Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis

Background  

In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods.     

Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.     

Investigating MS2/MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence

Improvements in ion trap instrumentation have made n-dimensional mass spectrometry more practical. The overall goal of the study was to describe a model for making use of MS(2) and MS(3) information in mass spectrometry experiments. We present a statistical model for adjusting peptide identification probabilities based on the combined information obtained by coupling peptide assignments of consecutive MS(2) and MS(3) spectra. Using two data sets, a mixture of known proteins and a complex phosphopeptide-enriched sample, we demonstrate an increase in discriminating power of the adjusted probabilities compared with models using MS(2) or MS(3) data only. This work also addresses the overall value of generating MS(3) data as compared with an MS(2)-only approach with a focus on the analysis of phosphopeptide data.     

Subproteomics analysis of phosphorylated proteins: application to the study of B-lymphoblasts from a patient with Scott syndrome

Proteomics based approaches, which examine the expressed proteins of a tissue or cell type, complement the genome initiatives and are increasingly used to address biomedical questions. Proteins are the main functional output, and post-translational modifications such as phosphorylation are very important in determining protein function. To address this question, we developed a method for specific immunoprecipitation using anti-phosphotyrosine antibodies. This method is directly compatible with two-dimensional gel electrophoresis (2-DE). In this report data are presented on B-lymphoblasts from a patient suffering of Scott syndrome. Scott syndrome is an orphan inherited hemorrhagic disorder due to a lack of exposure of procoagulant phosphatidylserine at the exoplasmic leaflet of plasma membrane of blood cells. We hypothesized that a consequence of the mutation is to alter phosphorylation of proteins involved in signal transduction leading to breakdown in cellular signaling pathways mediating phosphatidylserine exposure. An immunoprecipitation method combined with 2-DE was applied to search for modifications in the expression of phosphorylated polypeptides related to Scott syndrome phenotype. We report here the construction of a B-lymphoblast subproteomic map comprising of polypeptides observed after immunoprecipitation using antibodies to phosphotyrosine. The polypeptides were identified either by mass fingerprinting, by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and/or by matching with various lymphoid cell 2-DE maps included in the Laboratoire de Biochimie des Protéines et Protéomique 2-DE database. A differential analysis was further performed to explore several hundred proteins in Scott B-lymphoblasts in comparison with control B-lymphoblasts. Then, image analysis allowed detection of variations between control and Scott syndrome phenotype lymphoblasts. Five spots were specifically found on 2-DE from Scott syndrome phenotype lymphoblasts, and four only appeared on 2-DE from control cells. Protein identification was achieved using a combination of mass fingerprinting and peptide identification using LC-MS/MS.     

In silico analysis of accurate proteomics, complemented by selective isolation of peptides

Protein identification by mass spectrometry is mainly based on MS/MS spectra and the accuracy of molecular mass determination. However, the high complexity and dynamic ranges for any species of proteomic samples, surpass the separation capacity and detection power of the most advanced multidimensional liquid chromatographs and mass spectrometers. Only a tiny portion of signals is selected for MS/MS experiments and a still considerable number of them do not provide reliable peptide identification. In this article, an in silico analysis for a novel methodology of peptides and proteins identification is described. The approach is based on mass accuracy, isoelectric point (pI), retention time (t(R)) and N-terminal amino acid determination as protein identification criteria regardless of high quality MS/MS spectra. When the methodology was combined with the selective isolation methods, the number of unique peptides and identified proteins increases. Finally, to demonstrate the feasibility of the methodology, an OFFGEL-LC-MS/MS experiment was also implemented. We compared the more reliable peptide identified with MS/MS information, and peptide identified with three experimental features (pI, t(R), molecular mass). Also, two theoretical assumptions from MS/MS identification (selective isolation of peptides and N-terminal amino acid) were analyzed. Our results show that using the information provided by these features and selective isolation methods we could found the 93% of the high confidence protein identified by MS/MS with false-positive rate lower than 5%.     

Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data

MOTIVATION: Surface-enhanced laser desorption and ionization (SELDI) time of flight (TOF) is a mass spectrometry technology. The key features in a mass spectrum are its peaks. In order to locate the peaks and quantify their intensities, several pre-processing steps are required. Though different approaches to perform pre-processing have been proposed, there is no systematic study that compares their performance. RESULTS: In this article, we present the results of a systematic comparison of various popular packages for pre-processing of SELDI-TOF data. We evaluate their performance in terms of two of their primary functions: peak detection and peak quantification. Regarding peak quantification, the performance of the algorithms is measured in terms of reproducibility. For peak detection, the comparison is based on sensitivity and false discovery rate. Our results show that for spectra generated with low laser intensity, the software developed by Ciphergen Biosystems (ProteinChip Software 3.1 with the additional tool Biomarker Wizard) produces relatively good results for both peak quantification and detection. On the other hand, for the data produced with either medium or high laser intensity, none of the methods show uniformly better performances under both criteria. Our analysis suggests that an advantageous combination is the use of the packages MassSpecWavelet and PROcess, the former for peak detection and the latter for peak quantification.     

Peak tree: a new tool for multiscale hierarchical representation and peak detection of mass spectrometry data

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.     

Sequence optimization as an alternative to de novo analysis of tandem mass spectrometry data

MOTIVATION: Peptide identification following tandem mass spectrometry (MS/MS) is usually achieved by searching for the best match between the mass spectrum of an unidentified peptide and model spectra generated from peptides in a sequence database. This methodology will be successful only if the peptide under investigation belongs to an available database. Our objective is to develop and test the performance of a heuristic optimization algorithm capable of dealing with some features commonly found in actual MS/MS spectra that tend to stop simpler deterministic solution approaches. RESULTS: We present the implementation of a Genetic Algorithm (GA) in the reconstruction of amino acid sequences using only spectral features, discuss some of the problems associated with this approach and compare its performance to a de novo sequencing method. The GA can potentially overcome some of the most problematic aspects associated with de novo analysis of real MS/MS data such as missing or unclearly defined peaks and may prove to be a valuable tool in the proteomics field. We assess the performance of our algorithm under conditions of perfect spectral information, in situations where key spectral features are missing, and using real MS/MS spectral data.     

Identification of genes and proteins involved in the pleiotropic response to arsenic stress in Caenibacter arsenoxydans, a metalloresistant beta-proteobacterium with an unsequenced genome

The effect of high concentrations of arsenic has been investigated in Caenibacter arsenoxydans, a beta-proteobacterium isolated from an arsenic contaminated environment and able to oxidize arsenite to arsenate. As the genome of this bacterium has not yet been sequenced, the use of a specific proteomic approach based on nano-high performance liquid chromatography tandem mass spectrometry (nanoLC-MS/MS) studies and de novo sequencing to perform cross-species protein identifications was necessary. In addition, a random mutational analysis was performed. Twenty-two proteins and 16 genes were shown to be differentially accumulated and expressed, respectively, in cells grown in the presence of arsenite. Two genes involved in arsenite oxidation and one in arsenite efflux as well as two proteins responsible for arsenate reduction were identified. Moreover, numerous genes and proteins belonging to various functional classes including information and regulation pathways, intermediary metabolism, cell envelope and cellular processes were also up- or down-regulated, which demonstrates that bacterial response to arsenic is pleiotropic.     

Identification and quantification of N-linked glycoproteins using hydrazide chemistry,stable isotope labeling and mass spectrometry

Quantitative proteome profiling using stable isotope protein tagging and automated tandem mass spectrometry (MS/MS) is an emerging technology with great potential for the functional analysis of biological systems and for the detection of clinical diagnostic or prognostic marker proteins. Owing to the enormous complexity of proteomes, their comprehensive analysis is an as-yet-unresolved technical challenge. However, biologically or clinically important information can be obtained if specific, information-rich protein classes, or sub-proteomes, are isolated and analyzed. Glycosylation is the most common post-translational modification. Here we describe a method for the selective isolation, identification and quantification of peptides that contain N-linked carbohydrates. It is based on the conjugation of glycoproteins to a solid support using hydrazide chemistry, stable isotope labeling of glycopeptides and the specific release of formerly N-linked glycosylated peptides via peptide- N-glycosidase F (PNGase F). The recovered peptides are then identified and quantified by MS/MS. We applied the approach to the analysis of plasma membrane proteins and proteins contained in human blood serum.     

