Gotowe bibliografie tematyczne / Proteomics

Gotowa bibliografia na temat „Proteomics”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 7 lipca 2024

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Artykuły w czasopismach na temat "Proteomics"

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Krieg, Rene C., Cloud P. Paweletz, Lance A. Liotta i Emanuel F. Petricoin. "Clinical Proteomics for Cancer Biomarker Discovery and Therapeutic Targeting". Technology in Cancer Research & Treatment 1, nr 4 (sierpień 2002): 263–72. http://dx.doi.org/10.1177/153303460200100407.

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As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the “software” of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the “wiring diagram” of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.
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Sukumaran, Pariveena, Ainun Aida Bahardin, Luqmanul Hakim Abdul Razak i Mohd Harizal Senik. "Application of Proteomics in Alzheimer’s Disease: A Mini Review". SEPTEMBER 2023 19, nr 5 (11.09.2023): 317–30. http://dx.doi.org/10.47836/mjmhs.19.5.38.

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Alzheimer’s disease (AD) is classified as one of neurodegenerative disease caused by neuronal death. It is characterized as memory impairment, including the inability to produce new memories. Since AD has low treatment effectiveness, proteomics research opens possibilities for advancement. Proteomics is the study of proteomes produced by the disease-bearing host to identify and understand diseases. In this case, to investigate the use of protein as a reliable molecular entity and their involvement in AD. Therefore, this review focused on three main applications of proteomics; the potential use of proteomics as a diagnostic tool for AD, the use of proteomics to assess the treatment progression of AD and the advancement in AD research. The review discussed three research areas utilizing the proteomics approach: ageing, behavioural, and demographic research of AD populations. Proteomic approaches have also been shown to be effective to discover the biomarkers for infectious diseases, cancers, heart diseases, and neurological disorders. Although much work remained to be done, the proteomics approach is an interesting method to be carried out in detecting AD at an earlier stage and will be very useful for AD treatment and management in the future.
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Mahajan, R., i P. Gupta. "Proteomics: taking over where genomics leaves off". Czech Journal of Genetics and Plant Breeding 46, No. 2 (29.06.2010): 47–53. http://dx.doi.org/10.17221/34/2009-cjgpb.

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The proteomic studies are simultaneously developed in several directions and significantly influence our notions on the capabilities of biological sciences. The need for proteomics research is necessary as there are certain genes in a cell that encode proteins with specific functions. Using a variety of techniques, proteomics can be used to study how proteins interact within a system or how the protein expression changes in different parts of the body, in different stages of its life cycle and in different environmental conditions as every individual has one genome and many proteomes. Besides the qualitative and quantitative description of the expressed proteins, proteomics also deals with the analysis of mutual interactions of proteins. Thereby, candidate proteins can be identified which may be used as starting-points for diagnostic or even therapeutic approaches.
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Sokolowska, Izabela, Armand G. Ngounou Wetie, Alisa G. Woods i Costel C. Darie. "Applications of Mass Spectrometry in Proteomics". Australian Journal of Chemistry 66, nr 7 (2013): 721. http://dx.doi.org/10.1071/ch13137.

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Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).
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Sadeesh, Nithin, Mauro Scaravilli i Leena Latonen. "Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes". Cancers 13, nr 19 (27.09.2021): 4829. http://dx.doi.org/10.3390/cancers13194829.

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Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.
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Walther, Tobias C., i Matthias Mann. "Mass spectrometry–based proteomics in cell biology". Journal of Cell Biology 190, nr 4 (23.08.2010): 491–500. http://dx.doi.org/10.1083/jcb.201004052.

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The global analysis of protein composition, modifications, and dynamics are important goals in cell biology. Mass spectrometry (MS)–based proteomics has matured into an attractive technology for this purpose. Particularly, high resolution MS methods have been extremely successful for quantitative analysis of cellular and organellar proteomes. Rapid advances in all areas of the proteomic workflow, including sample preparation, MS, and computational analysis, should make the technology more easily available to a broad community and turn it into a staple methodology for cell biologists.
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Duong, Van-An, i Hookeun Lee. "Bottom-Up Proteomics: Advancements in Sample Preparation". International Journal of Molecular Sciences 24, nr 6 (10.03.2023): 5350. http://dx.doi.org/10.3390/ijms24065350.

