MS2‐Deisotoper: A Tool for Deisotoping High‐Resolution MS/MS Spectra in Normal and Heavy Isotope‐Labelled Samples

High‐resolution MS/MS spectra of peptides can be deisotoped to identify monoisotopic masses of peptide fragments. The use of such masses should improve protein identification rates. However, deisotoping is not universally used and its benefits have not been fully explored. Here, MS2‐Deisotoper, a tool for use prior to database search, is used to identify monoisotopic peaks in centroided MS/MS spectra. MS2‐Deisotoper works by comparing the mass and relative intensity of each peptide fragment peak to every other peak of greater mass, and by applying a set of rules concerning mass and intensity differences. After comprehensive parameter optimization, it is shown that MS2‐Deisotoper can improve the number of peptide spectrum matches (PSMs) identified by up to 8.2% and proteins by up to 2.8%. It is effective with SILAC and non‐SILAC MS/MS data. The identification of unique peptide sequences is also improved, increasing the number of human proteoforms by 3.7%. Detailed investigation of results shows that deisotoping increases Mascot ion scores, improves FDR estimation for PSMs, and leads to greater protein sequence coverage. At a peptide level, it is found that the efficacy of deisotoping is affected by peptide mass and charge. MS2‐Deisotoper can be used via a user interface or as a command‐line tool.     

A Heuristic method for assigning a false-discovery rate for protein identifications from Mascot database search results

MS/MS and database searching has emerged as a valuable technology for rapidly analyzing protein expression, localization, and post-translational modifications. The probability-based search engine Mascot has found widespread use as a tool to correlate tandem mass spectra with peptides in a sequence database. Although the Mascot scoring algorithm provides a probability-based model for peptide identification, the independent peptide scores do not correlate with the significance of the proteins to which they match. Herein, we describe a heuristic method for organizing proteins identified at a specified false-discovery rate using Mascot-matched peptides. We call this method PROVALT, and it uses peptide matches from a random database to calculate false-discovery rates for protein identifications and reduces a complex list of peptide matches to a nonredundant list of homologous protein groups. This method was evaluated using Mascot-identified peptides from a Trypanosoma cruzi epimastigote whole-cell lysate, which was separated by multidimensional LC and analyzed by MS/MS. PROVALT was then compared with the two traditional methods of protein identification when using Mascot, the single peptide score and cumulative protein score methods, and was shown to be superior to both in regards to the number of proteins identified and the inclusion of lower scoring nonrandom peptide matches.     

Experiment-specific estimation of peptide identification probabilities using a randomized database

Determining the error rate for peptide and protein identification accurately and reliably is necessary to enable evaluation and crosscomparisons of high throughput proteomics experiments. Currently, peptide identification is based either on preset scoring thresholds or on probabilistic models trained on datasets that are often dissimilar to experimental results. The false discovery rates (FDR) and peptide identification probabilities for these preset thresholds or models often vary greatly across different experimental treatments, organisms, or instruments used in specific experiments. To overcome these difficulties, randomized databases have been used to estimate the FDR. However, the cumulative FDR may include low probability identifications when there are a large number of peptide identifications and exclude high probability identifications when there are few. To overcome this logical inconsistency, this study expands the use of randomized databases to generate experiment-specific estimates of peptide identification probabilities. These experiment-specific probabilities are generated by logistic and Loess regression models of the peptide scores obtained from original and reshuffled database matches. These experiment-specific probabilities are shown to very well approximate "true" probabilities based on known standard protein mixtures across different experiments. Probabilities generated by the earlier Peptide_Prophet and more recent LIPS models are shown to differ significantly from this study's experiment-specific probabilities, especially for unknown samples. The experiment-specific probabilities reliably estimate the accuracy of peptide identifications and overcome potential logical inconsistencies of the cumulative FDR. This estimation method is demonstrated using a Sequest database search, LIPS model, and a reshuffled database. However, this approach is generally applicable to any search algorithm, peptide scoring, and statistical model when using a randomized database.     

Analysis of the resolution limitations of peptide identification algorithms

Proteome 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.     

Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics

Development of robust statistical methods for validation of peptide assignments to tandem mass (MS/MS) spectra obtained using database searching remains an important problem. PeptideProphet is one of the commonly used computational tools available for that purpose. An alternative simple approach for validation of peptide assignments is based on addition of decoy (reversed, randomized, or shuffled) sequences to the searched protein sequence database. The probabilistic modeling approach of PeptideProphet and the decoy strategy can be combined within a single semisupervised framework, leading to improved robustness and higher accuracy of computed probabilities even in the case of most challenging data sets. We present a semisupervised expectation-maximization (EM) algorithm for constructing a Bayes classifier for peptide identification using the probability mixture model, extending PeptideProphet to incorporate decoy peptide matches. Using several data sets of varying complexity, from control protein mixtures to a human plasma sample, and using three commonly used database search programs, SEQUEST, MASCOT, and TANDEM/k-score, we illustrate that more accurate mixture estimation leads to an improved control of the false discovery rate in the classification of peptide assignments.     

A new strategy to filter out false positive identifications of peptides in SEQUEST database search results

Based on the randomized database method and a linear discriminant function (LDF) model, a new strategy to filter out false positive matches in SEQUEST database search results is proposed. Given an experiment MS/MS dataset and a protein sequence database, a randomized database is constructed and merged with the original database. Then, all MS/MS spectra are searched against the combined database. For each expected false positive rate (FPR), LDFs are constructed for different charge states and used to filter out the false positive matches from the normal database. In order to investigate the error of FPR estimation, the new strategy was applied to a reference dataset. As a result, the estimated FPR was very close to the actual FPR. While applied to a human K562 cell line dataset, which is a complicated dataset from real sample, more matches could be confirmed than the traditional cutoff-based methods at the same estimated FPR. Also, though most of the results confirmed by the LDF model were consistent with those of PeptideProphet, the LDF model could still provide complementary information. These results indicate that the new method can reliably control the FPR of peptide identifications and is more sensitive than traditional cutoff-based methods.     

Nonlinear fitting method for determining local false discovery rates from decoy database searches

False discovery rate (FDR) analyses of protein and peptide identification results using decoy database searching conventionally report aggregate or global FDRs for a whole set of identifications, which are often not very informative about the error rates of individual members in the set. We describe a nonlinear curve fitting method for calculating the local FDR, which estimates the chance that an individual protein (or peptide) is incorrect, and present a simple tool that implements this analysis. The goal of this method is to offer a simple extension to the now commonplace decoy database searching, providing additional valuable information.     

A semi-empirical approach for predicting unobserved peptide MS/MS spectra from spectral libraries

Spectral library searching is a promising alternative to sequence database searching in peptide identification from MS/MS spectra. The key advantage of spectral library searching is the utilization of more spectral features to improve score discrimination between good and bad matches, and hence sensitivity. However, the coverage of reference spectral library is limited by current experimental and computational methods. We developed a computational approach to expand the coverage of spectral libraries with semi-empirical spectra predicted from perturbing known spectra of similar sequences, such as those with single amino acid substitutions. We hypothesized that the peptide of similar sequences should produce similar fragmentation patterns, at least in most cases. Our results confirm our hypothesis and specify when this approach can be applied. In actual spectral searching of real data sets, the sensitivity advantage of spectral library searching over sequence database searching can be mostly retained even when all real spectra are replaced by semi-empirical ones. We demonstrated the applicability of this approach by detecting several known non-synonymous single-nucleotide polymorphisms in three large human data sets by spectral searching.     

Transferred Subgroup False Discovery Rate for Rare Post-translational Modifications Detected by Mass Spectrometry

