Speeding up tandem mass spectrometry-based database searching by longest common prefix

Background  

Tandem mass spectrometry-based database searching has become an important technology for peptide and protein identification. One of the key challenges in database searching is the remarkable increase in computational demand, brought about by the expansion of protein databases, semi- or non-specific enzymatic digestion, post-translational modifications and other factors. Some software tools choose peptide indexing to accelerate processing. However, peptide indexing requires a large amount of time and space for construction, especially for the non-specific digestion. Additionally, it is not flexible to use.     

Validation of endogenous peptide identifications using a database of tandem mass spectra

The SwePep database is designed for endogenous peptides and mass spectrometry. It contains information about the peptides such as mass, pl, precursor protein and potential post-translational modifications. Here, we have improved and extended the SwePep database with tandem mass spectra, by adding a locally curated version of the global proteome machine database (GPMDB). In peptidomic experiment practice, many peptide sequences contain multiple tandem mass spectra with different quality. The new tandem mass spectra database in SwePep enables validation of low quality spectra using high quality tandem mass spectra. The validation is performed by comparing the fragmentation patterns of the two spectra using algorithms for calculating the correlation coefficient between the spectra. The present study is the first step in developing a tandem spectrum database for endogenous peptides that can be used for spectrum-to-spectrum identifications instead of peptide identifications using traditional protein sequence database searches.     

Faster SEQUEST searching for peptide identification from tandem mass spectra

Computational analysis of mass spectra remains the bottleneck in many proteomics experiments. SEQUEST was one of the earliest software packages to identify peptides from mass spectra by searching a database of known peptides. Though still popular, SEQUEST performs slowly. Crux and TurboSEQUEST have successfully sped up SEQUEST by adding a precomputed index to the search, but the demand for ever-faster peptide identification software continues to grow. Tide, introduced here, is a software program that implements the SEQUEST algorithm for peptide identification and that achieves a dramatic speedup over Crux and SEQUEST. The optimization strategies detailed here employ a combination of algorithmic and software engineering techniques to achieve speeds up to 170 times faster than a recent version of SEQUEST that uses indexing. For example, on a single Xeon CPU, Tide searches 10,000 spectra against a tryptic database of 27,499 Caenorhabditis elegans proteins at a rate of 1550 spectra per second, which compares favorably with a rate of 8.8 spectra per second for a recent version of SEQUEST with index running on the same hardware.     

ProbID: a probabilistic algorithm to identify peptides through sequence database searching using tandem mass spectral data

With the recent quick expansion of DNA and protein sequence databases, intensive efforts are underway to interpret the linear genetic information of DNA in terms of function, structure, and control of biological processes. The systematic identification and quantification of expressed proteins has proven particularly powerful in this regard. Large-scale protein identification is usually achieved by automated liquid chromatography-tandem mass spectrometry of complex peptide mixtures and sequence database searching of the resulting spectra [Aebersold and Goodlett, Chem. Rev. 2001, 101, 269-295]. As generating large numbers of sequence-specific mass spectra (collision-induced dissociation/CID) spectra has become a routine operation, research has shifted from the generation of sequence database search results to their validation. Here we describe in detail a novel probabilistic model and score function that ranks the quality of the match between tandem mass spectral data and a peptide sequence in a database. We document the performance of the algorithm on a reference data set and in comparison with another sequence database search tool. The software is publicly available for use and evaluation at http://www.systemsbiology.org/research/software/proteomics/ProbID.     

Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.     

A mass accuracy sensitive probability based scoring algorithm for database searching of tandem mass spectrometry data

Background  

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has become one of the most used tools in mass spectrometry based proteomics. Various algorithms have since been developed to automate the process for modern high-throughput LC-MS/MS experiments.     

SCOPE: a probabilistic model for scoring tandem mass spectra against a peptide database

Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model.     

Fast parallel tandem mass spectral library searching using GPU hardware acceleration

Mass spectrometry-based proteomics is a maturing discipline of biologic research that is experiencing substantial growth. Instrumentation has steadily improved over time with the advent of faster and more sensitive instruments collecting ever larger data files. Consequently, the computational process of matching a peptide fragmentation pattern to its sequence, traditionally accomplished by sequence database searching and more recently also by spectral library searching, has become a bottleneck in many mass spectrometry experiments. In both of these methods, the main rate-limiting step is the comparison of an acquired spectrum with all potential matches from a spectral library or sequence database. This is a highly parallelizable process because the core computational element can be represented as a simple but arithmetically intense multiplication of two vectors. In this paper, we present a proof of concept project taking advantage of the massively parallel computing available on graphics processing units (GPUs) to distribute and accelerate the process of spectral assignment using spectral library searching. This program, which we have named FastPaSS (for Fast Parallelized Spectral Searching), is implemented in CUDA (Compute Unified Device Architecture) from NVIDIA, which allows direct access to the processors in an NVIDIA GPU. Our efforts demonstrate the feasibility of GPU computing for spectral assignment, through implementation of the validated spectral searching algorithm SpectraST in the CUDA environment.     

