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• W2099277710 abstract "Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva. Human saliva has great potential for clinical disease diagnostics. Constructing a comprehensive catalogue of saliva proteins using proteomic approaches is a necessary first step to identifying potential protein biomarkers of disease. However, because of the challenge presented in cataloguing saliva proteins with widely varying abundance, new proteomic approaches are needed. To this end, we used a newly developed approach coupling peptide separation using free flow electrophoresis with linear ion trap tandem mass spectrometry to identify proteins in whole human saliva. We identified 437 proteins with high confidence (false positive rate below 1%), producing the largest catalogue of proteins from a single saliva sample to date and providing new information on the composition and potential diagnostic utility of this fluid. The statistically validated, transparently presented, and annotated dataset provides a model for presenting large scale proteomic data of this type, which should facilitate better dissemination and easier comparisons of proteomic datasets from future studies in saliva. Clinicians and researchers value human saliva as potentially the ultimate bodily fluid for clinical disease diagnosis and prognostic monitoring (1Hofman L.F. Human saliva as a diagnostic specimen.J. Nutr. 2001; 131: 1621S-1625SGoogle Scholar, 2Lawrence H.P. Salivary markers of systemic disease: noninvasive diagnosis of disease and monitoring of general health.J. Can. Dent. Assoc. 2002; 68: 170-174Google Scholar). To identify potential protein biomarkers in saliva, construction of a comprehensive protein catalogue in this fluid is a necessary first step. Several mass spectrometry-based proteomic studies have begun this task. These studies used different strategies, including two-dimensional gel-based analysis (3Yao Y. Berg E.A. Costello C.E. Troxler R.F. Oppenheim F.G. Identification of protein components in human acquired enamel pellicle and whole saliva using novel proteomics approaches.J. Biol. Chem. 2003; 278: 5300-5308Google Scholar, 4Vitorino R. Lobo M.J. Ferrer-Correira A.J. Dubin J.R. Tomer K.B. Domingues P.M. Amado F.M. Identification of human whole saliva protein components using proteomics.Proteomics. 2004; 4: 1109-1115Google Scholar, 5Ghafouri B. Tagesson C. Lindahl M. Mapping of proteins in human saliva using two-dimensional gel electrophoresis and peptide mass fingerprinting.Proteomics. 2003; 3: 1003-1015Google Scholar, 6Hardt M. Thomas L.R. Dixon S.E. Newport G. Agabian N. Prakobphol A. Hall S.C. Witkowska H.E. Fisher S.J. Toward defining the human parotid gland salivary proteome and peptidome: identification and characterization using 2D SDS-PAGE, ultrafiltration, HPLC, and mass spectrometry.Biochemistry. 2005; 44: 2885-2899Google Scholar) and liquid chromatography-based analysis coupled with MS/MS (7Hu S. Xie Y. Ramachandran P. Ogorzalek Loo R.R. Li Y. Loo J.A. Wong D.T. Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry.Proteomics. 2005; 5: 1714-1728Google Scholar, 8Wilmarth P.A. Riviere M.A. Rustvold D.L. Lauten J.D. Madden T.E. David L.L. Two-dimensional liquid chromatography study of the human whole saliva proteome.J. Proteome Res. 2004; 3: 1017-1023Google Scholar). The wide range of protein abundance in whole saliva makes cataloguing of saliva proteins using any of these strategies a challenge (9Honore B. Ostergaard M. Vorum H. Functional genomics studied by proteomics.Bioessays. 2004; 26: 901-915Google Scholar). Therefore, to obtain a more comprehensive catalogue of saliva proteins innovative proteomic approaches are needed. Recently we described a new approach (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar) to proteomic analysis that uses preparative IEF by free flow electrophoresis (FFE) 1The abbreviations used are: FFE, free flow electrophoresis; μLC, microcapillary LC. (11Loseva O.I. Gavryushkin A.V. Osipov V.V. Vanyakin E.N. Application of free-flow electrophoresis for isolation and purification of proteins and peptides.Electrophoresis. 1998; 19: 1127-1134Google Scholar, 12Moritz R.L. Ji H. Schutz F. Connolly L.M. Kapp E.A. Speed T.P. Simpson R.J. A proteome strategy for fractionating proteins and peptides using continuous free-flow electrophoresis coupled off-line to reversed-phase high-performance liquid chromatography.Anal. Chem. 2004; 76: 4811-4824Google Scholar) for a first dimension fractionation of complex peptide mixtures. The use of FFE not only provides a high resolution peptide separation, but also it adds a constraint of peptide pI information to the determination of peptide sequence matches in the sequence database search of the MS/MS data, significantly improving the confidence of the peptide sequence matches and effectively increasing the number of high confidence protein identifications (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar, 13Cargile B.J. Talley D.L. Stephenson Jr., J.L. Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides.Electrophoresis. 2004; 25: 936-945Google Scholar, 14Cargile B.J. Bundy J.L. Freeman T.W. Stephenson Jr., J.L. Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification.J. Proteome Res. 2004; 3: 112-119Google Scholar, 15Cargile B.J. Bundy J.L. Stephenson Jr., J.L. Potential for false positive identifications from large databases through tandem mass spectrometry.J. Proteome Res. 2004; 3: 1082-1085Google Scholar). The goal of this study was to use peptide separation by FFE coupled with a linear ion trap mass spectrometer to comprehensively identify proteins in whole human saliva. We identified 437 proteins with high confidence, providing the largest catalogue of proteins from a single saliva sample to date. The protein catalogue provides new information on the composition of this bodily fluid and its potential utility in disease diagnostics. The statistically validated and transparently presented dataset (shown in the supplemental table) provides a model for presenting large, mass spectrometry-based proteomic data that should provide improved dissemination and comparison of datasets in this clinically important biological fluid. Whole unstimulated saliva was collected from a healthy female subject in the University of Minnesota Oral Medicine Clinic using a protocol described previously (16Rhodus N.L. Cheng B. Myers S. Bowles W. Ho V. Ondrey F. A comparison of the pro-inflammatory, NF-κB-dependent cytokines: TNF-α, IL-1-α, IL-6, and IL-8 in different oral fluids from oral lichen planus patients.Clin. Immunol. 2005; 114: 278-283Google Scholar). 1 ml of whole saliva was removed and centrifuged at 25,000 × g and 4 °C for 30 min. The supernatant was collected and quantified by using the BCA protein assay (Pierce), giving 1.05 mg of total soluble proteins. The saliva was brought to 100 mm with HEPES, pH 8.0 and 5 mm with Tris(2-carboxyethyl)phosphine and incubated overnight with 20 μg of trypsin (Promega, Madison, WI) at 37 °C. The resulting peptides were concentrated and desalted using a reverse-phase Sep-Pak cartridge (Waters, Milford, MA) and dried by vacuum centrifugation. Preparative IEF of the peptide mixture was performed using a commercially available Pro Team free flow electrophoresis system (BD Biosciences) (11Loseva O.I. Gavryushkin A.V. Osipov V.V. Vanyakin E.N. Application of free-flow electrophoresis for isolation and purification of proteins and peptides.Electrophoresis. 1998; 19: 1127-1134Google Scholar, 12Moritz R.L. Ji H. Schutz F. Connolly L.M. Kapp E.A. Speed T.P. Simpson R.J. A proteome strategy for fractionating proteins and peptides using continuous free-flow electrophoresis coupled off-line to reversed-phase high-performance liquid chromatography.Anal. Chem. 2004; 76: 4811-4824Google Scholar). The saliva peptides were dissolved in 250 μl of FFE separation buffer and fractionated by FFE into a 96-well microtiter plate as described previously (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar). Immediately after FFE separation, the pH of each FFE fraction was measured using a microelectrode (Accument combination microelectrode, Fisher). A 50-μl aliquot (of ∼500 μl total) was taken from each of the microtiter plate wells and processed as described previously (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar) prior to mass spectrometric analysis. All μLC separations were done on an automated Paradigm MS4 system (Michrom Bioresources, Inc., Auburn, CA). Each processed FFE fraction was automatically loaded across a Paradigm Platinum Peptide Nanotrap (Michrom Bioresources, Inc.) precolumn (0.15 × 50 mm, 400-μl volume) for sample concentrating and desalting at a flow rate of 50 μl/min in HPLC buffer A. The in-line analytical capillary column (75 μm × 12 cm) was home-packed using C18 resin (5-μm, 200-Å Magic C18AG, Michrom Bioresources, Inc.) and Picofrit capillary tubing (New Objective, Cambridge, MA). Peptides were eluted using a linear gradient of 10–35% buffer B over 60 min followed by isocratic elution at 80% buffer B for 5 min with a flow rate of 0.25 μl/min across the column. Peptides were analyzed by MS/MS using a linear ion trap mass spectrometer system (LTQ, Thermo Electron Corp., San Jose, CA). The electrospray voltage was set to 2.0 kV using a collision energy setting of 29% and a data-dependent procedure that alternated between one MS scan (over the m/z range of 400–1800) followed by four MS/MS scans for the four most abundant precursor ions in the MS survey scan. Both the MS and MS/MS spectra were acquired using a single microscan with a maximum fill time of 50 ms in the ion trap. m/z values selected for MS/MS were dynamically excluded for 30 s. The MS/MS spectra were sequence database-searched using TurboSEQUEST (17Eng J. McCormack A.L. Yates III, J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.J. Am. Soc. Mass Spectrom. 