{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T04:31:33Z","timestamp":1759638693854},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2010,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Mass spectrometry (MS) is an essential analytical tool in proteomics. Many existing algorithms for peptide detection are based on isotope template matching and usually work at different charge states separately, making them ineffective to detect overlapping peptides and low abundance peptides.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We present BPDA, a Bayesian approach for peptide detection in data produced by MS instruments with high enough resolution to baseline-resolve isotopic peaks, such as MALDI-TOF and LC-MS. We model the spectra as a mixture of candidate peptide signals, and the model is parameterized by MS physical properties. BPDA is based on a rigorous statistical framework and avoids problems, such as voting and ad-hoc thresholding, generally encountered in algorithms based on template matching. It systematically evaluates all possible combinations of possible peptide candidates to interpret a given spectrum, and iteratively finds the best fitting peptide signal in order to minimize the mean squared error of the inferred spectrum to the observed spectrum. In contrast to previous detection methods, BPDA performs deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals and produces higher sensitivities and more robust results. Unlike template-matching algorithms, BPDA can handle complex data where features overlap. Our experimental results indicate that BPDA performs well on simulated data and real MS data sets, for various resolutions and signal to noise ratios, and compares very favorably with commonly used commercial and open-source software, such as flexAnalysis, OpenMS, and Decon2LS, according to sensitivity and detection accuracy.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>Unlike previous detection methods, which only employ isotopic distributions and work at each single charge state alone, BPDA takes into account the charge state distribution as well, thus lending information to better identify weak peptide signals and produce more robust results. The proposed approach is based on a rigorous statistical framework, which avoids problems generally encountered in algorithms based on template matching. Our experiments indicate that BPDA performs well on both simulated data and real data, and compares very favorably with commonly used commercial and open-source software. The BPDA software can be downloaded from <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/gsp.tamu.edu\/Publications\/supplementary\/sun10a\/bpda\" ext-link-type=\"uri\">http:\/\/gsp.tamu.edu\/Publications\/supplementary\/sun10a\/bpda<\/jats:ext-link>.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-490","type":"journal-article","created":{"date-parts":[[2010,9,29]],"date-time":"2010-09-29T18:14:26Z","timestamp":1285784066000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["BPDA - A Bayesian peptide detection algorithm for mass spectrometry"],"prefix":"10.1186","volume":"11","author":[{"given":"Youting","family":"Sun","sequence":"first","affiliation":[]},{"given":"Jianqiu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ulisses","family":"Braga-Neto","sequence":"additional","affiliation":[]},{"given":"Edward R","family":"Dougherty","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,9,29]]},"reference":[{"key":"3947_CR1","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1002\/(SICI)1520-6343(1997)3:4<259::AID-BSPY2>3.0.CO;2-#","volume":"3","author":"C Hop","year":"1997","unstructured":"Hop C, Bakhtiar R: An introduction to electrospray ionization and matrix-assisted laser desorption\/ionization mass spectrometry: essential tools in a modern biotechnology environment. Biospectroscopy 1997, 3: 259\u2013280. 10.1002\/(SICI)1520-6343(1997)3:4<259::AID-BSPY2>3.0.CO;2-#","journal-title":"Biospectroscopy"},{"key":"3947_CR2","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/0165-9936(90)85065-F","volume":"9","author":"M Karas","year":"1990","unstructured":"Karas M, Bahr U: Laser desorption ionization mass spectrometry of large biomolecules. Trends Anal Chem 1990, 9: 321\u2013325. 