In shotgun proteomics, high-throughput mass spectrometry experiments and the subsequent data analysis produce thousands to millions of hypothetical peptide identifications. The common way to estimate the false discovery rate (FDR) of peptide identifications is the target-decoy database search strategy, which is efficient and accurate for large datasets. However, the legitimacy of the target-decoy strategy for protein-modification-centric studies has rarely been rigorously validated. It is often the case that a global FDR is estimated for all peptide identifications including both modified and unmodified peptides, but that only a subgroup of identifications with a certain type of modification is focused on. As revealed recently, the subgroup FDR of modified peptide identifications can differ dramatically from the global FDR at the same score threshold, and thus the former, when it is of interest, should be separately estimated. However, rare modifications often result in a very small number of modified peptide identifications, which makes the direct separate FDR estimation inaccurate because of the inadequate sample size. This paper presents a method called the transferred FDR for accurately estimating the FDR of an arbitrary number of modified peptide identifications. Through flexible use of the empirical data from a target-decoy database search, a theoretical relationship between the subgroup FDR and the global FDR is made computable. Through this relationship, the subgroup FDR can be predicted from the global FDR, allowing one to avoid an inaccurate direct estimation from a limited amount of data. The effectiveness of the method is demonstrated with both simulated and real mass spectra.Post-translational modifications of proteins often play an essential role in the functions of proteins in cells (1). Abnormal modifications can change the properties of proteins, causing serious diseases (2). Because protein modifications are not directly encoded in the nucleotide sequences of organisms, they must be investigated at the protein level. In recent years, mass spectrometry technology has developed rapidly and has become the standard method for identifying proteins and their modifications in biological and clinical samples (3–5).In shotgun proteomics experiments, proteins are digested into peptide mixtures that are then analyzed via high-throughput liquid chromatography–tandem mass spectrometry, resulting in thousands to millions of tandem mass spectra. To identify the peptide sequences and the modifications on them, the spectra are commonly searched against a protein sequence database (6–8). During the database search, according to the variable modification types specified by the user, all forms of modified candidate peptides are enumerated. For each spectrum, candidate peptides (with possible modifications) from the database are scored according to the quality of their match to the input spectrum. However, for many reasons, the top-scored matches are not always correct peptide identifications, and therefore they must be filtered according to their identification scores (9). Finding an appropriate score threshold that gives the desired false discovery rate (FDR)¹ is a multiple hypothesis testing problem (10–12).At present, the common way to control the FDR of peptide identifications is an empirical approach called the target-decoy search strategy (13). In this strategy, in addition to the target protein sequences, the mass spectra are also searched against the same number of decoy protein sequences (e.g. reverse sequences of the target proteins). Because an incorrect identification has an equal chance of being a match to the target sequences or to the decoy sequences, the number of decoy matches above a score threshold can be used as an estimate of the number of random target matches, and the FDR (of the target matches) can be simply estimated as the number of decoy matches divided by the number of target matches. The target-decoy method, although simple and effective, is applicable to large datasets only. When the number of matches being evaluated is very small, this method becomes inaccurate because of the inadequate sample size (13, 14). Fortunately, for high-throughput proteomic mass spectrometry experiments, the number of mass spectra is always sufficiently large. Current efforts are mostly devoted to increasing the sensitivity of peptide identification at a given FDR by using various techniques such as machine learning (15).When the purpose of an experiment is to search for protein modifications, the problem of FDR estimation becomes somewhat complex. In fact, the legality of the target-decoy method for modification-centric studies was not rigorously discussed until very recently (16). At present, for multiple reasons, the identifications of modified and unmodified peptides are usually combined in the search result, and a global FDR is estimated for them in combination, with only a subgroup of identifications with specific modifications being focused on. However, the FDR of modified peptides can be significantly or even extremely different from that of unmodified peptides at the same score threshold. There are three reasons for this fact. First, because the spectra of modified peptides can have their own features (e.g. insufficient fragmentation or neutral losses), they can have different score distributions from those of unmodified peptides. Second, because the proportions of modified and unmodified peptides in the protein sample are different, the prior probabilities of obtaining a correct identification are different for modified and unmodified peptides. Third, because the proportions of modified and unmodified candidate peptides in the search space are different, the prior probabilities of obtaining an incorrect identification are also different for modified and unmodified peptides. Therefore, the modified peptide identifications of interest should be extracted from the identification result and subjected to a separate FDR estimation, as pointed out recently (16–18).The difficulty of separate FDR estimations is highlighted when there are too few modified peptide identifications to allow an accurate estimation. Many protein modifications are present in low abundance in cells but play important biological functions. These rare modifications have very low chances of being detected by mass spectrometry. A crucial question is, if very few modifications are identified from a very large dataset of mass spectra, can they be regarded as correct identifications? There was no answer to this question in the past in terms of FDR control. The target-decoy strategy loses its efficacy in such cases. For example, imagine that we have 10 modified peptide identifications above a score threshold after a search and that all of them are matches to target protein sequences. Can we say that the FDR of these identifications is zero (0/10)? If we decrease the score threshold slightly in such a way that one more modified peptide identification is included but find that that peptide is unfortunately a match to the decoy sequence, then can we say that the FDR of the top 10 target identifications is 10% (1/10)? It is clear here that the inclusion or exclusion of the 11th decoy identification has a great influence on the FDR estimated via the common target-decoy strategy. In fact, according to a binomial model (14), the probability that there are one or more false identifications among the top 10 target matches is as high as 0.5, which means that the real proportion of false discoveries has a half-chance of being no less than 10% (1/10). The appropriate way to estimate the FDR of the 10 target identifications is to give an appropriate estimate of the expected number of false identifications among them, and, most important, this estimate must not be an integer (e.g. 0 or 1) but can be a real number between 0 and 1. Note that single-spectrum significance measures (e.g. p values) are not appropriate for multiple hypothesis testing, not to mention that they can hardly be accurately computed in mass spectrometry.Separate FDR estimation for grouped multiple hypothesis testing is not new in statistics and bioinformatics. A typical example is the microarray data of mRNAs from different locations in an organism or from genes that are involved in different biological processes (19, 20). Efron (21) recently proposed a method for robust separate FDR estimation for small subgroups in the empirical Bayes framework. The underlying principle of this method is that if we can find the quantitative relationship between the subgroup FDR and the global FDR, the former can be indirectly inferred from the latter instead of being estimated from a limited amount of data. The relationship given by Efron is quite general and makes no use of domain-specific information. Furthermore, it requires known conditional probabilities of null and non-null cases given the score threshold. These probabilities are, however, unavailable in the modified peptide identification problem.This paper presents a dedicated method for accurate FDR estimation for rare protein modifications detected from large-scale mass spectral data. This method is based on a theoretical relationship between the subgroup FDR of modified peptide identifications and the global FDR of all peptide identifications. To make the relationship computable, the component factors in it are replaced by or fitted from the empirical data of the target-decoy database search results. Most important, the probability that an incorrect identification is an assignment of a modified peptide is approximated by a linear function of the score threshold. By extrapolation, this probability can be reliably obtained for high-tail scores that are suitable as thresholds. The proposed method was validated on both simulated and real mass spectra. To the best of our knowledge, this study is the first effort toward reliable FDR control of rare protein modifications identified from mass spectra. (Note that the error rate control for modification site location is another complex problem (22, 23) and is not the aim of this paper.)     

