Figures
Abstract
Alternative splicing (AS) is an intrinsic regulatory mechanism of all metazoans. Recent findings suggest that 100% of multiexonic human genes give rise to splice isoforms. AS can be specific to tissue type, environment or developmentally regulated. Splice variants have also been implicated in various diseases including cancer. Detection of these variants will enhance our understanding of the complexity of the human genome and provide disease-specific and prognostic biomarkers. We adopted a proteomics approach to identify exon skip events - the most common form of AS. We constructed a database harboring the peptide sequences derived from all hypothetical exon skip junctions in the human genome. Searching tandem mass spectrometry (MS/MS) data against the database allows the detection of exon skip events, directly at the protein level. Here we describe the application of this approach to human platelets, including the mRNA-based verification of novel splice isoforms of ITGA2, NPEPPS and FH. This methodology is applicable to all new or existing MS/MS datasets.
Citation: Power KA, McRedmond JP, de Stefani A, Gallagher WM, Ó Gaora P (2009) High-Throughput Proteomics Detection of Novel Splice Isoforms in Human Platelets. PLoS ONE 4(3): e5001. https://doi.org/10.1371/journal.pone.0005001
Editor: Cathal Seoighe, University of Cape Town, South Africa
Received: November 24, 2008; Accepted: February 20, 2009; Published: March 24, 2009
Copyright: © 2009 Power et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Funding is acknowledged from the EU FP6 Integrated Project, InnoMed (in particular, the PredTox subgroup; http://www.innomed-predtox.com), as well as University College Dublin under its Research Demonstratorship scheme (for part-support of KAP). The UCD Conway Institute is funded by the Programme for Research in Third Level Institutions, as administered by the Higher Education Authority of Ireland. The work was also supported by funding from Science Foundation Ireland (04/BR/B0527) to JMcR and the Strategic Research Cluster, Network of Excellence of Functional Biomaterials, to WMG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Since the publication of the human genome sequence, understanding the functional complexity of the genome has become a primary goal of high-throughput experimental research. By definition, AS contributes to proteomic complexity but it has also been suggested that AS is a major driver of phenotypic complexity, though this role remains unproven [1]–[3]. By splicing several combinations of exons into different transcripts, AS generates, from a single gene, multiple isoforms of a protein with potentially diverse functions. Not only has AS been invoked as an explanation for our complexity as a species, detection of splice isoforms has been associated with the cause and progression of certain diseases. Alternative splicing is associated with a wide variety of conditions including bipolar disorder, schizophrenia, cancer, diabetes, multiple sclerosis, cystic fibrosis and asthma (for a review see Wang & Cooper [4]). Splice isoforms may be functionally relevant in disease or may act as biomarkers - indicators of normal or altered biological processes or pharmacological response to a therapeutic intervention [5]. Biomarkers such as disease-specific AS isoforms can serve as indicators of disease susceptibility as well as diagnostic and prognostic markers.
Alternative splicing occurs in many cell types including platelets - hemostatic, anucleate cells derived from megakaryocytes. Although devoid of a nucleus, they retain low levels of mRNA which undergo translation. They have an intact spliceosome and cellular activation of platelets induces splicing of pre-mRNAs including IL-1β [6] and tissue factor (TF) [7]. Platelets are primarily involved in thrombus formation but their functions also extend to pathophysiological processes such as host defense, regulation of vascular tone, inflammation and tumor growth [8]. Splice isoforms in platelets have been implicated in the variable response to aspirin [9] and as possible antithrombotic drug targets [10]. Blood-based biomarker discovery would provide minimally invasive and sensitive detection of disease-associated molecular changes. Disease biomarkers, serving as specific diagnostic signatures of phenotype, could improve drug discovery and facilitate the development of modern, personalized clinical applications.