10.1016\/0165-9936(90)85065-F","journal-title":"Trends Anal Chem"},{"key":"3947_CR3","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1080\/05704920802108198","volume":"43","author":"S Batoy","year":"2008","unstructured":"Batoy S, Akhmetova E, Miladinovic S, Smeal J, Wilkins CL: Developments in MALDI mass spectrometry: the quest for the perfect matrix. Appl Spectrosc Rev 2008, 43: 485\u2013550. 10.1080\/05704920802108198","journal-title":"Appl Spectrosc Rev"},{"key":"3947_CR4","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1002\/jms.856","volume":"40","author":"Q Hu","year":"2005","unstructured":"Hu Q, Noll RJ, Li H, Makarov A, Hardman M, Graham Cooks R: The Orbitrap: a new mass spectrometer. Journal of mass spectrometry 2005, 40: 430\u2013443. 10.1002\/jms.856","journal-title":"Journal of mass spectrometry"},{"key":"3947_CR5","volume-title":"Quadrupole ion trap mass spectrometry","author":"JFJ Todd","year":"2005","unstructured":"Todd JFJ, March RE: Quadrupole ion trap mass spectrometry. New York, NY, USA: Wiley-Interscience; 2005."},{"key":"3947_CR6","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1002\/mas.1280120202","volume":"12","author":"H Wollnik","year":"1993","unstructured":"Wollnik H: Time-of-flight mass analyzers. Mass Spectrometry Reviews 1993, 12: 89\u201311. 10.1002\/mas.1280120202","journal-title":"Mass Spectrometry Reviews"},{"key":"3947_CR7","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1002\/jms.207","volume":"36","author":"IV Chernushevich","year":"2001","unstructured":"Chernushevich IV, Loboda AV, Thomson BA: An introduction to quadrupole-time-of-flight mass spectrometry. J Mass Spectrom 2001, 36: 849\u2013865. 10.1002\/jms.207","journal-title":"J Mass Spectrom"},{"key":"3947_CR8","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1126\/science.6385250","volume":"226","author":"ML Gross","year":"1984","unstructured":"Gross ML, Rempel DL: Fourier transform mass spectrometry. Science 1984, 226: 261\u2013268. 10.1126\/science.6385250","journal-title":"Science"},{"key":"3947_CR9","doi-asserted-by":"publisher","first-page":"388","DOI":"10.2174\/138920209789177638","volume":"10","author":"J Zhang","year":"2009","unstructured":"Zhang J, Gonzalez E, Hestilow T, Haskins W, Huang Y: Review of peak detection algorithms in liquid-chromatography-mass spectrometry. Curr Genomics 2009, 10: 388\u2013401. 10.2174\/138920209789177638","journal-title":"Curr Genomics"},{"key":"3947_CR10","doi-asserted-by":"publisher","first-page":"3385","DOI":"10.1021\/ac052212q","volume":"78","author":"P Du","year":"2006","unstructured":"Du P, Angeletti RH: Automatic Deconvolution of Isotope-Resolved Mass Spectra Using Variable Selection and Quantized Peptide Mass Distribution. Anal Chem 2006, 78: 3385\u20133392. 10.1021\/ac052212q","journal-title":"Anal Chem"},{"key":"3947_CR11","doi-asserted-by":"publisher","first-page":"1764","DOI":"10.1093\/bioinformatics\/bti254","volume":"21","author":"JS Morris","year":"2005","unstructured":"Morris JS, Coombes KR, Koomen J, Baggerly KA, Kobayashi R: Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 2005, 21: 1764\u20131775. 10.1093\/bioinformatics\/bti254","journal-title":"Bioinformatics"},{"key":"3947_CR12","doi-asserted-by":"publisher","first-page":"4107","DOI":"10.1002\/pmic.200401261","volume":"5","author":"KR Coombes","year":"2005","unstructured":"Coombes KR, Tsavachidis S, Morris JS, Baggerly KA, Hung MC, Kuerer HM: Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 2005, 5: 4107\u20134117. 10.1002\/pmic.200401261","journal-title":"Proteomics"},{"key":"3947_CR13","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1093\/bioinformatics\/btl355","volume":"22","author":"P Du","year":"2006","unstructured":"Du P, Kibbe WA, Lin SM: Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006, 22: 2059\u20132065. 10.1093\/bioinformatics\/btl355","journal-title":"Bioinformatics"},{"key":"3947_CR14","doi-asserted-by":"publisher","first-page":"i407","DOI":"10.1093\/bioinformatics\/btn143","volume":"24","author":"Y Wang","year":"2008","unstructured":"Wang Y, Zhou X, Wang H, Li K, Yao L, Wong STC: Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model. Bioinformatics 2008, 24: i407-i413. 