A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data

Background  

Alternative splicing is an important gene regulation mechanism. It is estimated that about 74% of multi-exon human genes have alternative splicing. High throughput tandem (MS/MS) mass spectrometry provides valuable information for rapidly identifying potentially novel alternatively-spliced protein products from experimental datasets. However, the ability to identify alternative splicing events through tandem mass spectrometry depends on the database against which the spectra are searched.     

Flying blind,or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

Mass spectrometry‐based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph‐based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.     

Gaining knowledge from previously unexplained spectra-application of the PTM-Explorer software to detect PTM in HUPO BPP MS/MS data

A novel software tool named PTM-Explorer has been applied to LC-MS/MS datasets acquired within the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP). PTM-Explorer enables automatic identification of peptide MS/MS spectra that were not explained in typical sequence database searches. The main focus was detection of PTMs, but PTM-Explorer detects also unspecific peptide cleavage, mass measurement errors, experimental modifications, amino acid substitutions, transpeptidation products and unknown mass shifts. To avoid a combinatorial problem the search is restricted to a set of selected protein sequences, which stem from previous protein identifications using a common sequence database search. Prior to application to the HUPO BPP data, PTM-Explorer was evaluated on excellently manually characterized and evaluated LC-MS/MS data sets from Alpha-A-Crystallin gel spots obtained from mouse eye lens. Besides various PTMs including phosphorylation, a wealth of experimental modifications and unspecific cleavage products were successfully detected, completing the primary structure information of the measured proteins. Our results indicate that a large amount of MS/MS spectra that currently remain unidentified in standard database searches contain valuable information that can only be elucidated using suitable software tools.     

Peptide charge state determination of tandem mass spectra from low-resolution collision induced dissociation

Background

Charge states of tandem mass spectra from low-resolution collision induced dissociation can not be determined by mass spectrometry. As a result, such spectra with multiple charges are usually searched multiple times by assuming each possible charge state. Not only does this strategy increase the overall database search time, but also yields more false positives. Hence, it is advantageous to determine charge states of such spectra before database search.

Results

We propose a new approach capable of determining the charge states of low-resolution tandem mass spectra. Four novel and discriminant features are introduced to describe tandem mass spectra and used in Gaussian mixture model to distinguish doubly and triply charged peptides. By testing on three independent datasets with known validity, the results have shown that this method can assign charge states to low-resolution tandem mass spectra more accurately than existing methods.

Conclusions

The proposed method can be used to improve the speed and reliability of peptide identification.

     

Pripper: prediction of caspase cleavage sites from whole proteomes

Background  

Caspases are a family of proteases that have central functions in programmed cell death (apoptosis) and inflammation. Caspases mediate their effects through aspartate-specific cleavage of their target proteins, and at present almost 400 caspase substrates are known. There are several methods developed to predict caspase cleavage sites from individual proteins, but currently none of them can be used to predict caspase cleavage sites from multiple proteins or entire proteomes, or to use several classifiers in combination. The possibility to create a database from predicted caspase cleavage products for the whole genome could significantly aid in identifying novel caspase targets from tandem mass spectrometry based proteomic experiments.     

A novel scoring schema for peptide identification by searching protein sequence databases using tandem mass spectrometry data

Background  

Tandem mass spectrometry (MS/MS) is a powerful tool for protein identification. Although great efforts have been made in scoring the correlation between tandem mass spectra and an amino acid sequence database, improvements could be made in three aspects, including characterization ofpeaks in spectra, adoption of effective scoring functions and access to thereliability of matching between peptides and spectra.     

A high confidence, manually validated human blood plasma protein reference set

Background

The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods

To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results

Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion

Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.     

An unsupervised machine learning method for assessing quality of tandem mass spectra

Background

In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.

Results

This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.

Conclusions

Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.