Spectral dictionaries: Integrating de novo peptide sequencing with database search of tandem mass spectra

Database search tools identify peptides by matching tandem mass spectra against a protein database. We study an alternative approach when all plausible de novo interpretations of a spectrum (spectral dictionary) are generated and then quickly matched against the database. We present a new MS-Dictionary algorithm for efficiently generating spectral dictionaries and demonstrate that MS-Dictionary can identify spectra that are missed in the database search. We argue that MS-Dictionary enables proteogenomics searches in six-frame translation of genomic sequences that may be prohibitively time-consuming for existing database search approaches. We show that such searches allow one to correct sequencing errors and find programmed frameshifts.     

Gapped spectral dictionaries and their applications for database searches of tandem mass spectra

Generating all plausible de novo interpretations of a peptide tandem mass (MS/MS) spectrum (Spectral Dictionary) and quickly matching them against the database represent a recently emerged alternative approach to peptide identification. However, the sizes of the Spectral Dictionaries quickly grow with the peptide length making their generation impractical for long peptides. We introduce Gapped Spectral Dictionaries (all plausible de novo interpretations with gaps) that can be easily generated for any peptide length thus addressing the limitation of the Spectral Dictionary approach. We show that Gapped Spectral Dictionaries are small thus opening a possibility of using them to speed-up MS/MS searches. Our MS-Gapped-Dictionary algorithm (based on Gapped Spectral Dictionaries) enables proteogenomics applications (such as searches in the six-frame translation of the human genome) that are prohibitively time consuming with existing approaches. MS-Gapped-Dictionary generates gapped peptides that occupy a niche between accurate but short peptide sequence tags and long but inaccurate full length peptide reconstructions. We show that, contrary to conventional wisdom, some high-quality spectra do not have good peptide sequence tags and introduce gapped tags that have advantages over the conventional peptide sequence tags in MS/MS database searches.     

Speeding up tandem mass spectrometry database search: metric embeddings and fast near neighbor search

MOTIVATION: Due to the recent advances in technology of mass spectrometry, there has been an exponential increase in the amount of data being generated in the past few years. Database searches have not been able to keep with this data explosion. Thus, speeding up the data searches becomes increasingly important in mass-spectrometry-based applications. Traditional database search methods use one-against-all comparisons of a query spectrum against a very large number of peptides generated from in silico digestion of protein sequences in a database, to filter potential candidates from this database followed by a detailed scoring and ranking of those filtered candidates. RESULTS: In this article, we show that we can avoid the one-against-all comparisons. The basic idea is to design a set of hash functions to pre-process peptides in the database such that for each query spectrum we can use the hash functions to find only a small subset of peptide sequences that are most likely to match the spectrum. The construction of each hash function is based on a random spectrum and the hash value of a peptide is the normalized shared peak counts score (cosine) between the random spectrum and the hypothetical spectrum of the peptide. To implement this idea, we first embed each peptide into a unit vector in a high-dimensional metric space. The random spectrum is represented by a random vector, and we use random vectors to construct a set of hash functions called locality sensitive hashing (LSH) for preprocessing. We demonstrate that our mapping is accurate. We show that our method can filter out >95.65% of the spectra without missing any correct sequences, or gain 111 times speedup by filtering out 99.64% of spectra while missing at most 0.19% (2 out of 1014) of the correct sequences. In addition, we show that our method can be effectively used for other mass spectra mining applications such as finding clusters of spectra efficiently and accurately. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

DSEARCH: sensitive database searching using distributed computing

SUMMARY: We present a distributed and fully cross-platform database search program that allows the user to utilize the idle clock cycles of machines to perform large searches using the most sensitive algorithms. For those in an academic or corporate environment with hundreds of idle desktop machines, DSEARCH can deliver a 'free' database search supercomputer. AVAILABILITY: The software is publicly available under the GNU general public licence from http://www.cs.may.ie/distributed CONTACT: tom.naughton@may.ie SUPPLEMENTARY INFORMATION: Full documentation and a user manual is available from http://www.cs.may.ie/distributed.     

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.     

Clustering millions of tandem mass spectra

Tandem mass spectrometry (MS/MS) experiments often generate redundant data sets containing multiple spectra of the same peptides. Clustering of MS/MS spectra takes advantage of this redundancy by identifying multiple spectra of the same peptide and replacing them with a single representative spectrum. Analyzing only representative spectra results in significant speed-up of MS/MS database searches. We present an efficient clustering approach for analyzing large MS/MS data sets (over 10 million spectra) with a capability to reduce the number of spectra submitted to further analysis by an order of magnitude. The MS/MS database search of clustered spectra results in fewer spurious hits to the database and increases number of peptide identifications as compared to regular nonclustered searches. Our open source software MS-Clustering is available for download at http://peptide.ucsd.edu or can be run online at http://proteomics.bioprojects.org/MassSpec.     

