54 research outputs found
Digging deeper in the data, novel approaches and analysis methods in quantitative peptide-centric proteomics
No full textAnalysis of the resolution limitations of peptide identification algorithms
No full textProteome identification using peptide-centric proteomics techniques is a routinely used analysis technique. One of the most powerful and popular methods for the identification of peptides from MS/MS spectra is protein database matching using search engines. Significance thresholding through false discovery rate (FDR) estimation by target/decoy searches is used to ensure the retention of predominantly confident assignments of MS/MS spectra to peptides. However, shortcomings have become apparent when such decoy searches are used to estimate the FDR. To study these shortcomings, we here introduce a novel kind of decoy database that contains isobaric mutated versions of the peptides that were identified in the original search. Because of the supervised way in which the entrapment sequences are generated, we call this a directed decoy database. Since the peptides found in our directed decoy database are thus specifically designed to look quite similar to the forward identifications, the limitations of the existing search algorithms in making correct calls in such strongly confusing situations can be analyzed. Interestingly, for the vast majority of confidently identified peptide identifications, a directed decoy peptide-to-spectrum match can be found that has a better or equal match score than the forward match score, highlighting an important issue in the interpretation of peptide identifications in present-day high-throughput proteomics
A comparison of MS2-based label-free quantitative proteomic techniques with regards to accuracy and precision
No full textThe advent of algorithms for fragmentation spectrum-based label-free quantitative proteomics has enabled straightforward quantification of shotgun proteomic experiments. Despite the popularity of these approaches, few studies have been performed to assess their performance. We have therefore profiled the precision and the accuracy of three distinct relative label-free methods on both the protein and the proteome level. We derived our test data from two well-characterized publicly available quantitative data sets
RIBAR and xRIBAR: methods for reproducible relative MS/MS-based label-free protein quantification
No full textMass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various, MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new Methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones
A case study on the comparison of different software tools for automated quantification of peptides
No full textMS-driven proteomics has evolved over the past two decades to a high tech and high impact research field. Two distinct factors clearly influenced its expansion: the rapid growth of an arsenal of instrument and proteomic techniques that led to an explosion of high quality data and the development of software tools to analyze and interpret these data which boosted the number of scientific discoveries. In analogy with the benchmarking of new instruments and proteomic techniques, such software tools must be thoroughly tested and analyzed. Recently, new tools were developed for automatic peptide quantification in quantitative proteomic experiments. Here we present a case study where the most recent and frequently used tools are analyzed and compared
A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods
No full textThe identification of peptides and proteins from fragmentation mass spectra is a very common approach in the field of proteomics. Contemporary high-throughput peptide identification pipelines can quickly produce large quantities of MS/MS data that contain valuable knowledge about the actual physicochemical processes involved in the peptide fragmentation process, which can be extracted through extensive data mining studies. As these studies attempt to exploit the intensity information contained in the MS/MS spectra, a critical step required for a meaningful comparison of this information between MS/MS spectra is peak intensity normalization. We here describe a procedure for quantifying the efficiency of different published normalization methods in terms of the quartile coefficient of dispersion (qcod) statistic. The quartile coefficient of dispersion is applied to measure the dispersion of the peak intensities between redundant MS/MS spectra, allowing the quantification of the differences in computed peak intensity reproducibility between the different normalization methods. We demonstrate that our results are independent of the data set used in the evaluation procedure, allowing us to provide generic guidance on the choice of normalization method to apply in a certain MS/MS pipeline application
Rover: a tool to visualize and validate quantitative proteomics data from different sources
No full textManual validation of regulated proteins found in MS-driven quantitative proteome studies is tedious. Here we present Rover (http://genesis.ugent.be/rover), a tool that facilitates this process. Rover accepts quantitative data from different sources such as MASCOT Distiller and MaxQuant and, in an intuitive environment, Rover visualizes these data such that the user can select and validate algorithm-suggested regulated proteins in the frame of the whole experiment and in the context of the protein inference problem
RIBAR and xRIBAR: Methods for Reproducible Relative MS/MS-based Label-Free Protein Quantification
No full textMass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones
RIBAR and xRIBAR: Methods for Reproducible Relative MS/MS-based Label-Free Protein Quantification
No full textMass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones
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