To date, efforts to detect AS events have relied primarily on sequencing mature mRNA species. The bulk of our knowledge comes from mapping expressed sequence tags (ESTs) to the genome. However, this approach is hindered by the lack of EST coverage with few ESTs sequenced for most genes [11] and the central region of mRNAs inadequately represented. More recently, exon arrays have been developed to determine genome-wide exon expression levels. This technology detects differences in expression across a gene to infer the presence of alternative splicing events, but cannot determine unambiguously what combination of exons is present on a single mRNA. The inference of AS is confounded somewhat by the variable hybridization intensities of neighboring probe sets within a sample and differential gene expression between samples. Ultra high-throughput sequencing addresses some of the problems encountered with previous methods of AS detection [12]. This approach can identify many alternative splice variants if sufficient sequence reads are carried out [13], [14]. As longer sequence reads become available, it will be possible to identify considerable structure flanking a given AS event.
The capacity to discover AS events at the mRNA level is very powerful and mRNAseq has provided evidence for AS occurring in 100% of multi-exonic human genes [13]. It remains unclear how many of the splice isoforms identified are sufficiently stable to result in translation products. Studying the proteome circumvents this issue - a recent study by Tress and coworkers for example, demonstrated the presence of translated AS isoforms in Drosophila melanogaster [15]. The development of new, innovative discovery approaches based on protein expression will greatly enhance the existing methodologies.
Mass spectrometry (MS) has emerged as a highly effective analytical technique capable of detecting vast numbers of peptides in complex mixtures. This is achieved by mapping spectra generated from a MS experiment to a database of known or, more commonly, theoretically derived spectra to infer the peptide sequence. Exon skip splice isoforms are characterized by the peptides spanning the exon-exon junction of a novel splicing event. To detect these peptides, we generate a database containing the theoretical exon-skip junction peptides across a genome. We then use standard MS search tools to identify junction peptides that represent exon skip events in MS/MS spectra by comparison with this database (Fig. 1). Here, we show that this approach can detect novel exon skip events in human platelets and verify a number of these at the mRNA level.
A rectangle represents a program, rhombus represents a program output, cylinder represents a data source and circle represents a program input.
Results
Database design
The strategy we employed to generate the database (which we call SkipE) is outlined in Figure 1. Transcript and exon data were extracted from Ensembl v46 [16] for all 22,680 annotated human protein-coding genes. To create exon skip junctions in silico, a gene containing multiple transcripts was first reduced to a single ‘full length transcript’ (Fig. 2a) as described in Materials and Methods.
(A) Generation of representative transcripts. Each box represents an exon and each line is an intron. i), ii) and iii) represent transcripts from a single gene. iv) shows the full length representative transcript used to generate the junction peptides. (B) Structure of a junction peptide. The top two boxes represent the translated sequences of two separate, non-adjacent exons. The tryptic cleavage sites are represented by dashed vertical bars. The C-terminal sequence of the upstream exon from the final tryptic site is spliced to the N-terminus of the downstream exon and extends to the first tryptic cleavage site of the downstream exon.
All non-contiguous junction peptides in a ‘full length transcript’ were created such that the termini are trypsin cleavage sites (Fig. 2b). It is possible to design a database for other proteolytic enzymes but trypsin is by far the most commonly employed proteinase in proteomics experiments. Combinations of exons yielding junction peptides were constrained by the phase of the exons in order to keep the sequences within the correct reading frame. Phase describes the number of nucleotides upstream of an exon that are used to form a codon so that the length of the exon is a multiple of three. A previous study by Sorek et al. [17] showed, using coding sequence information from Genbank, that the majority of orthologous alternatively spliced exons conserved between human and mouse did not endure a frame shift. Furthermore, it is likely that many phase shifting splice events generate transcripts which are degraded via nonsense-mediated decay [18]. In order to detect only alternative splice events in which the correct reading frame is maintained, the phase of both exons joined by the alternatively spliced junction was calculated and only those junctions with exons of compatible phase were entered into the database.