Integrated approach for manual evaluation of peptides identified by searching protein sequence databases with tandem mass spectra

Quantitative proteomics relies on accurate protein identification, which often is carried out by automated searching of a sequence database with tandem mass spectra of peptides. When these spectra contain limited information, automated searches may lead to incorrect peptide identifications. It is therefore necessary to validate the identifications by careful manual inspection of the mass spectra. Not only is this task time-consuming, but the reliability of the validation varies with the experience of the analyst. Here, we report a systematic approach to evaluating peptide identifications made by automated search algorithms. The method is based on the principle that the candidate peptide sequence should adequately explain the observed fragment ions. Also, the mass errors of neighboring fragments should be similar. To evaluate our method, we studied tandem mass spectra obtained from tryptic digests of E. coli and HeLa cells. Candidate peptides were identified with the automated search engine Mascot and subjected to the manual validation method. The method found correct peptide identifications that were given low Mascot scores (e.g., 20-25) and incorrect peptide identifications that were given high Mascot scores (e.g., 40-50). The method comprehensively detected false results from searches designed to produce incorrect identifications. Comparison of the tandem mass spectra of synthetic candidate peptides to the spectra obtained from the complex peptide mixtures confirmed the accuracy of the evaluation method. Thus, the evaluation approach described here could help boost the accuracy of protein identification, increase number of peptides identified, and provide a step toward developing a more accurate next-generation algorithm for protein identification.     

TANDEM: matching proteins with tandem mass spectra

SUMMARY: Tandem mass spectra obtained from fragmenting peptide ions contain some peptide sequence specific information, but often there is not enough information to sequence the original peptide completely. Several proprietary software applications have been developed to attempt to match the spectra with a list of protein sequences that may contain the sequence of the peptide. The application TANDEM was written to provide the proteomics research community with a set of components that can be used to test new methods and algorithms for performing this type of sequence-to-data matching. AVAILABILITY: The source code and binaries for this software are available at http://www.proteome.ca/opensource.html, for Windows, Linux and Macintosh OSX. The source code is made available under the Artistic License, from the authors.     

Error tolerant searching of uninterpreted tandem mass spectrometry data

An error tolerant mode for database matching of uninterpreted tandem mass spectrometry data is described. Selected database entries are searched without enzyme specificity, using a comprehensive list of chemical and post-translational modifications, together with a residue substitution matrix. The modifications are tested serially, to avoid the catastrophic loss of discrimination that would occur if all the permutations of large numbers of modifications in combination were possible. The new mode has been coded as an extension to the Mascot search engine, and tested against a number of Liquid chromatography-tandem mass spectrometry datasets. The results show a number of additional peptide matches, but require careful interpretation. The most significant limitation of this approach is that it can only reveal new matches to proteins that already have at least one significant peptide match.     

Parallel tandem: a program for parallel processing of tandem mass spectra using PVM or MPI and X!Tandem

A method for the rapid correlation of tandem mass spectra to a list of protein sequences in a database has been developed. The combination of the fast and accurate computational search algorithm, X!Tandem, and a Linux cluster parallel computing environment with PVM or MPI, significantly reduces the time required to perform the correlation of tandem mass spectra to protein sequences in a database. A file of tandem mass spectra is divided into a specified number of files, each containing an equal number of the spectra from the larger file. These files are then searched in parallel against a protein sequence database. The results of each parallel output file are collated into one file for viewing through a web interface. Thousands of spectra can be searched in an accurate, practical, and time effective manner. The source code for running Parallel Tandem utilizing either PVM or MPI on Linux operating system is available from http://www.thegpm.org. This source code is made available under Artistic License from the authors.     

Automatic quality assessment of peptide tandem mass spectra

MOTIVATION: A powerful proteomics methodology couples high-performance liquid chromatography (HPLC) with tandem mass spectrometry and database-search software, such as SEQUEST. Such a set-up, however, produces a large number of spectra, many of which are of too poor quality to be useful. Hence a filter that eliminates poor spectra before the database search can significantly improve throughput and robustness. Moreover, spectra judged to be of high quality, but that cannot be identified by database search, are prime candidates for still more computationally intensive methods, such as de novo sequencing or wider database searches including post-translational modifications. RESULTS: We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.     

Determination of glycan structure from tandem mass spectra

Glycans are molecules made from simple sugars that form complex tree structures. Glycans constitute one of the most important protein modifications and identification of glycans remains a pressing problem in biology. Unfortunately, the structure of glycans is hard to predict from the genome sequence of an organism. In this paper, we consider the problem of deriving the topology of a glycan solely from tandem mass spectrometry (MS) data. We study, how to generate glycan tree candidates that sufficiently match the sample mass spectrum, avoiding the combinatorial explosion of glycan structures. Unfortunately, the resulting problem is known to be computationally hard. We present an efficient exact algorithm for this problem based on fixed-parameter algorithmics that can process a spectrum in a matter of seconds. We also report some preliminary results of our method on experimental data, combining it with a preliminary candidate evaluation scheme. We show that our approach is fast in applications, and that we can reach very well de novo identification results. Finally, we show how to count the number of glycan topologies for a fixed size or a fixed mass. We generalize this result to count the number of (labeled) trees with bounded out degree, improving on results obtained using Pólya's enumeration theorem.