are also investigated in this dissertation.
Chapter 3 discusses the developed methods in the differential analysis of LC/GC-MS based omic data, specifically for the preprocessing in data of LC-MS profiled glycans. Chapter 4 presents the assumptions and inference details of IPM, SPM, and DDM. A latent Dirichlet allocation (LDA) core is used to model the heterogeneous cancerous data as mixtures of topics consisting of sample-specific pure cancerous source and non-cancerous contaminants. We evaluated the capability of the proposed models in capturing mixture proportions of contaminants and cancer profiles on LC-MS based serum and tissue proteomic and GC-MS based tissue metabolomic datasets acquired from patients with hepatocellular carcinoma (HCC) and liver cirrhosis. Chapter 5 elaborates these applications in cancer biomarker discovery, where typical single omic and integrative analysis of multi-omic studies are included.Ph. D.
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Conrad, Tim [Verfasser]. "New statistical algorithms for the analysis of mass spectrometry time-of-flight mass data with applications in clinical diagnostics / Tim Conrad." Berlin : Freie Universität Berlin, 2008. http://d-nb.info/1023262851/34.
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Xu, Hua. "Novel data analysis methods and algorithms for identification of peptides and proteins by use of tandem mass spectrometry." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1187113396.
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Tanaka, Yuuki. "Deciphering the physiological codes of bone using elemental and isotopic data obtained by ICP-mass spectrometry." Kyoto University, 2017. http://hdl.handle.net/2433/228224.
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Lee, Joanna L. S. "Time-of-flight secondary ion mass spectrometry - fundamental issues for quantitative measurements and multivariate data analysis." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:f0e4b8ff-f563-429e-9e71-9c277a5139c4.
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Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful technique for the analysis of organic surfaces and interfaces for many innovative technologies. However, despite recent developments, there are still many issues and challenges hindering the robust, validated use of ToF-SIMS for quantitative measurement. These include: the lack of metrology and fundamental understanding for the use of novel cluster primary ion beams such as C60n+ and Ar2000+; the need for validated and robust measurement protocols for difficult samples, such as those with significant micron scale surface topography; the lack of guidance on novel data analysis methods including multivariate analysis which have the potential to simplify many time-consuming and intensive analyses in industry; and the need to establish best practice to improve the accuracy of measurements. This thesis describes research undertaken to address the above challenges. Sample topography and field effects were evaluated experimentally using model conducting and insulating fibres and compared with computer simulations to provide recommendation to diagnose and reduce the effects. Two popular multivariate methods, principal component analysis (PCA) and multivariate curve resolution (MCR), were explored using mixed organic systems consisting of a simple polymer blend and complex hair fibres treated with a multi-component formulation to evaluate different multivariate and data preprocessing methods for the optimal identification, localisation and quantification of the chemical components. Finally, cluster ion beams C60n+ and Ar500-2500+ were evaluated on an inorganic surface and an organic delta layer reference material respectively to elucidate the fundamental metrology of cluster ion sputtering and pave the way for their use in organic depth profiling. These studies provide the essential metrological foundation to address frontier issues in surface and nanoanalysis and extend the measurement capabilities of ToF-SIMS.
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Lancashire, Lee James. "Multi-layer perceptron artificial neural network predictive modelling of genomic and mass spectrometry data in bioinformatics." Thesis, Nottingham Trent University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442340.
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Momo, Remi Ako-Mbianyor. "MALDI-ToF mass spectrometry biomarker profiling via multivariate data analysis application in the biopharmaceutical bioprocessing industry." Thesis, University of Newcastle upon Tyne, 2013. http://hdl.handle.net/10443/1939.