Duplicate entries of the same junction peptide mapping to different genes were removed to eliminate ambiguity, since the source of such peptides could not be ascribed to a particular gene. This procedure yielded 307,030 junction peptides for the human genome. Previous genome-based studies, such as 6-frame translation of the genome, result in search spaces that are incompatible with high-throughput approaches. Genome-based methods that reduce the search space complexity, provide a powerful means to identify new protein-coding exons and genes but are not appropriate for direct mapping of exon skips since these junctions are derived from non-contiguous sequences [19]. The database we constructed, subject to the constraints described, generates a search space appropriate for the high-throughput MS/MS methods in use today and into the future. Further details on the composition of the human, mouse and rat databases are provided (Table S1).
The skipE database is in FASTA format and therefore suitable for use with any of the major search engines; in this case we employed SEQUEST [20] combined with PeptideProphet and ProteinProphet for statistical validation of identifications [21]. We chose a cutoff score of 0.9, a commonly used cutoff in MS/MS experiments [22], for both tools. We then determined which junction-spanning peptides are novel and those which were previously described by comparing peptide sequences with the Alternative Splice Transcript Database (ASTD) [23]–[25] and the International Protein Index database (IPI) [26] using WU-BLAST (http://blast.wustl.edu). This also filters out junction peptides which are identical to sequences within “canonical” isoforms, whether they occur at exon boundaries or elsewhere.
Identification of platelet proteins and AS peptides
Platelet mass spectra were collected and compared with both the IPI and SkipE databases to identify peptides. The number of peptides and proteins identified in each database are shown in table S2. SEQUEST searching against IPI identified 6,292 unique peptides representing 1,122 unique proteins in the samples with a ProteinProphet probability score of P>0.9. Since the SkipE database harbors peptide rather than protein sequences, ProteinProphet is inappropriate. Therefore, spectra identified by comparison with SkipE were validated using a PeptideProphet probability cut-off of 0.9 resulting in 1,297 unique protein identifications. Of these, 359 were represented by more than a single occurrence of the peptide in the dataset.
The spatial distribution of AS identifications closely mirrors that of the IPI data with the exception of the releasate (Fig. 3a, b). In this case, more skips were found in the activated than in the resting samples for the AS data. Although the activation step was very brief, this may indicate a tendency towards diversification of the exported proteome in response to platelet activation. Functionally, this would be advantageous since these cells must interact with the milieu and other cell types but cannot mount a transcriptomic response to stimuli. All identified proteins in both SkipE and IPI data were mapped to KEGG pathways using Pathway-Express [27] (Table S3 and S4). In a typical MS/MS data analysis, protein identifications rely on multiple peptide identifications for any given protein. Since SkipE harbors isolated peptide sequences, we decided to focus further experiments on those AS events for which evidence of cognate gene expression was also obtained in the IPI analysis. Therefore, we constructed a list of 89 genes which represents the intersection of the AS and IPI datasets (Table 1).
(A) and (B) describe the distribution of the SkipE and IPI peptides respectively, across the different subcellular compartments for both resting and activated platelet samples.
Verification of splice variants at mRNA level
We confirmed the presence of several mRNA species encoding previously undescribed exon skip events by RT-PCR and sequencing of the products. We chose 3 junctions identified in the SkipE data for which evidence of protein expression was obtained in the IPI search (Fig. 4). The proteins chosen were integrin alpha 2 or platelet glycoprotein Ia (ITGA2), fumarate hydratase (FH) and puromycin-sensitive aminopeptidase (NPEPPS). These proteins represent different compartments and perform various roles in the cell.
Each numbered box represents an exon and the position in the gene. The skip event is indicated by the diagonal lines. The parallelograms enclose the portion of amino acid sequence that is absent from the novel splice isoform. The bold and underlined form the junction peptides.