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Liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based proteomics is a powerful technique for profiling proteomes of cells, tissues, and body fluids. Typical bottom-up proteomic workflows consist of the following three major steps: sample preparation, LC–MS/MS analysis, and data analysis. LC–MS/MS and data analysis techniques have been intensively developed, whereas sample preparation, a laborious process, remains a difficult task and the main challenge in different applications. Sample preparation is a crucial stage that affects the overall efficiency of a proteomic study; however, it is prone to errors and has low reproducibility and throughput. In-solution digestion and filter-aided sample preparation are the typical and widely used methods. In the past decade, novel methods to improve and facilitate the entire sample preparation process or integrate sample preparation and fractionation have been reported to reduce time, increase throughput, and improve reproducibility. In this review, we have outlined the current methods used for sample preparation in proteomics, including on-membrane digestion, bead-based digestion, immobilized enzymatic digestion, and suspension trapping. Additionally, we have summarized and discussed current devices and methods for integrating different steps of sample preparation and peptide fractionation.
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Oikonomou, Panos, Roberto Salatino i Saeed Tavazoie. "In vivo mRNA display enables large-scale proteomics by next generation sequencing". Proceedings of the National Academy of Sciences 117, nr 43 (9.10.2020): 26710–18. http://dx.doi.org/10.1073/pnas.2002650117.

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Large-scale proteomic methods are essential for the functional characterization of proteins in their native cellular context. However, proteomics has lagged far behind genomic approaches in scalability, standardization, and cost. Here, we introduce in vivo mRNA display, a technology that converts a variety of proteomics applications into a DNA sequencing problem. In vivo-expressed proteins are coupled with their encoding messenger RNAs (mRNAs) via a high-affinity stem-loop RNA binding domain interaction, enabling high-throughput identification of proteins with high sensitivity and specificity by next generation DNA sequencing. We have generated a high-coverage in vivo mRNA display library of the Saccharomyces cerevisiae proteome and demonstrated its potential for characterizing subcellular localization and interactions of proteins expressed in their native cellular context. In vivo mRNA display libraries promise to circumvent the limitations of mass spectrometry-based proteomics and leverage the exponentially improving cost and throughput of DNA sequencing to systematically characterize native functional proteomes.
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Stubbs, Keith A., i David J. Vocadlo. "Affinity-Based Proteomics Probes; Tools for Studying Carbohydrate-Processing Enzymes". Australian Journal of Chemistry 62, nr 6 (2009): 521. http://dx.doi.org/10.1071/ch09140.

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As more information becomes available through the efforts of high-throughput screens, there is increasing pressure on the three main ‘omic’ fields, genomics, proteomics, and metabolomics, to organize this material into useful libraries that enable further understanding of biological systems. Proteomics especially is faced with two highly challenging tasks. The first is assigning the activity of thousands of putative proteins, the existence of which has been suggested by genomics studies. The second is to serve as a link between genomics and metabolomics by demonstrating which enzymes play roles in specific metabolic pathways. Underscoring these challenges in one area are the thousands of putative carbohydrate-processing enzymes that have been bioinformatically identified, mostly in prokaryotes, but that have unknown or unverified activities. Using two brief examples, we illustrate how biochemical pathways within bacteria that involve carbohydrate-processing enzymes present interesting potential antimicrobial targets, offering a clear motivation for gaining a functional understanding of biological proteomes. One method for studying proteomes that has been developed recently is to use synthetic compounds termed activity-based proteomics probes. Activity-based proteomic profiling using such probes facilitates rapid identification of enzyme activities within proteomes and assignment of function to putative enzymes. Here we discuss the general design principles for these probes with particular reference to carbohydrate-processing enzymes and give an example of using such a probe for the profiling of a bacterial proteome.
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Masood, Afshan, Hicham Benabdelkamel i Assim Alfadda. "Obesity Proteomics: An Update on the Strategies and Tools Employed in the Study of Human Obesity". High-Throughput 7, nr 3 (12.09.2018): 27. http://dx.doi.org/10.3390/ht7030027.

