2,176 research outputs found

    Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

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    A number of commercial software packages are currently available to perform digital two-dimensional electrophoresis (2D-GE) gel analysis. However, both the high cost of the commercial packages and the unavailability of a standard data analysis workflow, have prompted several groups to develop freeware systems to perform certain steps of gel analysis. Unfortunately, to the best of our knowledge none of them offer a package that performs all the steps envisaged in a 2D-GE gel analysis. Here we describe an ImageJ-based procedure, able to manage all the steps of a 2D-GE gel analysis. ImageJ is a free available image processing and analysis application developed by National Institutes of Health (NIH) and widely used in different life sciences fields as medical imaging, microscopy, western blotting and PAGE. Nevertheless no one has yet developed a procedure enabled to compare spots on 2D-GE gels. We collected all used ImageJ tools in a plug-in that allows us to perform the whole 2D-GE analysis. To test it, we performed a set of 2D-GE experiments on plasma samples from 9 patients victims of acute myocardial infarction and 8 controls, and we compared the results obtained by our procedure to those obtained using a widely diffuse commercial package, finding similar performance

    Two-Dimensional Gel Electrophoresis Image Registration Using Block-Matching Techniques and Deformation Models

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    [Abstract] Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching technique—based on successive deformation, search, fitting, filtering, and interpolation stages—is proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2D–PAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.Galicia. Consellería de Economía e Industria; 10MDS014CTGalicia. Consellería de Economía e Industria; 10SIN105004PRInstituto de Salud Carlos III; PI13/0028

    Proteomics Databases and Websites

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    Information avalanche (overload or expansion) in various scientific fields is a novel issue turned out by a number of factors considered necessary to facilitate their record and registration. Though, the biological science and its diverse fields like proteomics are not immune of this event and even may be as the event’s herald. On the other hand, time as the most valued anxiety of human has encountered a huge mass of information. Therefore, in order to maintain access and ease the understanding of information in several fields some emprises have been prepared. Bioinformatics is an upshot of this anxiety and emprise. Interestingly, proteomics through studying proteins collection in alive things has covered a great portion of bioinformatics. Consequently, a noteworthy outlook on proteomics related databases (DBs) and websites not only can help investigators to face the upcoming archive of databases but also estimate the volume of the needed facilitates. Furthermore, enrichment of the DBs or related websites must be the priority of researchers. Herein, by covering the major proteomics related databases and websites, we have presented a comprehensive classification to simplify and clarify their understanding and applications

    LmaPA2G4, a Homolog of Human Ebp1, Is an Essential Gene and Inhibits Cell Proliferation in L. major

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    We have identified LmaPA2G4, a homolog of the human proliferation-associated 2G4 protein (also termed Ebp1), in aphosphoproteomic screening. Multiple sequence alignment and cluster analysis revealed that LmaPA2G4 is a non-peptidasemember of the M24 family of metallopeptidases. This pseudoenzyme is structurally related to methionine aminopeptidases. Anull mutant system based on negative selection allowed us to demonstrate that LmaPA2G4 is an essential gene inLeishmaniamajor. Over-expression of LmaPA2G4 did not alter cell morphology or the ability to differentiate into metacyclic and amastigotestages. Interestingly, the over-expression affected cell proliferation and virulence in mouse footpad analysis. LmaPA2G4 binds asynthetic double-stranded RNA polyriboinosinic polyribocytidylic acid [poly(I:C)] as shown in an electrophoretic mobility shiftassay (EMSA). Quantitative proteomics revealed that the over-expression of LmaPA2G4 led to accumulation of factors involved intranslation initiation and elongation. Significantly, we found a strong reduction ofde novoprotein biosynthesis in transgenicparasites using a non-radioactive metabolic labeling assay. In conclusion, LmaPA2G4 is an essential gene and is potentiallyimplicated in fundamental biological mechanisms, such as translation, making it an attractive target for therapeutic intervention.Fil: Norris Mullins, Brianna. University Of Notre Dame-Indiana; Estados UnidosFil: VanderKolk, Kaitlin. University Of Notre Dame-Indiana; Estados UnidosFil: Vacchina, Paola. University Of Notre Dame-Indiana; Estados Unidos. Consejo Nacional de Investigaciones CientĂ­ficas y TĂ©cnicas; ArgentinaFil: Joyce, Michelle V.. University Of Notre Dame-Indiana; Estados UnidosFil: Morales, Miguel A.. University Of Notre Dame-Indiana; Estados Unido

    Exploring Information Technologies to Support Shotgun Proteomics

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    Shotgun proteomics refers to the direct analysis of complex protein mixtures to create a profile of the proteins present in the cell. These profiles can be used to study the underlying biological basis for cancer development. Closely studying the profiles as the cancer proliferates reveals the molecular interactions in the cell. They provide clues to researchers on potential drug targets to treat the disease. A little more than a decade old, shotgun proteomics is a relatively new form of discovery, one that is data intensive and requires complex data analysis. Early studies indicated a gap between the ability to analyze biological samples with a mass spectrometer and the information systems available to process and analyze this data. This thesis reflects on an automated proteomic information system at the University of Colorado Central Analytical Facility. Investigators there are using cutting edge proteomic techniques to analyze melanoma cell lines responsible for skin cancer in patients. The paper will provide insight on key design processes in the development of an Oracle relational database and automation system to support high-throughput shotgun proteomics in the facility. It will also discuss significant contributions, technologies, software, a data standard, and leaders in the field developing solutions and products in proteomics

