BPDA2d-a 2D global optimization-based Bayesian peptide detection algorithm for liquid chromatograph-mass spectrometry
Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually wor...
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| Published in | Bioinformatics Vol. 28; no. 4; pp. 564 - 572 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Oxford University Press
15.02.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1367-4803 1367-4811 1460-2059 1367-4811 |
| DOI | 10.1093/bioinformatics/btr675 |
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| Abstract | Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides.
Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together-especially by deriving information across charge states-low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy.
Availability: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/
Contact:
Michelle.Zhang@utsa.edu; edward@ece.tamu.edu
Supplementary information:
Supplementary data are available at Bioinformatics online. |
|---|---|
| AbstractList | Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides.MOTIVATIONPeptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides.BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together--especially by deriving information across charge states--low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy.RESULTSBPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together--especially by deriving information across charge states--low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy.The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/.AVAILABILITYThe BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/. Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together-especially by deriving information across charge states-low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy. Availability: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/ Contact: Michelle.Zhang@utsa.edu; edward@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together—especially by deriving information across charge states—low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography–mass spectrometry data, according to sensitivity and detection accuracy. Availability: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/ Contact: Michelle.Zhang@utsa.edu; edward@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online. Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. Results: BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together—especially by deriving information across charge states—low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography–mass spectrometry data, according to sensitivity and detection accuracy. Availability: The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/ Contact: Michelle.Zhang@utsa.edu; edward@ece.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online. Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and thus may be prone to error propagation and ineffective to detect overlapping peptides. In addition, existing algorithms usually work at different charge states separately, isolating useful information that can be drawn from other charge states, which may lead to poor detection of low abundance peptides. BPDA2d models spectra as a mixture of candidate peptide signals and systematically evaluates all possible combinations of possible peptide candidates to interpret the given spectra. For each candidate, BPDA2d takes into account its elution profile, charge state distribution and isotope pattern, and it combines all evidence to infer the candidate's signal and existence probability. By piecing all evidence together--especially by deriving information across charge states--low abundance peptides can be better identified and peptide detection rates can be improved. Instead of local template matching, BPDA2d performs global optimization for all candidates and systematically optimizes their signals. Since BPDA2d looks for the optimal among all possible interpretations of the given spectra, it has the capability in handling complex spectra where features overlap. BPDA2d estimates the posterior existence probability of detected peptides, which can be directly used for probability-based evaluation in subsequent processing steps. Our experiments indicate that BPDA2d outperforms state-of-the-art detection methods on both simulated data and real liquid chromatography-mass spectrometry data, according to sensitivity and detection accuracy. The BPDA2d software package is available at http://gsp.tamu.edu/Publications/supplementary/sun11a/. |
| Author | Zhang, Jianqiu Dougherty, Edward R. Sun, Youting Braga-Neto, Ulisses |
| AuthorAffiliation | 1 Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, 2 Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, 3 Computational Biology Division, Translational Genomics Research Institution, Phoenix, AZ 85004 and 4 Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA |
| AuthorAffiliation_xml | – name: 1 Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, 2 Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, 3 Computational Biology Division, Translational Genomics Research Institution, Phoenix, AZ 85004 and 4 Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA |
| Author_xml | – sequence: 1 givenname: Youting surname: Sun fullname: Sun, Youting organization: 1Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, 2Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, 3Computational Biology Division, Translational Genomics Research Institution, Phoenix, AZ 85004 and 4Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA – sequence: 2 givenname: Jianqiu surname: Zhang fullname: Zhang, Jianqiu organization: 1Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, 2Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, 3Computational Biology Division, Translational Genomics Research Institution, Phoenix, AZ 85004 and 4Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA – sequence: 3 givenname: Ulisses surname: Braga-Neto fullname: Braga-Neto, Ulisses organization: 1Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, 2Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, 3Computational Biology Division, Translational Genomics Research Institution, Phoenix, AZ 85004 and 4Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA – sequence: 4 givenname: Edward R. surname: Dougherty fullname: Dougherty, Edward R. organization: 1Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, 2Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX 78249, 3Computational Biology Division, Translational Genomics Research Institution, Phoenix, AZ 85004 and 4Department of Bioinformatics and Computational Biology, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA |
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| CitedBy_id | crossref_primary_10_1016_j_talanta_2020_121580 crossref_primary_10_1021_acs_analchem_5b01521 crossref_primary_10_1109_TCBB_2014_2377723 crossref_primary_10_1371_journal_pone_0072951 crossref_primary_10_1186_1471_2164_13_S6_S2 crossref_primary_10_1002_pmic_201800444 crossref_primary_10_1016_j_ultramic_2020_113014 |
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| Snippet | Motivation: Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template... Peptide detection is a crucial step in mass spectrometry (MS) based proteomics. Most existing algorithms are based upon greedy isotope template matching and... |
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| SubjectTerms | Algorithms Bayes Theorem Biological and medical sciences Chromatography, Liquid Fundamental and applied biological sciences. Psychology General aspects Mass Spectrometry Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Original Papers Peptides - analysis Probability Proteomics - methods Software |
| Title | BPDA2d-a 2D global optimization-based Bayesian peptide detection algorithm for liquid chromatograph-mass spectrometry |
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