10.1093\/bioinformatics\/btn143","journal-title":"Bioinformatics"},{"key":"3947_CR15","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1074\/mcp.M500141-MCP200","volume":"4","author":"X Li","year":"2005","unstructured":"Li X, Yi EC, Kemp CJ, Zhang H, Aebersold R: A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry. S Mol Cell Proteom 2005, 4: 1328\u20131340. 10.1074\/mcp.M500141-MCP200","journal-title":"S Mol Cell Proteom"},{"issue":"15","key":"3947_CR16","doi-asserted-by":"publisher","first-page":"1902","DOI":"10.1093\/bioinformatics\/btl276","volume":"22","author":"M Bellew","year":"2006","unstructured":"Bellew M, Coram M, Fitzgibbon M, Igra M, Randolph T, Wang P, May D, Eng J, Fang R, Lin C, Chen J, Goodlett D, Whiteaker J, Paulovich A, McIntosh M: A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC-MS. Bioinformatics 2006, 22(15):1902\u2013909. 10.1093\/bioinformatics\/btl276","journal-title":"Bioinformatics"},{"key":"3947_CR17","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1093\/bioinformatics\/btm385","volume":"23","author":"K Noy","year":"2007","unstructured":"Noy K, Fasulo D: Improved model-based, platform-independent feature extraction for mass spectrometry. Bioinformatics 2007, 23: 2528\u20132535. 10.1093\/bioinformatics\/btm385","journal-title":"Bioinformatics"},{"key":"3947_CR18","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/1471-2105-10-87","volume":"10","author":"N Jaitly","year":"2009","unstructured":"Jaitly N, Mayampurath A, Littlefield K, Adkins JN, Anderson GA, Smith RD: Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data. BMC bioinformatics 2009, 10: 87. 10.1186\/1471-2105-10-87","journal-title":"BMC bioinformatics"},{"key":"3947_CR19","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1186\/1471-2105-9-163","volume":"9","author":"M Sturm","year":"2008","unstructured":"Sturm M, Bertsch A, Gr\u00f6pl C, Hildebrandt A, Hussong R, Lange E, Pfeifer N, Schulz-Trieglaff O, Zerck A, Reinert K, Kohlbacher O: OpenMS - An open-source software framework for mass spectrometry. BMC Bioinformatics 2008, 9: 163. 10.1186\/1471-2105-9-163","journal-title":"BMC Bioinformatics"},{"key":"3947_CR20","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/0020-7381(83)85053-0","volume":"52","author":"JA Yergey","year":"1983","unstructured":"Yergey JA: A general approach to calculating isotopic distributions for mass spectrometry. Int J Mass Spectrom Ion Phys 1983, 52: 337\u2013349. 10.1016\/0020-7381(83)85053-0","journal-title":"Int J Mass Spectrom Ion Phys"},{"key":"3947_CR21","doi-asserted-by":"publisher","first-page":"2699","DOI":"10.1021\/ac00111a031","volume":"67","author":"AL Rockwood","year":"1995","unstructured":"Rockwood AL, Van Orden SL, Smith R: Rapid cacluation of isotope distributions. Anal Chem 1995, 67: 2699\u20132704. 10.1021\/ac00111a031","journal-title":"Anal Chem"},{"issue":"4","key":"3947_CR22","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/S1044-0305(99)00157-9","volume":"11","author":"DM Horn","year":"2000","unstructured":"Horn DM, Zubarev RA, McLafferty FW: Automated reduction and interpretation of high resolution electrospray mass spectra of large molecules. Journal of the American Society for Mass Spectrometry 2000, 11(4):320\u2013332. 10.1016\/S1044-0305(99)00157-9","journal-title":"Journal of the American Society for Mass Spectrometry"},{"key":"3947_CR23","first-page":"661","volume-title":"Proc of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"J Zhang","year":"2008","unstructured":"Zhang J, Wang H, Suffredini A, Gonzales D, Gonzales E, Huang Y, Zhou X: Bayesian peak detection for pro-TOF MS MALDI data. In Proc of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Las Vegas, NV, USA; 2008:661\u2013664."},{"key":"3947_CR24","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1093\/bioinformatics\/btm083","volume":"23","author":"P Du","year":"2007","unstructured":"Du P, Sudha R, Prystowsky MB, Angeletti RH: Data reduction of isotope-resolved LC-MS spectra. Bioinformatics 2007, 23: 1394\u20131400. 10.1093\/bioinformatics\/btm083","journal-title":"Bioinformatics"},{"key":"3947_CR25","doi-asserted-by":"publisher","first-page":"5383","DOI":"10.1021\/ac025747h","volume":"74","author":"A Keller","year":"2002","unstructured":"Keller A, Nesvizhskii AI, 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\u20135392. 