     

Identification of polymorphic tandem repeats by direct comparison of genome sequence from different bacterial strains : a web-based resource

Background

Polymorphic tandem repeat typing is a new generic technology which has been proved to be very efficient for bacterial pathogens such as B. anthracis, M. tuberculosis, P. aeruginosa, L. pneumophila, Y. pestis. The previously developed tandem repeats database takes advantage of the release of genome sequence data for a growing number of bacteria to facilitate the identification of tandem repeats. The development of an assay then requires the evaluation of tandem repeat polymorphism on well-selected sets of isolates. In the case of major human pathogens, such as S. aureus, more than one strain is being sequenced, so that tandem repeats most likely to be polymorphic can now be selected in silico based on genome sequence comparison.

Results

In addition to the previously described general Tandem Repeats Database, we have developed a tool to automatically identify tandem repeats of a different length in the genome sequence of two (or more) closely related bacterial strains. Genome comparisons are pre-computed. The results of the comparisons are parsed in a database, which can be conveniently queried over the internet according to criteria of practical value, including repeat unit length, predicted size difference, etc. Comparisons are available for 16 bacterial species, and the orthopox viruses, including the variola virus and three of its close neighbors.

Conclusions

We are presenting an internet-based resource to help develop and perform tandem repeats based bacterial strain typing. The tools accessible at http://minisatellites.u-psud.fr now comprise four parts. The Tandem Repeats Database enables the identification of tandem repeats across entire genomes. The Strain Comparison Page identifies tandem repeats differing between different genome sequences from the same species. The "Blast in the Tandem Repeats Database" facilitates the search for a known tandem repeat and the prediction of amplification product sizes. The "Bacterial Genotyping Page" is a service for strain identification at the subspecies level.

     

HybGFS: a hybrid method for genome-fingerprint scanning

Background  

Protein identification based on mass spectrometry (MS) has previously been performed using peptide mass fingerprinting (PMF) or tandem MS (MS/MS) database searching. However, these methods cannot identify proteins that are not already listed in existing databases. Moreover, the alternative approach of de novo sequencing requires costly equipment and the interpretation of complex MS/MS spectra. Thus, there is a need for novel high-throughput protein-identification methods that are independent of existing predefined protein databases.     

Development of an integrative database with 499 novel microsatellite markers for Macaca fascicularis

Background

Cynomolgus macaques (Macaca fascicularis) are a valuable resource for linkage studies of genetic disorders, but their microsatellite markers are not sufficient. In genetic studies, a prerequisite for mapping genes is development of a genome-wide set of microsatellite markers in target organisms. A whole genome sequence and its annotation also facilitate identification of markers for causative mutations. The aim of this study is to establish hundreds of microsatellite markers and to develop an integrative cynomolgus macaque genome database with a variety of datasets including marker and gene information that will be useful for further genetic analyses in this species.

Results

We investigated the level of polymorphisms in cynomolgus monkeys for 671 microsatellite markers that are covered by our established Bacterial Artificial Chromosome (BAC) clones. Four hundred and ninety-nine (74.4%) of the markers were found to be polymorphic using standard PCR analysis. The average number of alleles and average expected heterozygosity at these polymorphic loci in ten cynomolgus macaques were 8.20 and 0.75, respectively.

Conclusion

BAC clones and novel microsatellite markers were assigned to the rhesus genome sequence and linked with our cynomolgus macaque cDNA database (QFbase). Our novel microsatellite marker set and genomic database will be valuable integrative resources in analyzing genetic disorders in cynomolgus macaques.     

A graph-based filtering method for top-down mass spectral identification

Background

Database search has been the main approach for proteoform identification by top-down tandem mass spectrometry. However, when the target proteoform that produced the spectrum contains post-translational modifications (PTMs) and/or mutations, it is quite time consuming to align a query spectrum against all protein sequences without any PTMs and mutations in a large database. Consequently, it is essential to develop efficient and sensitive filtering algorithms for speeding up database search.