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC‐MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re‐assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.     

Randomized sequence databases for tandem mass spectrometry peptide and protein identification

Tandem mass spectrometry (MS/MS) combined with database searching is currently the most widely used method for high-throughput peptide and protein identification. Many different algorithms, scoring criteria, and statistical models have been used to identify peptides and proteins in complex biological samples, and many studies, including our own, describe the accuracy of these identifications, using at best generic terms such as "high confidence." False positive identification rates for these criteria can vary substantially with changing organisms under study, growth conditions, sequence databases, experimental protocols, and instrumentation; therefore, study-specific methods are needed to estimate the accuracy (false positive rates) of these peptide and protein identifications. We present and evaluate methods for estimating false positive identification rates based on searches of randomized databases (reversed and reshuffled). We examine the use of separate searches of a forward then a randomized database and combined searches of a randomized database appended to a forward sequence database. Estimated error rates from randomized database searches are first compared against actual error rates from MS/MS runs of known protein standards. These methods are then applied to biological samples of the model microorganism Shewanella oneidensis strain MR-1. Based on the results obtained in this study, we recommend the use of use of combined searches of a reshuffled database appended to a forward sequence database as a means providing quantitative estimates of false positive identification rates of peptides and proteins. This will allow researchers to set criteria and thresholds to achieve a desired error rate and provide the scientific community with direct and quantifiable measures of peptide and protein identification accuracy as opposed to vague assessments such as "high confidence."     