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Proteomics has become one of the most important disciplines for characterizing cellular protein composition, building functional linkages between protein molecules, and providing insight into the mechanisms of biological processes in a high-throughput manner. Mass spectrometry-based proteomic advances have made it possible to study human diseases, including obesity, through the identification and biochemical characterization of alterations in proteins that are associated with it and its comorbidities. A sizeable number of proteomic studies have used the combination of large-scale separation techniques, such as high-resolution two-dimensional gel electrophoresis or liquid chromatography in combination with mass spectrometry, for high-throughput protein identification. These studies have applied proteomics to comprehensive biochemical profiling and comparison studies while using different tissues and biological fluids from patients to demonstrate the physiological or pathological adaptations within their proteomes. Further investigations into these proteome-wide alterations will enable us to not only understand the disease pathophysiology, but also to determine signature proteins that can serve as biomarkers for obesity and related diseases. This review examines the different proteomic techniques used to study human obesity and discusses its successful applications along with its technical limitations.
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Rozprawy doktorskie na temat "Proteomics"

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Miller, V. F. "Neuroretinal proteomics". Thesis, Queen's University Belfast, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.411748.

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Franchin, Cinzia. "Mass Spectrometry-Based Quantitative Proteomics to Study Proteomes, Phosphoproteomes and Interactomes". Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422169.