ITGA2 forms part of a platelet collagen receptor, involved in the initial adhesion of platelets to extracellular matrix exposed at sites of endothelial injury, such as atherosclerotic lesions [28], [29]. Splice variants may be functionally significant: a platelet-specific splice variant may allow some tissue specific functions, while polymorphic variations in ITGA2 are associated with risk of thrombotic stroke [30]. The junction peptide we identified, which was formed by splicing exon 26 to exon 29, occurred 3 times in the SkipE data and 16 peptides were present for this protein in the IPI data. This splicing event results in the deletion of 68 amino acids proximal to the single transmembrane domain on the extracellular surface, far from any reported ligand-binding domains. Similar changes in the length of the ‘stalk’ of the platelet adhesion receptor GPIb are reported to affect the ability of platelets to adhere at high flow rates [31].
FH is a Krebs' cycle enzyme which is located in the cytosol or can be transported to the mitochondrion and has been shown to act as a tumor suppressor [32]. The FH junction under study was formed by splicing exon 2 to exon 6 and was identified 5 times with 7 different peptides identified in the IPI data.
The final protein selected, NPEPPS, is a puromycin-sensitive aminopeptidase, common in brain and immune tissues. NPEPPS may play a role in cell development and cell cycle-regulating proteolysis [33]. The NPEPPS junction identified was created via the splicing of exon 10 to exon 17 and occurred 4 times while 4 peptides were identified in IPI sequences.
The NPEPPS event was the longest skip we investigated, removing 6 exons. Interestingly, skips of up to 96 exons were observed – the distribution of skip lengths shown in Fig. 5 is highly reminiscent of that observed by Sultan et al. in mRNAseq data [14]. Such long skips remain to be verified (perhaps by the use of 2-dimensional gel separation followed by Western blotting and/or MS), as the number of other potential AS events in genes exhibiting long range AS gives rise to multiple PCR products (data not shown). Primer pairs specific to the exons involved in each junction generated multiple or ambiguous products with a predominant band migrating at the “canonical” amplicon length. It is likely that the AS message is present in relatively small amounts and is out-competed by the canonical isoform in PCR. Therefore, we designed primers to span the novel junctions and paired them with compatible reverse primers providing a skip-specific PCR primer pair (Table 2).
The number of occurrences of each skip length identified is shown.
PCR products of the expected sizes were observed in each case with cDNA derived both from platelets and from their precursors, megakaryocytes. The bands derived from platelet cDNA were excised and the sequence verified that the predicted products were obtained. It can be seen from Figure 6 that the megakaryocyte template produced a greater quantity of the amplicon in each case, reflective of the availability of template rather than an increased proportion of AS message in these cells.
Lanes 1–5 show products amplified from megakaryocyte cDNA; samples in lanes 6–10 contained template from platelets. Molecular size markers are in lane 11.
Discussion
Our findings demonstrate that many exon skip events, which have not been previously described, occur in platelets. These events have been found in a novel high-throughput fashion. The approach described is compatible with existing MS/MS software solutions accessible to the scientific community. We have shown that, while these events were found computationally, using a proteomics platform, we selected and verified three of them at the transcriptomic level by PCR and sequencing.
It is notable that the overlap of proteins, identified in the AS and IPI databases, is relatively low – just 89 genes were represented by peptides in both datasets. In common with many other high-throughput experimental approaches such as yeast two-hybrid and protein interaction networking [34], [35], MS/MS proteomics experiments suffer from a lack of completeness - that is, coverage of the proteome is neither absolute nor unbiased. The completeness of proteomics experiments is increased by high-throughput approaches although approximately 10 repetitions of a multidimensional protein identification technology (MudPIT) experiment are required to reach 95% analytical completeness [36], [37]. The proteins identified in any given experiment will be constrained by a number of factors including expression level and presence of proteotypic peptides [38]. In the case of splice isoforms, these will not necessarily correspond to the ‘canonical’ isoforms. Therefore, although, in this experiment we used IPI-based detection of protein expression to filter potential targets for verification, it is clear that not all genes displaying AS will also be detected as canonical isoforms and vice versa. Although we applied a relatively strict cutoff of 0.9 to the SkipE hits, given the fact that they are subject to only PeptideProphet and not ProteinProphet validation, it is possible there are more false positives in the SkipE data than the IPI results. Ultra high throughput mRNAseq verification of high numbers of skip events detected in proteomics data will demonstrate the synergy derived from the combination of high-throughput techniques and these datasets will provide mutual cross validation.