10.1021\/ac025747h","journal-title":"Anal Chem"},{"key":"3947_CR26","unstructured":"Matlab mspeaks[http:\/\/www.mathworks.com\/access\/helpdesk\/help\/toolbox\/bioinfo\/ref\/mspeaks.html]"},{"key":"3947_CR27","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1186\/1471-2105-9-423","volume":"9","author":"O Schulz-Trieglaff","year":"2008","unstructured":"Schulz-Trieglaff O, Pfeifer N, Gr\u00f6pl C, Kohlbacher O, Reinert K: LC-MSsim - a simulation software for liquid chromatography mass spectrometry data. BMC Bioinformatics 2008, 9: 423. 10.1186\/1471-2105-9-423","journal-title":"BMC Bioinformatics"},{"key":"3947_CR28","doi-asserted-by":"publisher","first-page":"1070","DOI":"10.1093\/bioinformatics\/btn078","volume":"24","author":"P Du","year":"2008","unstructured":"Du P, Stolovitzky G, Horvatovich P, Bischoff R, Lim J, Suits F: A noise model for mass spectrometry based proteomics. Bioinformatics 2008, 24: 1070\u20131077. 10.1093\/bioinformatics\/btn078","journal-title":"Bioinformatics"},{"key":"3947_CR29","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1177\/117693510500100103","volume":"1","author":"KR Coombes","year":"2005","unstructured":"Coombes KR: Understanding the characteristics of mass spectrometry data through the use of simulation. Cancer Informatics 2005, 1: 41\u201352.","journal-title":"Cancer Informatics"},{"key":"3947_CR30","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","volume":"6","author":"S Geman","year":"1984","unstructured":"Geman S, Geman D: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 1984, 6: 721\u2013741. 10.1109\/TPAMI.1984.4767596","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3947_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-4145-2","volume-title":"Monte Carlo Statistical Methods","author":"CP Robert","year":"2004","unstructured":"Robert CP, Casella G: Monte Carlo Statistical Methods. New York, NY, USA: Springer; 2004."},{"key":"3947_CR32","volume-title":"Pattern Classification","author":"R Duda","year":"2001","unstructured":"Duda R, Hart P: Pattern Classification. New York, NY, USA: JohnWiley&Sons; 2001."},{"key":"3947_CR33","unstructured":"Shewanella Oneidensis data set[http:\/\/omics.pnl.gov]"},{"key":"3947_CR34","unstructured":"OpenMS\/TOPP website[http:\/\/open-ms.sourceforge.net]"},{"key":"3947_CR35","unstructured":"Bruker peptide calibration standard[http:\/\/www2.bdal.de\/data\/care-online_data\/206195\/PI_206195_Peptide%20Cal%20Stand_V2.pdf]"},{"key":"3947_CR36","unstructured":"Bruker Daltonics website[http:\/\/www.bdal.de]"},{"issue":"15","key":"3947_CR37","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1093\/bioinformatics\/btm281","volume":"23","author":"ME Monroe","year":"2007","unstructured":"Monroe ME, Tolic N, Jaitly N, Shaw JL, Adkins JN, Smith RD: VIPER: an advanced software package to support high-throughput LC-MS peptide identification. Bioinformatics 2007, 23(15):2021\u20132023. 10.1093\/bioinformatics\/btm281","journal-title":"Bioinformatics"},{"key":"3947_CR38","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1074\/mcp.M500426-MCP200","volume":"5","author":"DA Stead","year":"2006","unstructured":"Stead DA, Preece A, Brown JP: Universal metrics for quality assessment of protein identifications by mass spectrometry. Mol Cell Prot 2006, 5: 1205\u20131211. 10.1074\/mcp.M500426-MCP200","journal-title":"Mol Cell Prot"},{"key":"3947_CR39","doi-asserted-by":"publisher","first-page":"e12","DOI":"10.1371\/journal.pcbi.0040012","volume":"4","author":"L McHugh","year":"2008","unstructured":"McHugh L, Arthur JW: Computational Methods for Protein Identification from Mass Spectrometry Data. PLoS Comput Biol 2008, 4: e12. 10.1371\/journal.pcbi.0040012","journal-title":"PLoS Comput Biol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1471-2105-11-490.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T05:25:59Z","timestamp":1630473959000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/1471-2105-11-490"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2010,9,29]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2010,12]]}},"alternative-id":["3947"],"URL":"https:\/\/doi.org\/10.1186\/1471-2105-11-490","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2010,9,29]]},"assertion":[{"value":"1 June 2010","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2010","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 September 2010","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"490"}}