Prediction of peptides observable by mass spectrometry applied at the experimental set level

BMC Bioinformatics

Volumn 8, Issue SUPPL. 7, 2007, Pages

  Sanders, William S ^a   Bridges, Susan M ^a,b   McCarthy, Fiona M ^a,c   Nanduri, Bindu ^a,c   Burgess, Shane C ^a,c,d  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

^c MISSISSIPPI STATE UNIVERSITY   (United States)

^d MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL CONDITIONS; CLASSIFICATION RESULTS; MULTIDIMENSIONAL PROTEINS; NEURAL NETWORK CLASSIFIER; PREDICTIVE PERFORMANCE; PROTEIN IDENTIFICATION; PROTEIN QUANTIFICATION; PROTEOLYTIC DIGESTION;

AMINO ACIDS; DATA PROCESSING; EXPERIMENTS; FORECASTING; MASS SPECTROMETRY; MOLECULAR BIOLOGY; NEURAL NETWORKS; PEPTIDES; SYSTEMS ANALYSIS;

CLASSIFICATION (OF INFORMATION);

AMINO ACID; PEPTIDE; PROTEIN;

ACCURACY; AMINO ACID ANALYSIS; AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTIFICIAL NEURAL NETWORK; CLASSIFIER; CONFERENCE PAPER; CONTROLLED STUDY; EXPERIMENTAL DESIGN; HUMAN; LIQUID CHROMATOGRAPHY; MASS SPECTROMETRY; NONHUMAN; PEPTIDE ANALYSIS; PREDICTION; PROTEIN DATABASE; PROTEOMICS; STATISTICAL ANALYSIS; SYSTEM ANALYSIS; VALIDATION STUDY; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; METHODOLOGY; MOLECULAR GENETICS; PEPTIDE MAPPING; RESEARCH; SEQUENCE ANALYSIS;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; DATABASES, PROTEIN; MASS SPECTROMETRY; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; PEPTIDE MAPPING; PROTEINS; RESEARCH; SEQUENCE ANALYSIS, PROTEIN;

EID: 38349172005     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-8-S7-S23     Document Type: Conference Paper

References (17)

1. Hernandez H Robinson CV Dynamic protein complexes: Insights from mass spectrometry J Biol Chem 2001 276 50 46685-46688 10.1074/jbc.R100024200 11585844
2. Aebersold R Mann M Mass spectrometry-based proteomics Nature 2003 422 6928 198-207 10.1038/nature01511 12634793
3. Veenstra TD Conrads TP Issaq HJ What to do with "one-hit wonders"? Electrophoresis 2004 25 9 1278-1279 10.1002/ elps.200490007 15174049
4. Mallick P Schirle M Chen SS Flory MR Lee H Martin D Ranish J Raught B Schmitt R Werner T Computational prediction of proteotypic peptides for quantitative proteomics Nat Biotechnol 2007 25 1 125-131 10.1038/nbt1275 17195840
5. Buza JJ Burgess SC Modeling the proteome of a Marek's disease transformed cell line: A natural animal model for CD30 overexpressing lymphomas Proteomics 2007 7 8 1316-1326 10.1002/pmic.200600946 17443643
6. McCarthy FM Cooksey AM Wang N Bridges SM Pharr GT Burgess SC Modeling a whole organ using proteomics: The avian bursa of Fabricius Proteomics 2006 6 9 2759-2771 10.1002/pmic.200500648 16596704
7. Lu P Vogel C Wang R Yao X Marcotte EM Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation Nat Biotechnol 2007 25 1 117-124 10.1038/ nbt1270 17187058
8. Kuster B Schirle M Mallick P Aebersold R Scoring proteomes with proteotypic peptide probes Nat Rev Mol Cell Biol 2005 6 7 577- 583 10.1038/nrm1683 15957003
9. Richard E Higgs MDK Valentina Gelfanova Jon P Butler John E Hale Comprehensive Label-Free Method for the Relative Quantification of Proteins from Biological Samples Journal of Proteome Research 2005 4 1442-1450 10.1021/pr050109b 16083298
10. Washburn MP Ulaszek RR Yates JR 3rd Reproducibility of quantitative proteomic analyses of complex biological mixtures by multidimensional protein identification technology Anal Chem 2003 75 19 5054-5061 10.1021/ ac034120b 14708778
11. Nanduri B Lawrence ML Boyle CR Ramkumar M Burgess SC Effects of subminimum inhibitory concentrations of antibiotics on the Pasteurella multocida proteome J Proteome Res 2006 5 3 572-580 10.1021/pr050360r 16512672
12. Zhang B VerBerkmoes NC Langston MA Uberbacher E Hettich RL Samatova NF Detecting differential and correlated protein expression in label-free shotgun proteomics J Proteome Res 2006 5 11 2909-2918 10.1021/pr0600273 17081042
13. Kawashima S Kanehisa M AAindex: Amino acid index database Nucleic Acids Res 2000 28 1 374 102411 10592278 10.1093/nar/28.1.374
14. Witten IH Frank E Data Mining: Practical Machine Learning Tools and Techniques San Francisco: Morgan Kaufmann 2nd edition 2005
15. Burgess SC Young JR Baaten BJ Hunt L Ross LN Parcells MS Kumar PM Tregaskes CA Lee LF Davison TF Marek's disease is a natural model for lymphomas overexpressing Hodgkin's disease antigen (CD30) Proc Natl Acad Sci USA 2004 101 38 13879-13884 518847 15356338 10.1073/pnas.0305789101
16. Burgess SC Davison TF Identification of the neoplastically transformed cells in Marek's disease herpesvirus-induced lymphomas: Recognition by the monoclonal antibody AV37 J Virol 2002 76 14 7276-7292 136297 12072527 10.1128/JVI.76.14.7276-7292.2002
17. Gasteiger E Hoogland C Gattiker A Duvaud S Wilkins MR Appel RD Bairoch A Protein Identification and Analysis Tools on the ExPASy Server The Proteomics Protocols Handbook Humana Press Walker JM 2005