It appears that the novel splice events detected in this study were most likely inherited from the precursor rather than being specific to the platelet. It will be interesting to determine the distribution of these events in a variety of cell types and tissues across different organisms. It will also be of interest to determine whether any of the exon skip events occur specifically in the platelet since it is known that splicing can occur in these cells, despite the absence of a nucleus. While exon skips are the most common type of AS event described to date [13], [14], [39], [40], several other splicing patterns occur during transcription including alternative 5′ and 3′ splice sites and intron retention. These events require a different approach to detection in proteomics data. Clearly, intron-specific peptides could be incorporated into the SkipE database, though this would considerably increase the database size. A parallel intron peptide database would be a feasible approach. Alternative 5′ or 3′ splice sites on the other hand are not amenable to detection in this manner and require an alternative approach.
In conclusion, we have developed a novel database, suitable for the detection of alternative splicing in mass spectrometry data and shown that it can detect AS events in a platelet MS/MS dataset. The approach described augments current methodologies. Detection of AS directly at the protein level avoids any requirement for amplification steps and indicates that the events detected are indeed expressed. Millions of spectra, which are already available in both public and private repositories, can be reanalyzed using this database. As label-free quantitation tools are incorporated into proteomics pipelines, the added value becomes even greater as isoforms can be compared at the expression level within and between samples. Again, this approach is applicable to the vast repositories of data already gathered as well as to all new samples. The application of this methodology will rapidly give us new insights into AS throughout a range of tissue types and biological states. Since AS events have previously been associated with particular diseases, the approach described here will allow the discovery of disease-specific biomarkers at the splice isoform level. As the proteome is the network most closely related to the biological phenotype, the potential to discover clinically relevant biomarkers related to diagnosis, prognosis or susceptibility is immense, impacting on all levels of clinical practice and drug development.
Note added in proof
During the review process a similar database development was described by Mo et al. [41].
Materials and Methods
Platelet MS/MS data acquisition
Platelets were prepared as previously described in McRedmond et al. [42] and incubated at 37°C with stirring. One sample was activated by the addition of 5 µM thrombin receptor activating peptide for 5 minutes. Resting and activated samples were separated into subcellular compartments using a ProteoExtract subcellular proteome extraction kit (Merck Biosciences, Nottingham, UK). The manufacturer's protocol was modified to ensure separation of platelet pellets from supernatants and to allow the recovery of released platelet proteins. This procedure yields a ‘nuclear’ fraction, which is artefactual when applied to platelets. Fractions from resting and thrombin receptor activating peptide-activated platelets were separated by SDS-PAGE; gel lanes were cut into 32 slices and digested with trypsin. Peptides were separated by single-dimension reverse-phase liquid chromatography and analysed using an LTQ ion trap mass spectrometer (Thermo-Finnigan, San Jose, CA) [43].
Public data repositories
Ensembl version 46 was used to obtain all protein coding genes and sequences, along with their associated exon predictions for the human, mouse and rat genomes. Previously annotated AS events in our dataset were filtered out by comparing sequences with ASTD version 1.1 and IPI version 3.16 using Washington University basic local alignment search tool (WU-BLAST) version 2.0, applying the pam30 substitution matrix.
Database development
Transcript and exon data were extracted, via the Perl-API, from Ensembl v46 for all annotated genes in each of the human, mouse and rat genomes. For each species, a separate database was generated. Briefly, a standard “full-length transcript” containing, for each exon position along the transcript, the longest predicted exon sequence was generated. This procedure yields a single, representative, “standard” transcript from which to design junction peptides. The junction peptides are the derived peptide sequences that span exon-exon junctions from the most C-terminal protease site in the upstream exon to the most N-terminal protease site in the downstream exon. In this case, we used trypsin as the protease. Only the junctions of non-consecutive exons were included in the database and the content was further constrained by only including junctions in which phase was maintained between exons. The fasta files for all three species are publicly available online at http://bioinformatics.ucd.ie/SkipE.