Selective Sensitization of Zinc Finger Protein Oxidation by Reactive Oxygen Species through Arsenic Binding

Cysteine oxidation induced by reactive oxygen species (ROS) on redox-sensitive targets such as zinc finger proteins plays a critical role in redox signaling and subsequent biological outcomes. We found that arsenic exposure led to oxidation of certain zinc finger proteins based on arsenic interaction with zinc finger motifs. Analysis of zinc finger proteins isolated from arsenic-exposed cells and zinc finger peptides by mass spectrometry demonstrated preferential oxidation of C3H1 and C4 zinc finger configurations. C2H2 zinc finger proteins that do not bind arsenic were not oxidized by arsenic-generated ROS in the cellular environment. The findings suggest that selectivity in arsenic binding to zinc fingers with three or more cysteines defines the target proteins for oxidation by ROS. This represents a novel mechanism of selective protein oxidation and demonstrates how an environmental factor may sensitize certain target proteins for oxidation, thus altering the oxidation profile and redox regulation.     

Accurate peak list extraction from proteomic mass spectra for identification and profiling studies

Background  

Mass spectrometry is an essential technique in proteomics both to identify the proteins of a biological sample and to compare proteomic profiles of different samples. In both cases, the main phase of the data analysis is the procedure to extract the significant features from a mass spectrum. Its final output is the so-called peak list which contains the mass, the charge and the intensity of every detected biomolecule. The main steps of the peak list extraction procedure are usually preprocessing, peak detection, peak selection, charge determination and monoisotoping operation.     

SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments

We present a statistical method SAINT-MS1 for scoring protein-protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range.     

A platform for accurate mass and time analyses of mass spectrometry data

We describe an integrated suite of algorithms and software for general accurate mass and time (AMT) tagging data analysis of mass spectrometry data. The AMT approach combines identifications from liquid chromatography (LC) tandem mass spectrometry (MS/MS) data with peptide accurate mass and retention time locations from high-resolution LC-MS data. Our workflow includes the traditional AMT approach, in which MS/MS identifications are located in external databases, as well as methods based on more recent hybrid instruments such as the LTQ-FT or Orbitrap, where MS/MS identifications are embedded with the MS data. We demonstrate our AMT workflow's utility for general data synthesis by combining data from two dissimilar biospecimens. Specifically, we demonstrate its use relevant to serum biomarker discovery by identifying which peptides sequenced by MS/MS analysis of tumor tissue may also be present in the plasma of tumor-bearing and control mice. The analysis workflow, referred to as msInspect/AMT, extends and combines existing open-source platforms for LC-MS/MS (CPAS) and LC-MS (msInspect) data analysis and is available in an unrestricted open-source distribution.     

Isotopically labeled crosslinking reagents: resolution of mass degeneracy in the identification of crosslinked peptides

Mass spectrometry in three dimensions (MS3D) is a newly developed method for the determination of protein structures involving intramolecular chemical crosslinking of proteins, proteolytic digestion of the resulting adducts, identification of crosslinks by mass spectrometry (MS), peak assignment using theoretical mass lists, and computational reduction of crosslinks to a structure by distance geometry methods. To facilitate the unambiguous identification of crosslinked peptides from proteolytic digestion mixtures of crosslinked proteins by MS, we introduced double 18O isotopic labels into the crosslinking reagent to provide the crosslinked peptides with a characteristic isotope pattern. The presence of doublets separated by 4 Da in the mass spectra of these materials allowed ready discrimination between crosslinked and modified peptides, and uncrosslinked peptides using automated intelligent data acquisition (IDA) of MS/MS data. This should allow ready automation of the method for application to whole expressible proteomes.     

Tandem mass spectrometry data quality assessment by self-convolution

Background  

Many algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.