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Over the past few years, mass spectrometry-based proteomics has been widely applied to the most diverse fields of biochemistry, biomedicine and biology, and several approaches have been developed to allow absolute and relative quantification of proteins in very complex mixtures. During my PhD, I have conducted three main studies, taking advantage of different quantitative proteomics techniques. In particular SILAC and iTRAQ approaches have been exploited to investigate the calcification process induced by endotoxin in clonal interstitial aortic valve cells, while SILAC and label-free quantitative approaches have been exploited to identify new potential interacting partners and substrates of the protein kinase CK2. Calcific aortic valve disease represents the most common type of valvular disease and the first cause of surgical valve replacement in the industrialized world. No medical therapies are available to prevent or slow down calcium deposition within the valve leaflets, therefore surgery is the only possible treatment. To investigate the molecular mechanisms underlying the calcification process, SILAC and iTRAQ proteomics approaches have been applied to bovine interstitial aortic valve cells, a cellular model that is able to acquire a pro-calcific profile and drive matrix mineralization upon treatment with an inflammatory stimulus like the endotoxin lipopolysaccharide (LPS). The application to the same cellular model of two different quantitative technologies, led to the identification and relative quantification of hundreds of proteins, among which many showed a significant alteration in response to LPS. The acquired data suggest that cellular oxidoreductase activity, cytoskeletal and spliceosome regulation, glycolisis/gluconeogenesis, and arginine metabolism are altered during the acquisition of the pro-calcific profile. These results represent a starting point to investigate more in detail the molecular mechanisms that seem to be strongly involved in the calcification process induced by LPS. The other projects described in this thesis focus on CK2, an essential, constitutively active and highly pleiotropic protein kinase. CK2, like many other kinases, is strongly involved in several cellular processes, and in particular it has been hypothesized that this enzyme plays a crucial role in the transduction of survival signals. However, a clear comprehension of the multiple roles played by this kinase within the cell has not been achieved. The aim of these projects was the identification of interacting partners and substrates of CK2 by means of proteomics approaches to try to shed some light on the functions performed by this kinase. In particular a combination of immunoprecipitation experiments and label-free quantitative analyses has been performed to identify new potential interacting partners of CK2, while the SILAC technology, in combination with the use of a specific and potent inhibitor of CK2, was exploited to identify new putative substrates of this kinase directly in a cellular system. The results obtained confirm the notion that CK2 plays a role in many fundamental cellular functions and clearly indicate a strong involvement of this kinase in the biological processes of protein biosynthesis and degradation. Moreover interesting aspects linked to phosphorylation/dephosphorylation turnover rates emerged from these analyses. A detailed discussion, from a technical and biological point of view, of the data collected is presented. Finally, during my PhD I also collaborated to a project aiming at the identification of the primary molecular targets of antimicrobial photodynamic therapy. This work, not discussed in the thesis, has recently been submitted to the “Journal of Proteomics” with the title: “Molecular Targets of Antimicrobial Photodynamic Therapy Identified by a Proteomic Approach”.
Negli ultimi anni, la ricerca proteomica basata sulla spettrometria di massa è stata applicata in modo esponenziale ai più diversi campi della biochimica, biomedicina e biologia, permettendo il parallelo sviluppo di nuovi approcci per la quantificazione relativa e assoluta delle proteine. Nel corso del mio dottorato, ho seguito lo sviluppo di tre progetti principali, sfruttando diverse tecniche di spettrometria quantitativa. In particolare, le tecnologie SILAC e iTRAQ sono state applicate allo studio del processo di calcificazione delle cellule interstiziali delle valvole aortiche, mentre i metodi SILAC e di quantificazione label-free sono stati sfruttati per l’identificazione di potenziali interattori e substrati della protein chinasi CK2. La calcificazione delle valvole aortiche è una delle più comuni patologie valvolari e prima causa di sostituzione valvolare nei paesi industrializzati. A oggi sfortunatamente non esistono terapie che possano prevenire o curare la deposizione di calcio nelle valvole aortiche, e l’unica soluzione è l’intervento chirurgico. Per chiarire le basi molecolari di questo processo, abbiamo applicato le metodiche SILAC e iTRAQ ad un modello cellulare basato su cellule valvolari cardiache bovine (BVIC), in grado di acquisire un profilo pro-calcifico e favorire la mineralizzazione della matrice extra-cellulare in risposta ad uno stimolo infiammatorio come l’endotossina lipopolisaccaride (LPS). L’utilizzo di due diverse tecnologie allo stesso modello cellulare ha permesso l’identificazione, e la relativa quantificazione, di centinaia di proteine, parecchie delle quali mostrano una significativa alterazione in risposta al trattamento con LPS. L’analisi dei dati ha infatti rivelato l’alterazione di proteine appartenenti a diversi processi cellulari, quali la regolazione del citoscheletro, dei meccanismi ossidoriduttivi e dello spliceosoma, la via metabolica della glicolisi/gluconeogenesi, e il metabolismo dell’arginina, suggerendo il coinvolgimento di queste vie nel fenomeno della calcificazione delle valvole aortiche. Questi risultati rappresentano perciò un punto di partenza per nuovi dettagliati studi dei meccanismi molecolari alla base della calcificazione valvolare indotta da LPS. Gli altri progetti descritti in questa tesi sono focalizzato su CK2, una protein chinasi essenziale, altamente pleiotroica e costitutivamente attiva, fortemente implicata in una moltitudine di processi cellulari, in particolare nella trasduzione dei segnali di sopravvivenza, per la quale sembra giocare un ruolo chiave. Tuttavia una completa comprensione del ruolo che CK2 ricopre nei vari processi cellulari in cui è implicata non è ancora stata raggiunta, perciò questo lavoro ha come scopo l’identificazione di nuovi potenziali interattori e substrati di CK2, allo scopo di chiarire maggiormente la sua funzione all’interno della cellula. Nello specifico, abbiamo abbinato esperimenti d’immunoprecipitazione e analisi quantitativa label-free per lo studio delle proteine che interagiscono con CK2, mentre la tecnologia SILAC combinata con l’uso di un inibitore potente e specifico di CK2 è stata applicata alla ricerca di nuovi potenziali substrati di questa chinasi direttamente in un sistema cellulare. I risultati ottenuti confermano le conoscenze già note riguardo al coinvolgimento di CK2 in diversi processi essenziali per la vita cellulare, e fanno emergere chiaramente un coinvolgimento di primo piano di CK2 nei processi di biosintesi e degradazione proteica. Inoltre, l’analisi dei dati ha anche rivelato interessanti ed inattesi aspetti del turnover di fosforilazione/defosforilazione di proteine fosforilate da CK2. I dati ottenuti sono dettagliatamente presentati in questa tesi, da un punto di vista sia tecnico che biologico. Infine, durante il dottorato ho anche collaborato alla realizzazione di un progetto volto all’identificazione di bersagli molecolari nella terapia fotodinamica antimicrobica, utilizzando un approccio proteomico. Da questa collaborazione, è nato un lavoro (non descritto in questa tesi) che è stato recentemente sottoposto a “Journal of Proteomics” con il titolo: “Molecular Targets of Antimicrobial Photodynamic Therapy Identified by a Proteomic Approach”.
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Walther, Dirk Martin. "Analysis of aging by quantitative proteomics and mitochondrial organellar proteomics". Diss., Ludwig-Maximilians-Universität München, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-174637.