MS/MS data analysis
All MS/MS data analyses were carried out using the Proline proteomics platform (Biontrack, Dublin http://www.biontrack.com). Spectra were compared against databases using SEQUEST [20]. Validation of peptides and proteins was carried out using the transproteomics pipeline tools PeptideProphet and ProteinProphet [21], respectively, and filtered with a cut-off of P>0.9.
RNA isolation
RNA from platelet and the megakaryocytic cell line Meg-01 was isolated as previously described [42] and reverse-transcribed into cDNA using standard techniques.
Validation
PCR and sequencing was carried out to validate the alternative splicing events. All primer synthesis and sequencing was carried out by MWG biotech (http://www.eurofinsdna.com/). Primer sequences for ITGA2 were, forward CAAAGAATTGATTCCCCTGA and reverse TGCAACCAGAGCTAACAGCA. NPEPPS forward primer is TCCTATTGAAGCTCGAGCTG and reverse CAGCCCAGTCTCTCCCCTAT and FH forward primer is AACGCATGCCAGAATTTAGTG and reverse is CCACTTTTGCAGCAACCTTT. The PCR reactions were made up as follows; 8 µl 5× GoTaq buffer, 1 µl Taq polymerase, 2 µl 4 mM dNTPs (Promega), 22 µl H2O, 2 µl primers and 1.5 µl template. The following PCR conditions were used: 2 minutes of denaturation at 94°C followed by 40 cycles of 30 seconds denaturing at 94°C, 30 seconds annealing at 55°C for NPEPPS and 58°C for FH and ITGA2 and a 90 second extension at 72°C followed by incubation at 4°C. Products were separated on 2% agarose gels. Positive control was integrin ITGA2B (α2B), a known abundant platelet glycoprotein. Negative control was a no-template RT reaction.
Supporting Information
Table S1.
Characteristics of the contents and constraints applied to create the species-specific SkipE databases.
https://doi.org/10.1371/journal.pone.0005001.s001
(0.03 MB DOC)
Table S2.
Numbers of platelet peptide and protein identifications in IPI and SkipE databases
https://doi.org/10.1371/journal.pone.0005001.s002
(0.03 MB DOC)
Table S3.
KEGG annotations for all of the 89 genes found to be alternatively spliced and represented in the IPI data. In total, 32 pathways were found. These pathways are sorted by impact factor, a probabilistic term which is calculated from the number of genes in the input file, the size of the reference chip (U133 plus2.0), the number of input genes that are on a given pathway and the number of the pathway genes represented on the reference chip.
https://doi.org/10.1371/journal.pone.0005001.s003
(0.08 MB DOC)
Table S4.
KEGG annotations for all the genes found in IPI. In total, 78 pathways were found. These pathways are sorted by impact factor, a probabilistic term which is calculated from the number of genes in the input file, the size of the reference chip (U133 plus2.0), the number of input genes that are on a given pathway and the number of the pathway genes represented on the reference chip.
https://doi.org/10.1371/journal.pone.0005001.s004
(0.17 MB DOC)
Acknowledgments
Thanks to G. Cagney and M. Sullivan for MS/MS data analysis advice. The authors acknowledge access to instrumentation and support from staff of the mass spectrometry resource in the UCD Conway Institute.
Author Contributions
Conceived and designed the experiments: KAP JPM PG. Performed the experiments: KAP. Analyzed the data: KAP PG. Contributed reagents/materials/analysis tools: JPM AdS. Wrote the paper: KAP WG PG. Designed and implemented algorithms: KAP. Supported the work: WG.