An improved method for the construction of decoy peptide MS/MS spectra suitable for the accurate estimation of false discovery rates

The relevance of libraries of annotated MS/MS spectra is growing with the amount of proteomic data generated in high-throughput experiments. These reference libraries provide a fast and accurate way to identify newly acquired MS/MS spectra. In the context of multiple hypotheses testing, the control of the number of false-positive identifications expected in the final result list by means of the calculation of the false discovery rate (FDR). In a classical sequence search where experimental MS/MS spectra are compared with the theoretical peptide spectra calculated from a sequence database, the FDR is estimated by searching randomized or decoy sequence databases. Despite on-going discussion on how exactly the FDR has to be calculated, this method is widely accepted in the proteomic community. Recently, similar approaches to control the FDR of spectrum library searches were discussed. We present in this paper a detailed analysis of the similarity between spectra of distinct peptides to set the basis of our own solution for decoy library creation (DeLiberator). It differs from the previously published results in some key points, mainly in implementing new methods that prevent decoy spectra from being too similar to the original library spectra while keeping important features of real MS/MS spectra. Using different proteomic data sets and library creation methods, we evaluate our approach and compare it with alternative methods.     

Improving mass and liquid chromatography based identification of proteins using bayesian scoring

We present a method for peptide and protein identification based on LC-MS profiling. The method identified peptides at high-throughput without expending the sequencing time necessary for CID spectra based identification. The measurable peptide properties of mass and liquid chromatographic elution conditions are used to characterize and differentiate peptide features, and these peptide features are matched to a reference database from previously acquired and archived LC-MS/MS experiments to generate sequence assignments. The matches are scored according to the probability of an overlap between the peptide feature and the database peptides resulting in a ranked list of possible peptide sequences for each peptide submitted. This method resulted in 6 times more peptide sequence identifications from a single LC-MS analysis of yeast than from shotgun peptide sequencing using LC-MS/MS.     

Partially sequenced organisms, decoy searches and false discovery rates

Tandem mass spectrometry is commonly used to identify peptides, typically by comparing their product ion spectra with those predicted from a protein sequence database and scoring these matches. The most reported quality metric for a set of peptide identifications is the false discovery rate (FDR), the fraction of expected false identifications in the set. This metric has so far only been used for completely sequenced organisms or known protein mixtures. We have investigated whether FDR estimations are also applicable in the case of partially sequenced organisms, where many high-quality spectra fail to identify the correct peptides because the latter are not present in the searched sequence database. Using real data from human plasma and simulated partial sequence databases derived from two complete human sequence databases with different levels of redundancy, we could demonstrate that the mixture model approach in PeptideProphet is robust for partial databases, particularly if used in combination with decoy sequences. We therefore recommend using this method when estimating the FDR and reporting peptide identifications from incompletely sequenced organisms.     

BuildSummary: using a group-based approach to improve the sensitivity of peptide/protein identification in shotgun proteomics

The target-decoy database search strategy is widely accepted as a standard method for estimating the false discovery rate (FDR) of peptide identification, based on which peptide-spectrum matches (PSMs) from the target database are filtered. To improve the sensitivity of protein identification given a fixed accuracy (frequently defined by a protein FDR threshold), a postprocessing procedure is often used that integrates results from different peptide search engines that had assayed the same data set. In this work, we show that PSMs that are grouped by the precursor charge, the number of missed internal cleavage sites, the modification state, and the numbers of protease termini and that the proteins grouped by their unique peptide count should be filtered separately according to the given FDR. We also develop an iterative procedure to filter the PSMs and proteins simultaneously, according to the given FDR. Finally, we present a general framework to integrate the results from different peptide search engines using the same FDR threshold. Our method was tested with several shotgun proteomics data sets that were acquired by multiple LC/MS instruments from two different biological samples. The results showed a satisfactory performance. We implemented the method in a user-friendly software package called BuildSummary, which can be downloaded for free from http://www.proteomics.ac.cn/software/proteomicstools/index.htm as part of the software suite ProteomicsTools.     