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Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF MS) is a technique by which protein profiles can be rapidly produced from biological samples. Proteomic profiling and biomarker identification using MALDI-ToF MS have been utilised widely in microbiology for bacteria identification and in clinical proteomics for disease-related biomarker discovery. To date, the benefits of MALDI-ToF MS have not been realised in the area of mammalian cell culture during bioprocessing. This thesis explores the approach of ‘intact-cell’ MALDI-ToF MS (ICM-MS) combined with projection to latent structures – discriminant analysis (PLS-DA), to discriminate between mammalian cell lines during bioprocessing. Specifically, the industrial collaborator, Lonza Biologics is interested in adopting this approach to discriminate between IgG monoclonal antibody producing Chinese hamster ovaries (CHO) cell lines based on their productivities and identify protein biomarkers which are associated with the cell line productivities. After classifying cell lines into two categories (high/low producers; Hs/Ls), it is hypothesised that Hs and Ls CHO cells exhibit different metabolic profiles and hence differences in phenotypic expression patterns will be observed. The protein expression patterns correlate to the productivities of the cell lines, and introduce between-class variability. The chemometric method of PLS-DA can use this variability to classify the cell lines as Hs or Ls. A number of differentially expressed proteins were matched and identified as biomarkers after a SwissProt/TrEMBL protein database search. The identified proteins revealed that proteins involved in biological processes such as protein biosynthesis, protein folding, glycolysis and cytoskeleton architecture were upregulated in Hs. This study demonstrates that ICM-MS combined with PLS-DA and a protein database search can be a rapid and valuable tool for biomarker discovery in the bioprocessing industry. It may help in providing clues to potential cell genetic engineering targets as well as a tool in process development in the bioprocessing industry. With the completion of the sequencing of the CHO genome, this study provides a foundation for rapid biomarker profiling of CHO cell lines in culture during recombinant protein manufacturing.
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Green, Christopher Lee. "IP Algorithm Applied to Proteomics Data." Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd618.pdf.
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Harper, Robert T. "Determination of the proton affinities of gas phase peptides by mass spectrometry and computational chemistry." Scholarly Commons, 2007. https://scholarlycommons.pacific.edu/uop_etds/673.
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Helices in proteins have substantial permanent dipole moments arising from the nearly perfect alignment of the individual dipole moments of each peptide bond. Interaction with this helix "macrodipole" is thought to perturb the pKa values of basic or acidic residues at the helix termini. The goal of this project is to investigate the effect of the helix confonnation on the proton affinities ofbasic amino acids placed at theN- or Ctenninus of helical model peptides in the gas phase. Several series of model peptides having a basic residue, lysine (K) or 2,3- diaminopropionic acid (Dap ), located at either terminus were synthesized by solid phase peptide synthesis using conventional techniques or the amino acid fluoride approach. Proton affinities were determined for several basic amino acids and peptides using mass spectrometry by applying the extended Cooks' kinetic method. Favorable conformations and theoretical proton affinities were probed using computational chemistry. The proton affinities determined for Na-acetyl-(L)-lysine, Ac-AK, Ac-KA, and Ac-KAA are 236.8 ± 1.9 kcal mol-1 , 249.4 ± 2.0 kcal mol-1 , 241.5 ± 1.9 kcal mol-1 , and 244.4 ± 2.0 kcal mol-1 respectively. The large negative entropy changes for each of the peptides upon protonation ( -11.2 to - 21.7 cal mol-1 K- 1 ) are consistent with globular confmmations adopted by the protonated peptides due to extensive intramolecular hydrogen bonding. The measured proton affinities of the peptides increased with the size of the peptide as expected. However, the measured proton affinity of the peptide with C-terminal lysine, Ac-AK, is substantially higher than that of the con·esponding peptide with N-terrninal lysine, Ac-KA, contrary to expectations. Proton affinities determined for these compounds using computational chemistry are in reasonable agreement with experimental results. Additionally, proton affinities calculated for helical polyalanine and Aib (aaminoisobutytic acid) modified polyalanine peptides with C-terminal basic residues (Ac AnK and Ac-(AibA)n-Dap) are much larger than proton affinities calculated for the corresponding peptides with N-terminal basic residues. These results indicate that the helix dipole has a substantial effect on the basicity of residues at the helix termini.
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Taylor, John A. "Development of automated methods for analysis of mass spectrometric data and characterization of an active proteolytic fragment of CD45 /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9233.
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Treutler, Hendrik [Verfasser]. "Bioinformatics tools for mass spectrometry, phylogenetic footprinting, and the integration of biological data : [kumulative Dissertation] / Hendrik Treutler." Halle, 2017. http://d-nb.info/1153401967/34.
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Mueller, Michael. "Integrated analysis of proteomics data to assess and improve the scope of mass spectrometry based genome annotation." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611790.
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Hope, Janiece L. "Comprehensive gas chromatography with chemometric data analysis for pattern recognition and signal deconvolution of complex samples /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/8542.
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Veit, Johannes [Verfasser], and Oliver [Akademischer Betreuer] Kohlbacher. "Efficient Workflows for Analyzing High-Performance Liquid Chromatography Mass Spectrometry-Based Proteomics Data / Johannes Veit ; Betreuer: Oliver Kohlbacher." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1193489288/34.