References
- 1. Blencowe BJ (2006) Alternative splicing: new insights from global analyses. Cell 126: 37–47.
- 2. Brett D, Pospisil H, Valcarcel J, Reich J, Bork P (2002) Alternative splicing and genome complexity. Nat Genet 30: 29–30.
- 3. Tress ML, Martelli PL, Frankish A, Reeves GA, Wesselink JJ, et al. (2007) The implications of alternative splicing in the ENCODE protein complement. Proc Natl Acad Sci U S A 104: 5495–5500.
- 4. Wang GS, Cooper TA (2007) Splicing in disease: disruption of the splicing code and the decoding machinery. Nat Rev Genet 8: 749–761.
- 5. Atkinson AJ, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, et al. (2001) Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework*. Clin Pharmacol Ther 69: 89–95.
- 6. Denis MM, Tolley ND, Bunting M, Schwertz H, Jiang HM, et al. (2005) Escaping the nuclear confines: Signal-dependent Pre-mRNA splicing in anucleate platelets. Cell 122: 379–391.
- 7. Schwertz H, Tolley ND, Foulks JM, Denis MM, Risenmay BW, et al. (2006) Signal-dependent splicing of tissue factor pre-mRNA modulates the thrombogenecity of human platelets. J Exp Med 203: 2433–2440.
- 8. Harrison P (2005) Platelet function analysis. Blood Rev 19: 111–123.
- 9. Censarek P, Steger G, Paolini C, Hohlfeld T, Grosser T, et al. (2007) Alternative splicing of platelet cyclooxygenase-2 mRNA in patients after coronary artery bypass grafting. Thromb Haemostasis 98: 1309–1315.
- 10. Newland SA, Macaulay IC, Floto RA, de Vet EC, Ouwehand WH, et al. (2007) The novel inhibitory receptor G6B is expressed on the surface of platelets and attenuates platelet function in vitro. Blood 109: 4806–4809.
- 11. Modrek B, Lee C (2002) A genomic view of alternative splicing. Nat Genet 30: 13–19.
- 12. Marioni J, Mason C, Mane S, Stephens M, Gilad Y (2008) RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. DOI: 10.1101/gr.079558.108.
- 13. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, et al. (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456: 470–476.
- 14. Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, et al. (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321: 956–960.
- 15. Tress ML, Bodenmiller B, Aebersold R, Valencia A (2008) Proteomics studies confirm the presence of alternative protein isoforms on a large scale. Genome Biol 9: R162.
- 16. Hubbard TJP, Aken BL, Beal K, Ballester B, Caccamo M, et al. (2007) Ensembl 2007. Nucleic Acids Res 35: D610–D617.
- 17. Sorek R, Shamir R, Ast G (2004) How prevalent is functional alternative splicing in the human genome? Trends Genet 20: 68–71.
- 18. Lewis BP, Green RE, Brenner SE (2003) Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans. Proc Natl Acad Sci U S A 100: 189–192.
- 19. Tanner S, Shen Z, Ng J, Florea L, Guigo R, et al. (2007) Improving gene annotation using peptide mass spectrometry. Genome Res 17: 231–239.
- 20. Eng JK, McCormack AL, Yates JR (1994) An Approach to Correlate Tandem Mass-Spectral Data of Peptides with Amino-Acid-Sequences in a Protein Database. J Am Soc Mass Spectr 5: 976–989.
- 21. Keller A, Nesvizhskii AI, Kolker E, Aebersold R (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74: 5383–5392.
- 22. Bodenmiller B, Mueller LN, Mueller M, Domon B, Aebersold R (2007) Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat Methods 4: 231–237.
- 23. Stamm S, Riethoven JJ, Le Texier V, Gopalakrishnan C, Kumanduri V, et al. (2006) ASD: a bioinformatics resource on alternative splicing. Nucleic Acids Res 34: D46–D55.