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Ismail, Marcus. "Blodplasmahantering för proteomics". Thesis, Mälardalen University, School of Sustainable Development of Society and Technology, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-5485.

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Stone, Helen Marie. "Proteomics in COPD". Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7565/.

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In alpha-1-antitrypsin deficiency (A1ATD) there is excess neutrophil elastase activity, resulting in proteolytic destruction of the lung parenchyma. I hypothesised that the peptide fragments of proteins present in the lung might be detectable in plasma by mass spectrometry and that they might be useful biomarkers of disease activity and treatment efficacy. Calcium ionophore, neutrophil elastase and proteinase 3 were added to plasma from patients with A1ATD to create an in vitro model of the destructive processes. MALDI-based peptide profiling of plasma from patients pre and post treatment with intravenous A1AT was undertaken and MS/MS performed to identify differences. Plasma was also depleted of abundant plasma proteins, labelled with isobaric tags and analysed by shotgun proteomics. The readily detectible components of the plasma proteome remained unchanged with intravenous A1AT. Addition of ionophore, elastase and proteinase 3 to patient blood generated predominantly fragments of fibrinogen. In patients treated with intravenous A1AT, fragments of A1AT increased significantly with treatment: - 2 of these were fragments of a short C-terminal segment of the A1AT protein and were also present in healthy subjects. The shotgun experiments did not identify any robust biomarkers and illustrate the challenging nature of plasma proteomics.
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Le, Thao Thi. "Aptamers for proteomics". Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/1385.

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Changes in post-translation modifications are very important in the regulation of biological processes. Many modifications occur at very low levels, resulting in a low-abundance of the modified proteins in cells, and therefore assessing those modifications is not an easy task. Modern proteomics needs improved methods for identifying such changes. In this thesis, we focus on generating aptamers that can bind phosphoproteins with high affinities and therefore would be able to detect even low-abundance proteins. Aptamers are short sequences of nucleic acids that can be selected from libraries through a process called SELEX to bind targets of interest with high affinity and specificity. In this work, a phosphotyrosine (pY) peptide in a consensus sequence, commonly found in a class of phosphoproteins recognised by SH2 domains of signalling cascades in cells, was chosen as the target. By choosing this peptide target, we aim to create aptamers that can bind a class of proteins that carry this peptide sequence, mimicking the action of the intracellular SH2 domains. An RNA library with 7×1014 molecules with 30 nucleotides in the random region was employed for the selection and aptamers that bind the pY peptide were selected. Using surface plasmon resonance (SPR), binding affinities of these aptamers with their peptide target were determined (Kd values in high nanomolar (nM) range). In addition, aptamers that bind streptavidin tightly (Kd values in low nM range) were also isolated, as streptavidin was used as the matrix in partitioning step during the selection. Affinities of these aptamers were also determined by SPR. Moreover, fluorescence quenching suggested that the streptavidin binding aptamers bound in or near the biotin binding site. These aptamers can be used as affinity tags for RNA molecules. The secondary structures of both types of the aptamers were predicted based on their random-region sequences using the Mfold program.
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Lang, Alastair Michael. "Developing tissue proteomics : differential in gel electrophoresis in biomarker discovery and proteomic degradation". Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4642/.