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Yang, Li. "Functionalization, characterization, and applications of diamond particles, modification of planar silicon, and chemometrics analysis of mass spectrometry data /." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd2855.pdf.
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Moberg, My. "Liquid Chromatography Coupled to Mass Spectrometry : Implementation of Chemometric Optimization and Selected Applications." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7071.
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Zhou, Bin. "Computational Analysis of LC-MS/MS Data for Metabolite Identification." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/36109.
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Metabolomics aims at the detection and quantitation of metabolites within a biological system. As the most direct representation of phenotypic changes, metabolomics is an important component in system biology research. Recent development on high-resolution, high-accuracy mass spectrometers enables the simultaneous study of hundreds or even thousands of metabolites in one experiment. Liquid chromatography-mass spectrometry (LC-MS) is a commonly used instrument for metabolomic studies due to its high sensitivity and broad coverage of metabolome.
However, the identification of metabolites remains a bottle-neck for current metabolomic studies. This thesis focuses on utilizing computational approaches to improve the accuracy and efficiency for metabolite identification in LC-MS/MS-based metabolomic studies. First, an outlier screening approach is developed to identify those LC-MS runs with low analytical quality, so they will not adversely affect the identification of metabolites. The approach is computationally simple but effective, and does not depend on any preprocessing approach. Second, an integrated computational framework is proposed and implemented to improve the accuracy of metabolite identification and prioritize the multiple putative identifications of one peak in LC-MS data. Through the framework, peaks are likely to have the m/z values that can give appropriate putative identifications. And important guidance for the metabolite verification is provided by prioritizing the putative identifications. Third, an MS/MS
spectral matching algorithm is proposed based on support vector machine classification. The approach provides an improved retrieval performance in spectral matching, especially in the presence of data heterogeneity due to different instruments or experimental settings used during the MS/MS spectra acquisition.Master of Science
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Christison, Krege Matthew. "Exploring the Molecular Origin of Jet Fuel Thermal Oxidative Deposition Through Statistical Analysis of Mass Spectral Data and Pyrolysis Gas Chromatography/Mass Spectrometry of Deposits." Scholarly Commons, 2019. https://scholarlycommons.pacific.edu/uop_etds/3639.
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ASTM D3241 (Standard Test Method for Thermal Oxidation Stability of Aviation Turbine Fuels) measures the thermal oxidative stability of jet fuels under elevated temperature and pressure conditions. When jet fuels fail ASTM D3241, either at the refinery or in the distribution system, there can be supply disruptions and financial losses. Understanding the causes of poor thermal oxidative stability in jet fuels could help prevent or mitigate issues. In order to develop a deeper understanding of the molecular precursors that lead to ASTM D3241 failures, a number of analytical methodologies and data treatment techniques have been developed, applied, and reported here. Statistical analysis of LC/MS ESI data from jet fuels with varying thermal oxidative stabilities allows for the identification of molecules that are significant to ASTM D3241 failures. Differential statistical analysis of LC/MS ESI data from jet fuels before and after thermal oxidative stressing in a QCM reactor elucidates which significant molecules are being consumed during oxidation and which molecules are increasing in abundance. The analysis of thermal oxidative deposits that form during thermal oxidative stressing in the QCM reactor allows for the insight into the molecular components of the deposits. Attapulgus clay removes the polar molecules that lead to thermal oxidative stability issues in the refinery. Extraction of Attapulgus clay that has been used in a refinery to filter jet fuel with a series of solvents removes the polar molecules into a series of fractions. The subsequent analysis of the fractions by comprehensive GCxGC/MS leads to the identification of the different homologous series of molecules that are removed by the clay.
The analyses developed and employed here are shown to be particularly useful for the analysis of trace polar nitrogen and oxygen containing molecules. Similar homologous series of molecules are identified across all of the different analyses. It is also clear from some of the analyses, along with previously reported data in the literature, that reactive sulfur-containing molecules are significant to poor thermal oxidative stability as measured by ASTM D3241 and to the formation of thermal oxidative deposits. There is still an opportunity to find methodologies to better characterize the sulfur species present and correlate them to the data that is reported here.
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Rabe, Jan-Hinrich [Verfasser], and Carsten [Akademischer Betreuer] Hopf. "Multimodal FTIR Microscopy-guided Acquisition and Interpretation of MALDI Mass Spectrometry Imaging Data / Jan-Hinrich Rabe ; Betreuer: Carsten Hopf." Heidelberg : Universitätsbibliothek Heidelberg, 2018. http://d-nb.info/1177385341/34.
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