Results

In this paper, we propose a spectrum graph matching (SGM) based protein sequence filtering method for top-down mass spectral identification. It uses the subspectra of a query spectrum to generate spectrum graphs and searches them against a protein database to report the best candidates. As the sequence tag and gaped tag approaches need the preprocessing step to extract and select tags, the SGM filtering method circumvents this preprocessing step, thus simplifying data processing. We evaluated the filtration efficiency of the SGM filtering method with various parameter settings on an Escherichia coli top-down mass spectrometry data set and compared the performances of the SGM filtering method and two tag-based filtering methods on a data set of MCF-7 cells.

Conclusions

Experimental results on the data sets show that the SGM filtering method achieves high sensitivity in protein sequence filtration. When coupled with a spectral alignment algorithm, the SGM filtering method significantly increases the number of identified proteoform spectrum-matches compared with the tag-based methods in top-down mass spectrometry data analysis.

     

Recent segmental and gene duplications in the mouse genome

Background

The high quality of the mouse genome draft sequence and its associated annotations are an invaluable biological resource. Identifying recent duplications in the mouse genome, especially in regions containing genes, may highlight important events in recent murine evolution. In addition, detecting recent sequence duplications can reveal potentially problematic regions of the genome assembly. We use BLAST-based computational heuristics to identify large (≥ 5 kb) and recent (≥ 90% sequence identity) segmental duplications in the mouse genome sequence. Here we present a database of recently duplicated regions of the mouse genome found in the mouse genome sequencing consortium (MGSC) February 2002 and February 2003 assemblies.

Results

We determined that 33.6 Mb of 2,695 Mb (1.2%) of sequence from the February 2003 mouse genome sequence assembly is involved in recent segmental duplications, which is less than that observed in the human genome (around 3.5-5%). From this dataset, 8.9 Mb (26%) of the duplication content consisted of 'unmapped' chromosome sequence. Moreover, we suspect that an additional 18.5 Mb of sequence is involved in duplication artifacts arising from sequence misassignment errors in this genome assembly. By searching for genes that are located within these regions, we identified 675 genes that mapped to duplicated regions of the mouse genome. Sixteen of these genes appear to have been duplicated independently in the human genome. From our dataset we further characterized a 42 kb recent segmental duplication of Mater, a maternal-effect gene essential for embryogenesis in mice.

Conclusion

Our results provide an initial analysis of the recently duplicated sequence and gene content of the mouse genome. Many of these duplicated loci, as well as regions identified to be involved in potential sequence misassignment errors, will require further mapping and sequencing to achieve accuracy. A Genome Browser database was set up to display the identified duplication content presented in this work. This data will also be relevant to the growing number of investigators who use the draft genome sequence for experimental design and analysis.

     