Tandem mass spectrometry for the detection of plant pathogenic fungi and the effects of database composition on protein inferences

LC-MS/MS has demonstrated potential for detecting plant pathogens. Unlike PCR or ELISA, LC-MS/MS does not require pathogen-specific reagents for the detection of pathogen-specific proteins and peptides. However, the MS/MS approach we and others have explored does require a protein sequence reference database and database-search software to interpret tandem mass spectra. To evaluate the limitations of database composition on pathogen identification, we analyzed proteins from cultured Ustilago maydis, Phytophthora sojae, Fusarium graminearum, and Rhizoctonia solani by LC-MS/MS. When the search database did not contain sequences for a target pathogen, or contained sequences to related pathogens, target pathogen spectra were reliably matched to protein sequences from nontarget organisms, giving an illusion that proteins from nontarget organisms were identified. Our analysis demonstrates that when database-search software is used as part of the identification process, a paradox exists whereby additional sequences needed to detect a wide variety of possible organisms may lead to more cross-species protein matches and misidentification of pathogens.     

Generalized method for probability-based peptide and protein identification from tandem mass spectrometry data and sequence database searching

Tandem mass spectrometry-based proteomics is currently in great demand of computational methods that facilitate the elimination of likely false positives in peptide and protein identification. In the last few years, a number of new peptide identification programs have been described, but scores or other significance measures reported by these programs cannot always be directly translated into an easy to interpret error rate measurement such as the false discovery rate. In this work we used generalized lambda distributions to model frequency distributions of database search scores computed by MASCOT, X!TANDEM with k-score plug-in, OMSSA, and InsPecT. From these distributions, we could successfully estimate p values and false discovery rates with high accuracy. From the set of peptide assignments reported by any of these engines, we also defined a generic protein scoring scheme that enabled accurate estimation of protein-level p values by simulation of random score distributions that was also found to yield good estimates of protein-level false discovery rate. The performance of these methods was evaluated by searching four freely available data sets ranging from 40,000 to 285,000 MS/MS spectra.     

CHOMPER: a bioinformatic tool for rapid validation of tandem mass spectrometry search results associated with high-throughput proteomic strategies

Current efforts aimed at developing high-throughput proteomics focus on increasing the speed of protein identification. Although improvements in sample separation, enrichment, automated handling, mass spectrometric analysis, as well as data reduction and database interrogation strategies have done much to increase the quality, quantity and efficiency of data collection, significant bottlenecks still exist. Various separation techniques have been coupled with tandem mass spectrometric (MS/MS) approaches to allow a quicker analysis of complex mixtures of proteins, especially where a high number of unambiguous protein identifications are the exception, rather than the rule. MS/MS is required to provide structural / amino acid sequence information on a peptide and thus allow protein identity to be inferred from individual peptides. Currently these spectra need to be manually validated because: (a) the potential of false positive matches i.e., protein not in database, and (b) observed fragmentation trends may not be incorporated into current MS/MS search algorithms. This validation represents a significant bottleneck associated with high-throughput proteomic strategies. We have developed CHOMPER, a software program which reduces the time required to both visualize and confirm MS/MS search results and generate post-analysis reports and protein summary tables. CHOMPER extracts the identification information from SEQUEST MS/MS search result files, reproduces both the peptide and protein identification summaries, provides a more interactive visualization of the MS/MS spectra and facilitates the direct submission of manually validated identifications to a database.     

Refining similarity scoring to enable decoy‐free validation in spectral library searching

Spectral library searching is a maturing approach for peptide identification from MS/MS, offering an alternative to traditional sequence database searching. Spectral library searching relies on direct spectrum‐to‐spectrum matching between the query data and the spectral library, which affords better discrimination of true and false matches, leading to improved sensitivity. However, due to the inherent diversity of the peak location and intensity profiles of real spectra, the resulting similarity score distributions often take on unpredictable shapes. This makes it difficult to model the scores of the false matches accurately, necessitating the use of decoy searching to sample the score distribution of the false matches. Here, we refined the similarity scoring in spectral library searching to enable the validation of spectral search results without the use of decoys. We rank‐transformed the peak intensities to standardize all spectra, making it possible to fit a parametric distribution to the scores of the nontop‐scoring spectral matches. The statistical significance of the top‐scoring match can then be estimated in a rigorous manner according to Extreme Value Theory. The overall result is a more robust and interpretable measure of the quality of the spectral match, which can be obtained without decoys. We tested this refined similarity scoring function on real datasets and demonstrated its effectiveness. This approach reduces search time, increases sensitivity, and extends spectral library searching to situations where decoy spectra cannot be readily generated, such as in searching unidentified and nonpeptide spectral libraries.