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The field of proteomics and functional genomics has developed steadily since the completion of the human genome project. The wealth of genomic information and the pace at which it was compiled was astounding. Proteomics, despite considerable effort, on the other hand has not seen quite the same pace of development. The progress being considerably hindered by the lack of an amplification process and the relative complexity of the proteome in comparison to the genome. These intrinsic difficulties have led to the sensitivity of proteomic techniques being pushed closer to physical limits. There is therefore a further need to re-evaluated techniques such as sample preparation and integrity, analytical methods and collaborative strategies to maximise the effectiveness and quality of data collected. The importance of tissue in scientific and clinical research is unequivocal. However, tissue is difficult to collect, store and work with due to issues with proteomic degradation and storage. Good lab practices can minimise the effect of degradation but degradation of proteins can be rapid. Strategies to minimise degradation include freezing, formalin fixing and microwave treatment which all have their relative advantages and disadvantages. The importance of sample preparation as being the top of the workflow is often acknowledged but improvements are not well described in the literature. The main aim of this thesis is to present investigative studies into the mitigation of some of the limitations in tissue sample degradation, analytical approaches in differential in gel electrophoresis and accessing DiGE spot and tissue profile data. Presented is the evaluation of the effectiveness of rapid and controlled heating of intact tissue to inactivate native enzymatic activity and to aid in the cessation of proteomic degradation. A multifaceted analytical approach of differential in Gel electrophoresis spot data is assessed, giving proteomic profiles of mouse brain tissue. Preliminary data is presented showing that the process of heat-treatment has had a predominantly beneficial effect on mouse brain tissue, with a higher percentage of spots stabilised in heat-treated samples compared to snap-frozen samples. However, stabilisation did occur in snap-frozen samples for different protein spot so the appropriateness of using heat-treatment is as yet not fully determined and requires further analysis. In addition, the variation in tissue profiles of WKY, SP.WKYGla.2a and SHRSP rat model for hypertension is investigated with the future prospect of providing that vital connection between genomic and proteomic data and link phenotype and genotype preliminary investigated. A number of putative markers were identified and quantified using DiGE analysis. In order for these markers to be accepted as biomarkers, more downstream validation is required, however this study provides a good spring board as a proof of concept in using DiGE as an global putative biomarker discovery platform.
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Culwell, Thomas Franklin. "Study of the reproducibility of proteomics methods and variability of fruit fly proteomes /". Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2252.pdf.

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Culwell, Thomas Franklin. "Study of the Reproducibility of Proteomics Methods and Variability of Fruit Fly Proteomes". BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1232.

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The reliability of biomarker discovery by means of proteomics has been called into question. It was speculated that "background noise" variation resulting from differences in preparation and handling of samples and proteome dynamics may mask subtle, yet important, differences due to the biological condition. Little is understood about complex proteomes and their variability. A critical aspect of proteomic biomarker research that is largely unexplored is the comparative reproducibility of certain methods such as two-dimensional gel electrophoresis and liquid chromatography/mass spectrometry. In particular, with liquid chromatography/mass spectrometry, it is not known whether variability in peptide quantitation is dependent on any of their several properties such as size, abundance, or hydrophobicity. Such determinations may be critical in properly assessing the value of proteomics data. The fruit fly Drosophila melanogaster was used as a well-controlled multicellular animal model to study the relationship between the background variation and expected changes induced by environmental or genetic factors. The data, gathered by two different proteomics methods, were used to compare and evaluate the reproducibility of the methods. It is reported that there was on average 15 to 18% variability in quantitative measurements of protein abundance using 2-dimensional gel electrophoresis or liquid chromatography/mass spectrometry. Using liquid chromatography/mass spectrometry, peptides with a smaller mass-to-charge ratio were shown to be measured less reproducibly than peptides with a larger ratio. Statistically significant proteomic differences between fly populations could be demonstrated between males and females. In dynamic experiments, less than 0.5% of proteins measured were shown to change after 24 hour starvation of the flies. However, no significant difference in peptide composition could be found for flies fed on a second diet consisting of the standard diet augmented with 10% ethanol. These results suggest that proteomic variability while evident allowed for biomarker discovery using either method for this model system.
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Svensson, Marcus. "Neuropeptidomics expanding proteomics downwards /". Doctoral thesis, Uppsala : Uppsala universitet, Fakultetsövergripande enheter, Acta Universitatis Upsaliensis, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7465.

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Książki na temat "Proteomics"

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Comai, Lucio, Jonathan E. Katz i Parag Mallick, red. Proteomics. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6747-6.

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Reinders, Jörg, i Albert Sickmann, red. Proteomics. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-157-8.