- 24. Clark F, Thanaraj TA (2002) Categorization and characterization of transcript-confirmed constitutively and alternatively spliced introns and exons from human. Hum Mol Genet 11: 451–464.
- 25. Thanaraj TA, Stamm S, Clark F, Riethoven JJ, Le Texier V, et al. (2004) ASD: the Alternative Splicing Database. Nucleic Acids Res 32: D64–D69.
- 26. Kersey PJ, Duarte J, Williams A, Karavidopoulou Y, Birney E, et al. (2004) The International Protein Index: An integrated database for proteomics experiments. Proteomics 4: 1985–1988.
- 27. Draghici S, Khatri P, Tarca AL, Amin K, Done A, et al. (2007) A systems biology approach for pathway level analysis. Genome Res 17: 1537–1545.
- 28. Santoro SA (1999) Platelet surface collagen receptor polymorphisms: Variable receptor expression and thrombotic hemorrhagic risk. Blood 93: 3575–3577.
- 29. Holtkotter O, Nieswandt B, Smyth N, Muller W, Hafner M, et al. (2002) Integrin alpha(2)-deficient mice develop normally, are fertile, but display partially defective platelet interaction with collagen. J Biol Chem 277: 10789–10794.
- 30. Matarin M, Brown WM, Hardy JA, Rich SS, Singleton AB, et al. (2008) Association of integrin alpha 2 gene variants with ischemic stroke. J Cerebr Blood F Met 28: 81–89.
- 31. Ozelo MC, Origa AF, Aranha FJR, Mansur AR, Annichino-Bizzacchi JM, et al. (2004) Platelet glycoprotein Ib alpha polymorphisms modulate the risk for myocardial infarction. Thromb Haemostasis 92: 384–386.
- 32. Rustin P (2002) Mitochondria, from cell death to proliferation. Nat Genet 30: 352–353.
- 33. Constam DB, Tobler AR, Rensingehl A, Kemler I, Hersh LB, et al. (1995) Puromycin-Sensitive Aminopeptidase - Sequence-Analysis, Expression, and Functional-Characterization. J Biol Chem 270: 26931–26939.
- 34. Mestres J, Gregori-Puigjane E, Valverde S, Sole RV (2008) Data completeness–the Achilles heel of drug-target networks. Nat Biotechnol 26: 983–984.
- 35. Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, et al. (2008) High-quality binary protein interaction map of the yeast interactome network. Science 322: 104–110.
- 36. Durr E, Yu J, Krasinska KM, Carver LA, Yates JR, et al. (2004) Direct proteomic mapping of the lung microvascular endothelial cell surface in vivo and in cell culture. Nat Biotechnol 22: 985–992.
- 37. Liu H, Sadygov RG, Yates JR, 3rd (2004) A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem 76: 4193–4201.
- 38. Mallick P, Schirle M, Chen SS, Flory MR, Lee H, et al. (2007) Computational prediction of proteotypic peptides for quantitative proteomics. Nat Biotechnol 25: 125–131.
- 39. Sugnet C, Ares M, Haussler D (2004) Transcriptome and genome conservation of alternative splicing events in humans and mice. Pac Symp Biocomput 9: 66–77.
- 40. Kim E, Magen A, Ast G (2007) Different levels of alternative splicing among eukaryotes. Nucleic Acid Res 35: 125–131.
- 41. Mo F, Hong X, Gao F, Du L, Wang J, et al. (2008) A compatible exon-exon junction database for the identification of exon skipping events using tandem mass spectrum data. BMC Bioinformatics 9: 537.
- 42. McRedmond JP, Park SD, Reilly DF, Coppinger JA, Maguire PB, et al. (2004) Integration of proteomics and genomics in platelets - A profile of platelet proteins and platelet-specific genes. Mol Cell Proteomics 3: 133–144.
- 43. Mathivanan S, Ahmed M, Ahn NG, Alexandre H, Amanchy R, et al. (2008) Human Proteinpedia enables sharing of human protein data. Nat Biotechnol 26: 164–167.