Template Proteogenomics: Sequencing Whole Proteins Using an Imperfect Database

Database search algorithms are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database, preventing the identification of peptides from mutated or alternatively spliced sequences. A variety of methods has been developed to search a spectrum against a sequence allowing for variations. Some tools determine the sequence of the homologous protein in the related species but do not report the peptide in the target organism. Other tools consider variations, including modifications and mutations, in reconstructing the target sequence. However, these tools will not work if the template (homologous peptide) is missing in the database, and they do not attempt to reconstruct the entire protein target sequence. De novo identification of peptide sequences is another possibility, because it does not require a protein database. However, the lack of database reduces the accuracy. We present a novel proteogenomic approach, GenoMS, that draws on the strengths of database and de novo peptide identification methods. Protein sequence templates (i.e. proteins or genomic sequences that are similar to the target protein) are identified using the database search tool InsPecT. The templates are then used to recruit, align, and de novo sequence regions of the target protein that have diverged from the database or are missing. We used GenoMS to reconstruct the full sequence of an antibody by using spectra acquired from multiple digests using different proteases. Antibodies are a prime example of proteins that confound standard database identification techniques. The mature antibody genes result from large-scale genome rearrangements with flexible fusion boundaries and somatic hypermutation. Using GenoMS we automatically reconstruct the complete sequences of two immunoglobulin chains with accuracy greater than 98% using a diverged protein database. Using the genome as the template, we achieve accuracy exceeding 97%.Database search algorithms, such as Sequest (1), Mascot (2), and InsPecT (3), are the primary workhorses for the identification of tandem mass spectra. However, these methods are limited to the identification of spectra for which peptides are present in the database. It is well recognized that curated protein databases are, at best, an imperfect template for the extant peptides. For example, peptides arising from novel splice forms or fusion proteins would be difficult to identify using most protein databases.Recent developments have extended the identifications to peptides that have diverged from the database entry. By allowing divergence, the methods enable the identification of small-scale mutations, and post-translational modifications, albeit with some loss of sensitivity (4–7). Among these tools, MS-Blast is able to determine a homologous protein in the related species but does not report the (diverged) protein in the target organism. The other tools consider variations, including modifications and mutations, in reconstructing the target sequence. However, these tools will not work if the template (homologous peptide) is missing in the database or comes from a novel splice form. In addition, these tools do not attempt to reconstruct the entire protein target sequence. De novo identification of peptide sequences (8, 9) is another possibility and does not require a protein database. However, these methods are prone to error.The issue of discovering spliced peptides (more generally, eukaryotic gene structures) has been investigated using a combination of approaches, loosely termed proteogenomics. Often, these approaches start by creating specialized databases of splice forms, combining evidence from protein (e.g. NCBI nr (10)) and cDNA sequencing (11–13). To discover novel splicing events, the tools also search databases derived directly from the genome such as a six-frame translation or a compact encoding of multiple putative splicing events (14–17). For example, Castellana et al. (15) achieved this by constructing a database, represented as a graph (16), containing many putative exons and exon splice junctions.However, this approach also has its shortcomings. The putative gene models are constructed based on prior assumptions about splice junctions and proximal exons. In addition, recent genomic discoveries point to extensive structural variation in the genome in the form of large-scale deletions, insertions, inversions, and translocations on the genome that might fuse different genic regions or create nonstandard splice forms (18, 19). Indeed, many cancers are characterized by such large-scale mutations of the genome (20). Other examples of variation that confound standard database identification techniques are immunoglobulins and antibodies. Here, recombination events fuse disparate regions of the genome, often inserting nontemplated sequence and creating many novel gene structures in every individual. The common theme in all of the scenarios described is that it is not possible to maintain all possible encodings in a database to allow for a standard proteogenomic search.In this study, we sought to determine whether the imperfect template provided by the genome can be still used as a basis for peptide (and protein) identification. We are motivated in our approach by the work of Bandeira et al. (21), who were able to sequence monoclonal antibodies de novo, making no use of a database at all. In their method, an all-to-all comparison of spectra allowed the creation of spectral contigs, similar to sequence contigs in shotgun sequencing projects. The sequences of the spectral contigs were determined de novo. Using full antibody sequences as references, they were able to order the contigs and infer the missing sequence. Because the construction and sequencing of the contigs was performed completely de novo, Bandeira et al. (21) were able to sequence highly divergent proteins or proteins for which there is no database. However, the ordering of the sequenced contigs relies on a database of full antibody sequences for mapping. Sequences that cannot be mapped to an antibody in the database may be discarded. In contrast, the templates