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Seppo, Meri, i Baumann Marc, red. Proteomics. Amsterdam, The Netherlands: Elsevier, 2001.

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Christine, Finnie, red. Plant proteomics. Oxford, UK: Blackwell Pub., 2006.

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Fernando, Vivanco. Cardiovascular Proteomics. New Jersey: Humana Press, 2006. http://dx.doi.org/10.1385/1597452149.

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Valerie, Mechin, Damerval Catherine, Zivy Michel i Thiellement Hervé. Plant Proteomics. New Jersey: Humana Press, 2006. http://dx.doi.org/10.1385/1597452270.

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Carrera, Mónica, i Jesús Mateos, red. Shotgun Proteomics. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1178-4.

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Owens, Raymond J., red. Structural Proteomics. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1406-8.

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Chen, Yue, i Luke Erber. Functional Proteomics. Washington, DC, USA: American Chemical Society, 2022. http://dx.doi.org/10.1021/acsinfocus.7e5010.

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Corrales, Fernando J., Alberto Paradela i Miguel Marcilla, red. Clinical Proteomics. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1936-0.

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Części książek na temat "Proteomics"

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Schork, Karin, Katharina Podwojski, Michael Turewicz, Christian Stephan i Martin Eisenacher. "Important Issues in : Statistical Considerations of Quantitative Proteomic Data". W Methods in Molecular Biology, 1–20. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1024-4_1.

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AbstractMass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.
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Duan, Dayue Darrel. "Proteomics and Functional Proteomics". W Textbook of Pulmonary Vascular Disease, 591–612. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-87429-6_41.

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Budzinski, Ilara Gabriela F., Thaís Regiani, Mônica T. Veneziano Labate, Simone Guidetti-Gonzalez, Danielle Izilda R. Silva, Maria Juliana Calderan Rodrigues, Janaina Santana Borges, Ivan Miletovic Mozol i Carlos Alberto Labate. "Proteomics". W Omics in Plant Breeding, 59–79. Chichester, UK: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118820971.ch4.

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Ramsden, Jeremy. "Proteomics". W Computational Biology, 223–39. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6702-0_14.

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Ravi, Indu. "Proteomics". W Advances in Biotechnology, 117–49. New Delhi: Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1554-7_8.

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McAllister-Williams, R. Hamish, Daniel Bertrand, Hans Rollema, Raymond S. Hurst, Linda P. Spear, Tim C. Kirkham, Thomas Steckler i in. "Proteomics". W Encyclopedia of Psychopharmacology, 1077–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_308.

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Bertolla, Ricardo P. "Proteomics". W Proteomics in Human Reproduction, 9–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48418-1_2.

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Turner, J. Rick. "Proteomics". W Encyclopedia of Behavioral Medicine, 1756–57. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1689.

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Turner, J. Rick. "Proteomics". W Encyclopedia of Behavioral Medicine, 1550–51. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1689.

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Nahler, Gerhard. "proteomics". W Dictionary of Pharmaceutical Medicine, 149. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1152.

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Streszczenia konferencji na temat "Proteomics"

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Lesley, Scott A., Marc Nasoff, Andreas Kreusch i Glen Spraggon. "High-throughput proteomics". W BiOS 2001 The International Symposium on Biomedical Optics, redaktorzy Ramesh Raghavachari i Weihong Tan. SPIE, 2001. http://dx.doi.org/10.1117/12.424595.

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"Computational proteomics and genomics". W 2011 24th International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2011. http://dx.doi.org/10.1109/cbms.2011.5999043.

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Hall, Drew, Xiahan Zhou i Chih-Cheng Huang. "Magnetoresistive biosensors for quantitative proteomics". W Biosensing and Nanomedicine X, redaktorzy Hooman Mohseni, Massoud H. Agahi i Manijeh Razeghi. SPIE, 2017. http://dx.doi.org/10.1117/12.2276933.

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Lennox, Mark, Neil Robertson i Barry Devereux. "Deep Metric Learning for Proteomics". W 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2020. http://dx.doi.org/10.1109/icmla51294.2020.00057.

