The Experts below are selected from a list of 8298 Experts worldwide ranked by ideXlab platform
Patrick A. Limbach - One of the best experts on this subject based on the ideXlab platform.
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effect of sodium dodecyl sulfate micelles on Peptide Mass Fingerprinting by matrix assisted laser desorption ionization Mass spectrometry
Rapid Communications in Mass Spectrometry, 2004Co-Authors: Rama Tummala, Patrick A. LimbachAbstract:Here we have examined the effect of sodium dodecyl sulfate (SDS) at various concentrations on matrix-assisted laser desorption/ionization (MALDI) Peptide Mass Fingerprinting experiments. Several model proteins were digested with trypsin and then various amounts of SDS were added prior to MALDI Mass spectrometry. Evaluation of the data was made by calculating the amino acid sequence coverage within each analysis. It was found that addition of 0.1-0.3% w/v SDS prior to MALDI analysis results in an increase in the number of tryptic Peptides detected thereby improving the total sequence coverage of the analysis. The use of SDS at concentrations near its critical micelle concentration can improve sequence coverage from MALDI Peptide Mass Fingerprinting analyses allowing for increased confidence in protein identification or additional opportunities to identify putative regions of posttranslational modification.
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Effect of sodium dodecyl sulfate micelles on Peptide Mass Fingerprinting by matrix-assisted laser desorption/ionization Mass spectrometry.
Rapid Communications in Mass Spectrometry, 2004Co-Authors: Rama Tummala, Patrick A. LimbachAbstract:Here we have examined the effect of sodium dodecyl sulfate (SDS) at various concentrations on matrix-assisted laser desorption/ionization (MALDI) Peptide Mass Fingerprinting experiments. Several model proteins were digested with trypsin and then various amounts of SDS were added prior to MALDI Mass spectrometry. Evaluation of the data was made by calculating the amino acid sequence coverage within each analysis. It was found that addition of 0.1-0.3% w/v SDS prior to MALDI analysis results in an increase in the number of tryptic Peptides detected thereby improving the total sequence coverage of the analysis. The use of SDS at concentrations near its critical micelle concentration can improve sequence coverage from MALDI Peptide Mass Fingerprinting analyses allowing for increased confidence in protein identification or additional opportunities to identify putative regions of posttranslational modification.
Michael Buckley - One of the best experts on this subject based on the ideXlab platform.
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semi supervised machine learning for automated species identification by collagen Peptide Mass Fingerprinting
BMC Bioinformatics, 2018Co-Authors: Michael BuckleyAbstract:Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput Peptide Mass Fingerprinting has been largely applied to bacterial identification, but increasingly used to identify archaeological and palaeontological skeletal material to yield information on past environments and human-animal interaction. However, as applications move away from predominantly domesticate and the more abundant wild fauna to a much wider range of less common taxa that do not yet have genetically-derived sequence information, robust methods of species identification and biomarker selection need to be determined. Here we developed a supervised machine learning algorithm for classifying the species of ancient remains based on collagen Fingerprinting. The aim was to minimise requirements on prior knowledge of known species while yielding satisfactory sensitivity and specificity. The algorithm uses iterations of a modified random forest classifier with a similarity scoring system to expand its identified samples. We tested it on a set of 6805 spectra and found that a high level of accuracy can be achieved with a training set of five identified specimens per taxon. This method consistently achieves higher accuracy than two-dimensional principal component analysis and similar accuracy with hierarchical clustering using optimised parameters, which greatly reduces requirements for human input. Within the vertebrata, we demonstrate that this method was able to achieve the taxonomic resolution of family or sub-family level whereas the genus- or species-level identification may require manual interpretation or further experiments. In addition, it also identifies additional species biomarkers than those previously published.