1994; 5: 976-989Google Scholar) (Thermo Finnigan, San Jose, CA). The MS/MS spectra were searched against the non-redundant human International Protein Index database (18Kersey P.J. Duarte J. Williams A. Karavidopoulou Y. Birney E. Apweiler R. The International Protein Index: an integrated database for proteomics experiments.Proteomics. 2004; 4: 1985-1988Google Scholar) containing ∼50,000 protein sequences with a reverse version of the same database attached at the end of the forward version. The search parameters used included a precursor ion mass accuracy tolerance of 2.0 with methionine oxidation specified as a differential modification. Tryptic cleavage sites were specified as described below. The peptide sequence match results were organized and viewed using the software tool Interact (19Han D.K. Eng J. Zhou H. Aebersold R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry.Nat. Biotechnol. 2001; 19: 946-951Google Scholar). False positive rates were calculated as described previously (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar, 20Peng J. Elias J.E. Thoreen C.C. Licklider L.J. Gygi S.P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome.J. Proteome Res. 2003; 2: 43-50Google Scholar). The predicted pI of peptide sequences was calculated according to Shimura et al. (21Shimura K. Kamiya K. Matsumoto H. Kasai K. Fluorescence-labeled peptide pI markers for capillary isoelectric focusing.Anal. Chem. 2002; 74: 1046-1053Google Scholar) using an automated script, and peptide pI values were automatically inputted into the Interact results file. For FFE fractions in the pH range of 6.5–8.0 (fraction numbers 46–58), the average peptide pI value was used rather than the measured fraction pH for filtering peptide sequence matches in steps one and two (see “Results”). The MS/MS spectra were first searched against the database with the enzyme trypsin specified, allowing up to two missed cleavage sites in the peptide sequence match. To identify non-tryptic peptides derived from proline-rich proteins, as have been found in other proteomic studies of saliva (7Hu S. Xie Y. Ramachandran P. Ogorzalek Loo R.R. Li Y. Loo J.A. Wong D.T. Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry.Proteomics. 2005; 5: 1714-1728Google Scholar, 8Wilmarth P.A. Riviere M.A. Rustvold D.L. Lauten J.D. Madden T.E. David L.L. Two-dimensional liquid chromatography study of the human whole saliva proteome.J. Proteome Res. 2004; 3: 1017-1023Google Scholar), the MS/MS data were also searched with no enzyme specified, and the peptide matches were filtered by peptide pI and FFE fraction pH. This resulted in the identification of eight additional proteins, which were added to the protein results from the first filtering step described under “Results.” Our approach yielded a wealth of peptide sequence matches requiring filtering and statistical validation. To filter the sequence matches based upon peptide pI, it is first necessary to confirm the correspondence of peptide pI and measured FFE fraction pH for the dataset being analyzed (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar). To this end, the sequence matches were first filtered using Peptide Prophet (22Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal. Chem. 2002; 74: 5383-5392Google Scholar), which assigns to each peptide sequence match a probability (p) score between 0 and 1. The peptide sequence matches were initially filtered using a stringent p score threshold of 0.9. Next the theoretical pI for each matched peptide sequence was calculated (21Shimura K. Kamiya K. Matsumoto H. Kasai K. Fluorescence-labeled peptide pI markers for capillary isoelectric focusing.Anal. Chem. 2002; 74: 1046-1053Google Scholar), and the average peptide pI for each FFE fraction was determined. Fig. 1A shows the results of these calculations. The top two lines in the plot show the correspondence of the average peptide pI versus the measured pH value for each FFE fraction. Overall the close correspondence justifies the use of FFE fraction pH, in addition to p score, as a filtering criterion of peptide sequence matches for this catalogue as we describe below. There is some discrepancy between the pI and pH values in the pH range ∼6.5–8.0. The reason for this discrepancy is unknown and needs further investigation, although it may reflect an inaccuracy in the pI prediction algorithm as it has been observed regardless of the method used for IEF of peptide mixtures (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar, 13Cargile B.J. Talley D.L. Stephenson Jr., J.L. Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides.Electrophoresis. 2004; 25: 936-945Google Scholar, 14Cargile B.J. Bundy J.L. Freeman T.W. Stephenson Jr., J.L. Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification.J. Proteome Res. 2004; 3: 112-119Google Scholar). The bottom line in the plot shows the distribution of matched peptide sequence across each FFE fraction. The majority of the peptides cluster in the pH ranges 3.5–5.0 with very few peptides detected in fractions with neutral pH values (pH ∼7–8), similar to the distribution of tryptic peptides in other studies using preparative IEF (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar, 13Cargile B.J. Talley D.L. Stephenson Jr., J.L. Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides.Electrophoresis. 2004; 25: 936-945Google Scholar, 14Cargile B.J. Bundy J.L. Freeman T.W. Stephenson Jr., J.L. Gel based isoelectric focusing of peptides and the utility of isoelectric point in protein identification.J. Proteome Res. 2004; 3: 112-119Google Scholar). Our approach to generating a high confidence catalogue of proteins and their supporting peptide matches consists of two steps with each filtering matches based upon the difference (ΔpH) between the calculated peptide pI value for the matched sequence and the measured pH value of the FFE fraction from which the peptide was identified. True peptide sequence matches should have pI values very close to the measured fraction pH value, whereas false matches are expected to have random pI values and be eliminated when using the ΔpH filter (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar, 15Cargile B.J. Bundy J.L. Stephenson Jr., J.L. Potential for false positive identifications from large databases through tandem mass spectrometry.J. Proteome Res. 2004; 3: 1082-1085Google Scholar). The first step initially filtered the peptide sequence matches using a ΔpH tolerance of ±0.5, which we have shown to be the optimal ΔpH tolerance based upon the IEF resolution using FFE (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar). This filtering step allows for the p score threshold to be reduced while still maintaining a false positive rate below 1% (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar). The optimal p score threshold using ΔpH filtering will be different for each dataset being analyzed. As Fig. 1B shows, for this particular dataset the p score could be reduced to 0.76 when applying the ΔpH filter, decreased from the p score threshold of 0.96 needed to achieve the same confidence without considering peptide pI. The second step filtered the peptide sequence matches using a low stringency p score threshold of 0.2 and peptide pI, again using a ΔpH value of ±0.5, with the added proviso that a protein would be added to the catalogue only if it was matched by two or more unique peptide sequences. This step is based upon the assumption that when combined with the peptide pI constraint, multiple peptide sequence matches provide added confidence to protein identification even when the matches have a low p score. Indeed using these criteria the calculated false positive rate for this filtering step was also below 1%. Each filtering step added to the catalogue. The first step identified 433 proteins from peptide matches with a p score at or above the 0.76 threshold; each was added to the catalogue. 181 of these proteins had at least two peptide sequence matches, and the remainder had one peptide match. The second step identified and added to the catalogue another four proteins. At least one additional peptide sequence match was also added to 101 proteins (as indicated in the supplemental table) already in the catalogue, increasing the proteins identified by two or more peptide sequence matches to 221 of 437 total proteins. The supplemental table provides detailed information on this dataset, including all peptide sequence matches and the known biochemical functions and localizations of the identified proteins. The use of peptide pI maximized the number of high confidence proteins identified in this study. Using p score filtering alone, without the use of peptide pI information, the minimum p score threshold is 0.96 to obtain a false positive rate below 1% (see Fig. 1B). Such a threshold would have resulted in the identification of only 385 proteins. The use of peptide pI and FFE fraction pH in our two filtering steps allowed for a decrease in the p score threshold, thereby producing a significantly larger catalogue of high confidence proteins. These additional peptide sequence matches would otherwise be false negative matches when using p score filtering alone that are sequence matches that are actually correct but do not pass the set scoring threshold (10Xie H. Bandhakavi S. Griffin T.J. Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.Anal. Chem. 2005; 77: 3198-3207Google Scholar, 15Cargile B.J. Bundy J.L. Stephenson Jr., J.L. Potential for false positive identifications from large databases through tandem mass spectrometry.J. Proteome Res. 2004; 3: 1082-1085Google Scholar). The combined filtering steps using peptide pI and FFE fraction pH also increased the sequence coverage of identified proteins with about half of the catalogued proteins having two or more peptide sequence matches. Our approach identified 437 proteins with high confidence (false positive rate below 1%). We compared our catalogue to those from other proteomic studies of saliva attempting to comprehensively identify proteins in saliva using non-gel electrophoresis-based strategies. One recent study using multidimensional liquid chromatography and tandem mass spectrometry identified 102 proteins in whole human saliva (8Wilmarth P.A. Riviere M.A. Rustvold D.L. Lauten J.D. Madden T.E. David L.L. Two-dimensional liquid chromatography study of the human whole saliva proteome.J. Proteome Res. 2004; 3: 1017-1023Google Scholar). These protein matches were statistically validated using reversed database searching, providing an estimated false positive rate below 1%. Most of their catalogue’s proteins are contained in ours but not vice versa. Another recent report used both liquid chromatography-based separations and also two-dimensional gel separations to identify a combined 309 proteins from saliva (7Hu S. Xie Y. Ramachandran P. Ogorzalek Loo R.R. Li Y. Loo J.A. Wong D.T. Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry.Proteomics. 2005; 5: 1714-1728Google Scholar). The overlap between their catalogue and ours was relatively small with most of the common proteins between the studies being those that have also been found in other proteomic studies, most likely indicative of their high abundance and housekeeping functions in saliva. By comparison with these other studies, our catalogue of proteins is the largest obtained from a single saliva sample to date, thereby providing new information on its composition. Comparison of other catalogues with ours highlights an ongoing problem in the proteomics community: a lack of standards in publishing mass spectrometry-derived proteomic datasets (23Carr S. Aebersold R. Baldwin M. Burlingame A. Clauser K. Nesvizhskii A. The need for guidelines in publication of peptide and protein identification data: Working Group on Publication Guidelines for Peptide and Protein Identification Data.Mol. Cell. Proteomics. 2004; 3: 531-533Google Scholar, 24Ravichandran V. Sriram R.D. Toward data standards for proteomics.Nat. Biotechnol. 2005; 23: 373-376Google Scholar). For example, in the case of the study described in Ref. 7Hu S. Xie Y. Ramachandran P. Ogorzalek Loo R.R. Li Y. Loo J.A. Wong D.T. Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry.Proteomics. 2005; 5: 1714-1728Google Scholar, the dataset was non-transparently presented with little information on the criteria for determining correct peptide sequence matches provided and no estimate of false positive rates or detailed information on the scoring of peptide sequence matches. Furthermore the protein sequence database used outputted protein accession numbers for identified proteins from a variety of proteomic and genomic databases as opposed to non-redundant sequence databases such as the International Protein Index database (18Kersey P.J. Duarte J. Williams A. Karavidopoulou Y. Birney E. Apweiler R. The International Protein Index: an integrated database for proteomics experiments.Proteomics. 2004; 4: 1985-1988Google Scholar) used in our present study that provide consistent accession number formats (e.g. Uniprot) for identified proteins. Collectively these factors make comparison of these large proteomic datasets difficult. As such, we hope that the dataset of saliva proteins we present here will serve as a model for publishing large scale proteomic data to the growing number of research groups investigating this clinically important bodily fluid, helping the dissemination and comparison of proteomic datasets obtained from future studies. We gratefully acknowledge the Mass Spectrometry and Proteomics Center at the University of Minnesota for access to the mass spectrometer used in this work. We thank Patton Fast at the Minnesota Supercomputing Institute for help in setting up and maintaining the computer cluster used for sequence database searching. 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• W2099277710 title "A Catalogue of Human Saliva Proteins Identified by Free Flow Electrophoresis-based Peptide Separation and Tandem Mass Spectrometry" @default.
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• W2099277710 cites W1977452467 @default.
• W2099277710 cites W1988515943 @default.
• W2099277710 cites W1991060266 @default.
• W2099277710 cites W1992844066 @default.
• W2099277710 cites W1996692127 @default.
• W2099277710 cites W2006290622 @default.
• W2099277710 cites W2007426793 @default.
• W2099277710 cites W2022164275 @default.
• W2099277710 cites W2026465178 @default.
• W2099277710 cites W2040555297 @default.
• W2099277710 cites W2044690556 @default.
• W2099277710 cites W2051795211 @default.
• W2099277710 cites W2059647832 @default.
• W2099277710 cites W2063162077 @default.
• W2099277710 cites W2063292290 @default.
• W2099277710 cites W2085317185 @default.
• W2099277710 cites W2104426891 @default.
• W2099277710 cites W2110603815 @default.
• W2099277710 cites W2112078820 @default.
• W2099277710 cites W2116029572 @default.
• W2099277710 cites W2153132589 @default.
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