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Hashim, O. "Proteomics Approach To Cancer Studies". W 2nd International University of Malaya Research Imaging Symposium (UMRIS) 2005: Fundamentals of Molecular Imaging. Kuala Lumpur, Malaysia: Department of Biomedical Imaging, University of Malaya, 2005. http://dx.doi.org/10.2349/biij.1.1.e7-41.

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Enzer, N. A., S. Mason, B. Choi, A. A. Diaz, G. R. Washko, R. S. J. Estépar i S. Ash. "Prediction of Sarcopenia Using Proteomics". W American Thoracic Society 2022 International Conference, May 13-18, 2022 - San Francisco, CA. American Thoracic Society, 2022. http://dx.doi.org/10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a3787.

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Costessi, Mr Adalberto, Mr Carlo Vascotto, Dr Alex Pines, Mr Rogier Schonenborg, Dr Milena Romanello, Dr Peter Schiller, Prof Luigi Moro i Prof Gianluca Tell. "Bone Proteomics experiment (BOP): the first proteomic analysis of mammalian cells cultured in weightlessness conditions". W 57th International Astronautical Congress. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.iac-06-a1.4.08.

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Giannopoulou, Eugenia G., George Lepouras i Elias S. Manolakos. "VIP: Visualization of integrated proteomics data". W 2008 8th IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696670.

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Henao, Ricardo, J. Will Thompson, M. Arthur Moseley, Geoffrey S. Ginsburg, Lawrence Carin i Joseph E. Lucas. "Hierarchical factor modeling of proteomics data". W 2012 IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences (ICCABS). IEEE, 2012. http://dx.doi.org/10.1109/iccabs.2012.6182638.

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ABAGYAN, RUBEN. "COMPUTATIONAL STRUCTURAL PROTEOMICS AND INHIBITOR DISCOVERY". W Proceedings of the 3rd Annual RECOMB Workshop. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2008. http://dx.doi.org/10.1142/9781848162525_0009.

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Raporty organizacyjne na temat "Proteomics"

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Aebersold, Ruedi. Proteomics: Technology and Applications. Office of Scientific and Technical Information (OSTI), marzec 2003. http://dx.doi.org/10.2172/840129.

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Klein, Jon B. Pediatric Clinical Proteomics Center. Office of Scientific and Technical Information (OSTI), luty 2013. http://dx.doi.org/10.2172/1063767.

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Davidson, George S. High-throughput proteomics : optical approaches. Office of Scientific and Technical Information (OSTI), wrzesień 2008. http://dx.doi.org/10.2172/945920.

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Stemke-Hale, Katherine, Nevine Eltonsy i Zhenlin Ju. Functional Proteomics-Based Ovarian Cancer Biomarkers. Fort Belvoir, VA: Defense Technical Information Center, listopad 2010. http://dx.doi.org/10.21236/ada586794.

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Fodor, I., i D. Nelson. Leveraging Genomics Software to Improve Proteomics Results. Office of Scientific and Technical Information (OSTI), wrzesień 2005. http://dx.doi.org/10.2172/883739.

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Vaidyanathan, P. P. Genomics and Proteomics: A Signal Processor's Tour. Fort Belvoir, VA: Defense Technical Information Center, maj 2004. http://dx.doi.org/10.21236/ada427792.

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Benner, William. CRADA Final Report: Mass Spectrometry for Proteomics. Office of Scientific and Technical Information (OSTI), październik 2001. http://dx.doi.org/10.2172/1157027.

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Moita, Brenda, i Vikram Sharma. Applications of Prostate Cancer Proteomics: A Review. Journal of Young Investigators, kwiecień 2021. http://dx.doi.org/10.22186/jyi.39.4.45-53.

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Witzmann, Frank A. Biomolecular Profiling of Jet Fuel Toxicity Using Proteomics. Fort Belvoir, VA: Defense Technical Information Center, luty 2006. http://dx.doi.org/10.21236/ada444336.

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Ott, Lee W., Frank Witzmann, Camilla A. Mauzy, Claude C. Grigsby, Deirdre A. Mahle i John J. Schlager. Quantifying Biomarkers of Liver Damage Using Shotgun Proteomics. Fort Belvoir, VA: Defense Technical Information Center, sierpień 2006. http://dx.doi.org/10.21236/ada498948.

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