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Semi-supervised machine learning for automated species identification by collagen Peptide Mass Fingerprinting
BMC, 2018Co-Authors: Michael BuckleyAbstract:Abstract Background Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput Peptide Mass Fingerprinting has been largely applied to bacterial identification, but increasingly used to identify archaeological and palaeontological skeletal material to yield information on past environments and human-animal interaction. However, as applications move away from predominantly domesticate and the more abundant wild fauna to a much wider range of less common taxa that do not yet have genetically-derived sequence information, robust methods of species identification and biomarker selection need to be determined. Results Here we developed a supervised machine learning algorithm for classifying the species of ancient remains based on collagen Fingerprinting. The aim was to minimise requirements on prior knowledge of known species while yielding satisfactory sensitivity and specificity. The algorithm uses iterations of a modified random forest classifier with a similarity scoring system to expand its identified samples. We tested it on a set of 6805 spectra and found that a high level of accuracy can be achieved with a training set of five identified specimens per taxon. Conclusions This method consistently achieves higher accuracy than two-dimensional principal component analysis and similar accuracy with hierarchical clustering using optimised parameters, which greatly reduces requirements for human input. Within the vertebrata, we demonstrate that this method was able to achieve the taxonomic resolution of family or sub-family level whereas the genus- or species-level identification may require manual interpretation or further experiments. In addition, it also identifies additional species biomarkers than those previously published
Rama Tummala - One of the best experts on this subject based on the ideXlab platform.
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effect of sodium dodecyl sulfate micelles on Peptide Mass Fingerprinting by matrix assisted laser desorption ionization Mass spectrometry
Rapid Communications in Mass Spectrometry, 2004Co-Authors: Rama Tummala, Patrick A. LimbachAbstract:Here we have examined the effect of sodium dodecyl sulfate (SDS) at various concentrations on matrix-assisted laser desorption/ionization (MALDI) Peptide Mass Fingerprinting experiments. Several model proteins were digested with trypsin and then various amounts of SDS were added prior to MALDI Mass spectrometry. Evaluation of the data was made by calculating the amino acid sequence coverage within each analysis. It was found that addition of 0.1-0.3% w/v SDS prior to MALDI analysis results in an increase in the number of tryptic Peptides detected thereby improving the total sequence coverage of the analysis. The use of SDS at concentrations near its critical micelle concentration can improve sequence coverage from MALDI Peptide Mass Fingerprinting analyses allowing for increased confidence in protein identification or additional opportunities to identify putative regions of posttranslational modification.
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Effect of sodium dodecyl sulfate micelles on Peptide Mass Fingerprinting by matrix-assisted laser desorption/ionization Mass spectrometry.
Rapid Communications in Mass Spectrometry, 2004Co-Authors: Rama Tummala, Patrick A. LimbachAbstract:Here we have examined the effect of sodium dodecyl sulfate (SDS) at various concentrations on matrix-assisted laser desorption/ionization (MALDI) Peptide Mass Fingerprinting experiments. Several model proteins were digested with trypsin and then various amounts of SDS were added prior to MALDI Mass spectrometry. Evaluation of the data was made by calculating the amino acid sequence coverage within each analysis. It was found that addition of 0.1-0.3% w/v SDS prior to MALDI analysis results in an increase in the number of tryptic Peptides detected thereby improving the total sequence coverage of the analysis. The use of SDS at concentrations near its critical micelle concentration can improve sequence coverage from MALDI Peptide Mass Fingerprinting analyses allowing for increased confidence in protein identification or additional opportunities to identify putative regions of posttranslational modification.
José Manuel Gallardo - One of the best experts on this subject based on the ideXlab platform.
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Arginine kinase Peptide Mass Fingerprinting as a proteomic approach for species identification and taxonomic analysis of commercially relevant shrimp species.