used in our method are not full proteins, but substrings of proteins, such as exons, which are combinatorially chained together to best explain the spectrometric evidence.Liu et al. (22) have developed Champs, a method for sequencing a divergent protein using a homologous protein database. In their method, a single reference protein was chosen, and the de novo interpretations of spectra were mapped to the reference. They were able to sequence a protein with high accuracy using a reference protein with only 77% similarity to the target. Although Champs is able to map peptides that differ from the reference by one or two amino acids, it does not look for large insertions or deletions in the target sequence, as in a novel splice form. In our work, use of the database as an incomplete template lends additional confidence to the target sequencing without substantially limiting the ability to identify diverged sequences.Here, we describe a novel method for template proteogenomics, implemented in the tool GenoMS. GenoMS takes as input a collection of spectra (acquired from multiple protease digests) and a collection of imperfect templates and constraints (defined under Experimental Procedures). It returns a target protein sequence. At the heart of the approach is a novel method of extending a target amino acid sequence by recruiting and aligning spectra that match it partially. By using spectral data sets with multiple protease digests, we are able to identify many overlapping peptides. We then align the overlapping spectra and produce an extended consensus spectrum. We are able to extend 89% of the target amino acid sequences. More than 40% of these extensions are three or more amino acids.We test the performance of GenoMS in reconstructing monoclonal antibody sequences. Antibodies are an interesting test case because of their highly variable nature and because no complete antibody database exists. They are composed of four polypeptide chains: two identical heavy chains and two identical light chains (Fig. 1). An antibody''s preference and efficiency in the detection and removal of encountered antigens is heavily dependent on its amino acid sequence. Consequently, antibodies are extremely diverse. A principal way in which antibody diversity is achieved is through genome rearrangement of the germline locus (Fig. 1). An antibody''s heavy chain comprises four gene segments; a variable (V) segment, a diversity (D) segment, a joining (J) segment, and a constant (C) segment. Likewise, the light chain is composed of three gene segments: a V segment, a D segment, and a C segment. Each segment is chosen from potentially hundreds present in the genome, and many combinations of gene segments may be joined. Imprecise boundaries with the possible insertion of additional nucleotides allow the creation of many sequences from a single germline locus. Somatic hypermutation also plays a role in achieving antibody diversity. Although antibody sequence may be determined by sequencing the DNA of the source cell line, few direct protein-sequencing options exist when the source is unavailable or for ensuring antibody integrity. The antibody structure provides enough complexity to serve as a test case for template proteogenomics.Open in a separate windowFig. 1.An overview of the production of a mature immunoglobulin. Bottom, the mature immunoglobulin protein structure contains two identical light chains and two identical heavy chains. The germline heavy-chain and light-chain loci (top) contain many different gene segments. During heavy-chain gene rearrangement, in B-cell differentiation, one V, one D, and one J gene segment are combined. For light-chain gene formation, a V and a J gene segment are combined. The combined VDJ or VJ segments are joined by splice junction to a constant region.Using the technique of extending the peptide sequence without reference to a database, we are able to reconstruct the full protein sequence for the antibody raised against the B- and T-lymphocyte attenuator molecule (aBTLA¹) (21). We also test our approach by using an available data set of spectra acquired using multiple protease digests for bovine serum album (BSA). The sequence of BSA is determined using the bovine genome as a template database. Both chains of aBTLA were sequenced using unrearranged gene segments as templates. An independent reconstruction of the aBTLA heavy chain was performed using the unrearranged heavy-chain genomic locus as a template.     

A model of random mass-matching and its use for automated significance testing in mass spectrometric proteome analysis

A rapid and accurate method for testing the significance of protein identities determined by mass spectrometric analysis of protein digests and genome database searching is presented. The method is based on direct computation using a statistical model of the random matching of measured and theoretical proteolytic peptide masses. Protein identification algorithms typically rank the proteins of a genome database according to a score based on the number of matches between the masses obtained by mass spectrometry analysis and the theoretical proteolytic peptide masses of a database protein. The random matching of experimental and theoretical masses can cause false results. A result is significant only if the score characterizing the result deviates significantly from the score expected from a false result. A distribution of the score (number of matches) for random (false) results is computed directly from our model of the random matching, which allows significance testing under any experimental and database search constraints. In order to mimic protein identification data quality in large-scale proteome projects, low-to-high quality proteolytic peptide mass data were generated in silico and subsequently submitted to a database search program designed to include significance testing based on direct computation. This simulation procedure demonstrates the usefulness of direct significance testing for automatically screening for samples that must be subjected to peptide sequence analysis by e.g. tandem mass spectrometry in order to determine the protein identity.