    Optical High Content Nanoscopy of Epigenetic Marks Decodes Phenotypic Divergence in Stem Cells

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    While distinct stem cell phenotypes follow global changes in chromatin marks, single-cell chromatin technologies are unable to resolve or predict stem cell fates. We propose the first such use of optical high content nanoscopy of histone epigenetic marks (epi-marks) in stem cells to classify emergent cell states. By combining nanoscopy with epi-mark textural image informatics, we developed a novel approach, termed EDICTS (Epi-mark Descriptor Imaging of Cell Transitional States), to discern chromatin organizational changes, demarcate lineage gradations across a range of stem cell types and robustly track lineage restriction kinetics. We demonstrate the utility of EDICTS by predicting the lineage progression of stem cells cultured on biomaterial substrates with graded nanotopographies and mechanical stiffness, thus parsing the role of specific biophysical cues as sensitive epigenetic drivers. We also demonstrate the unique power of EDICTS to resolve cellular states based on epi-marks that cannot be detected via mass spectrometry based methods for quantifying the abundance of histone posttranslational modifications. Overall, EDICTS represents a powerful new methodology to predict single cell lineage decisions by integrating high content super-resolution nanoscopy and imaging informatics of the nuclear organization of epi-marks.National Institutes of Health (U.S.) (Grant GM110174

    Image Analysis Workflow for 2-D Electrophoresis Gels Based on ImageJ

    Get PDF
    A number of commercial software packages are currently available to perform digital two-dimensional electrophoresis (2D-GE) gel analysis. However, both the high cost of the commercial packages and the unavailability of a standard data analysis workflow, have prompted several groups to develop freeware systems to perform certain steps of gel analysis. Unfortunately, to the best of our knowledge none of them offer a package that performs all the steps envisaged in a 2D-GE gel analysis. Here we describe an ImageJ-based procedure, able to manage all the steps of a 2D-GE gel analysis. ImageJ is a free available image processing and analysis application developed by National Institutes of Health (NIH) and widely used in different life sciences fields as medical imaging, microscopy, western blotting and PAGE. Nevertheless no one has yet developed a procedure enabled to compare spots on 2D-GE gels. We collected all used ImageJ tools in a plug-in that allows us to perform the whole 2D-GE analysis. To test it, we performed a set of 2D-GE experiments on plasma samples from 9 patients victims of acute myocardial infarction and 8 controls, and we compared the results obtained by our procedure to those obtained using a widely diffuse commercial package, finding similar performances

    Visualization and Differential Analysis of Protein Expression Data Using R.

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    Data analysis is essential to derive meaningful conclusions from proteomic data. This chapter describes ways of performing common data visualization and differential analysis tasks on gel-based proteomic datasets using a freely available statistical software package (R). A workflow followed is illustrated using a synthetic dataset as example

    MolabIS: A Labs Backbone for Storing, Managing and Evaluating Molecular Genetics Data

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    Using paper lab books and spreadsheets to store and manage growing datasets in a file system is inefficient, time consuming and error-prone. Therefore, the overall purpose of this study is to develop an integrated information system for small laboratories conducting Sanger sequencing and microsatellite genotyping projects. To address this, the thesis has investigated the following three issues. First, we proposed a uniform solution using the workflow approach to efficiently collect and store data items in different labs. The outcome is the design of the formalized data framework which is the basic to create a general data model for biodiversity studies. Second, we designed and implemented a web-based information system (MolabIS) allowing lab people to store all original data at each step of their workflow. MolabIS provides essential tools to import, store, organize, search, modify, report and export relevant data. Finally, we conducted a case study to evaluate the performance of MolabIS with typical operations in a production mode. Consequently, we can propose the use of virtual appliance as an efficient solution for the deployment of complex open-source information systems like MolabIS. The major result of this study, along with the publications, is the MolabIS software which is freely released under GPL license at http://www.molabis.org. With its general data model, easy installation process and additional tools for data migration, MolabIS can be used in a wide range of molecular genetics labs

    Accounting for spot matching uncertainty in the analysis of proteomics data from two-dimensional gel electrophoresis

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    Two-dimensional gel electrophoresis is a biochemical technique that combines isoelectric focusing and SDS-polyacrylamide gel technology to achieve simultaneous separation of protein mixtures on the basis of isoelectric point and molecular weight. Upon staining, each protein on a gel can be characterized by an intensity measurement that reflects its abundance in the mixture. These can then conceptually be used to determine which proteins are differentially expressed under different experimental conditions. We propose an EM approach to identify differentially expressed proteins using an inferential strategy that accounts for uncertainty in matching spots to proteins across gels. The underlying mixture model has trivariate Gaussian components. The application of the EM is however, not straightforward, with the main difficulty lying in the E-step calculations because of the dependent structure of proteins within each gel. Therefore, the usual model-based clustering approach is inapplicable, and an MCMC approach is employed. Through data-based simulation, we demonstrate that our proposed method effectively accounts for uncertainty in spot matching and more successfully distinguishes differentially and non-differentially expressed proteins than a naĂŻve t-test which ignores uncertainty in spot matching
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