Journal of Agricultural and Food Chemistry, 2009Co-Authors: Ignacio Ortea, Benito Cañas, Pilar Calo-mata, Jorge Barros-velázquez, José Manuel GallardoAbstract:A proteomic approach aimed at species identification and taxonomic analysis of shrimp species of commercial interest is presented. Six different species belonging to the order Decapoda were considered. Preliminary, two-dimensional gel electrophoresis (2-DE) analysis of the sarcoplasmic proteome revealed interspecific variability in the isoelectric point (pI) of arginine kinase. For this reason, arginine kinase spot was selected as a potential molecular marker and subjected to tryptic digestion followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) Peptide Mass Fingerprinting (PMF) analysis. Arginine kinase PMF allowed the differentiation of the six species studied. Four samples of commercial origin obtained in local markets were analyzed to validate the methodology. The PMF cluster analysis also provided information about the phylogenetic relationships in these species. The application of this methodology may be of interest for the differentiation and taxonomic analysis of shri...
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arginine kinase Peptide Mass Fingerprinting as a proteomic approach for species identification and taxonomic analysis of commercially relevant shrimp species
Journal of Agricultural and Food Chemistry, 2009Co-Authors: Ignacio Ortea, Benito Cañas, Pilar Calomata, Jorge Barrosvelazquez, José Manuel GallardoAbstract:A proteomic approach aimed at species identification and taxonomic analysis of shrimp species of commercial interest is presented. Six different species belonging to the order Decapoda were considered. Preliminary, two-dimensional gel electrophoresis (2-DE) analysis of the sarcoplasmic proteome revealed interspecific variability in the isoelectric point (pI) of arginine kinase. For this reason, arginine kinase spot was selected as a potential molecular marker and subjected to tryptic digestion followed by matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) Peptide Mass Fingerprinting (PMF) analysis. Arginine kinase PMF allowed the differentiation of the six species studied. Four samples of commercial origin obtained in local markets were analyzed to validate the methodology. The PMF cluster analysis also provided information about the phylogenetic relationships in these species. The application of this methodology may be of interest for the differentiation and taxonomic analysis of shrimp species complementing DNA-based phylogenetic studies.
Michael Wagner - One of the best experts on this subject based on the ideXlab platform.
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Kolmogorov-Smirnov scores and intrinsic Mass tolerances for Peptide Mass Fingerprinting.
Journal of Proteome Research, 2010Co-Authors: Rachana Jain, Michael WagnerAbstract:Peptide Mass Fingerprinting (PMF) uses proteolytic Peptide Masses and a prespecified search database to identify proteins. At the core of a PMF database search algorithm lies a quality statistic that gauges the level to which an experimentally obtained peak list agrees with a list of theoretically observable Mass-to-charge ratios for a protein in a database. In this paper, we propose, implement and evaluate using a statistical (Kolmogorov−Smirnov-based) test computed for a large Mass error threshold to avoid the choice of appropriate Mass tolerance by the user. We use the Mass tolerance identified by the Kolmogorov−Smirnov test for computing other quality measures. The results from our careful and extensive benchmarks using publicly available gold-standard data sets suggest that the new method of computing the quality statistics without requiring the end-user to select a Mass tolerance is competitive. We investigate the similarity measures in terms of their information content and conclude that the simila...
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Kolmogorov-Smirnov scores and intrinsic Mass tolerances for Peptide Mass Fingerprinting.
Journal of proteome research, 2010Co-Authors: Rachana Jain, Michael WagnerAbstract:Peptide Mass Fingerprinting (PMF) uses proteolytic Peptide Masses and a prespecified search database to identify proteins. At the core of a PMF database search algorithm lies a quality statistic that gauges the level to which an experimentally obtained peak list agrees with a list of theoretically observable Mass-to-charge ratios for a protein in a database. In this paper, we propose, implement and evaluate using a statistical (Kolmogorov-Smirnov-based) test computed for a large Mass error threshold to avoid the choice of appropriate Mass tolerance by the user. We use the Mass tolerance identified by the Kolmogorov-Smirnov test for computing other quality measures. The results from our careful and extensive benchmarks using publicly available gold-standard data sets suggest that the new method of computing the quality statistics without requiring the end-user to select a Mass tolerance is competitive. We investigate the similarity measures in terms of their information content and conclude that the similarity measures are complementary and can be combined into a scoring function to possibly improve upon the over all accuracy of PMF based identification methods.