Proteomics, lipidomics, metabolomics: A mass spectrometry tutorial from a computer scientist's point of view

BMC Bioinformatics

Volumn 15, Issue , 2014, Pages

  Smith, Rob ^a   Mathis, Andrew D ^b   Ventura, Dan ^a   Prince, John T ^b  

^a 3361 TMCB   (United States)

^b Brigham Young University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CHEMICAL ANALYSIS; COMPUTATION THEORY; DIAGNOSIS; MASS SPECTROMETRY; MOLECULAR BIOLOGY; SPECTROMETRY;

ALGORITHMIC SOLUTIONS; CHEMICAL THEORIES; COMPUTER SCIENTISTS; DATA INTERPRETATION; DISEASE DIAGNOSTICS; DOMAIN-SPECIFIC KNOWLEDGE; MASS SPECTROMETRY DATA; RESEARCH INTERESTS;

DRUG PRODUCTS;

ALGORITHM; ARTICLE; MASS SPECTROMETRY; MATHEMATICS; METABOLOMICS; METHODOLOGY; PROTEOMICS;

ALGORITHMS; MASS SPECTROMETRY; MATHEMATICS; METABOLOMICS; PROTEOMICS;

EID: 84907424497     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-S7-S9     Document Type: Review

References (54)

1. Cappadona S, Baker PR, Cutillas PR, Heck AJR, van Breukelen B: Current challenges in software solutions for mass spectrometry-based quantitative proteomics. Amino Acids 2012, 43(3):1-22.
2. Wenk MR: The Emerging Field of Lipidomics. Nature Reviews Drug Discovery 2005, 4:594-601.
3. Hemminger BM, Losi T, Bauers A: Survey of bioinformatics programs in the United States. Journal of the American Society for Information Science and Technology 2005, 56(5):529-537.
4. German JB, Gillies LA, Smilowitz JT, Zivkovic AM, Watkins SM: Lipidomics and lipid profiling in metabolomics. Current Opinion in Lipidology 2007, 18(1):66-71.
5. Feng L, Prestwich GD: Functional Lipidomics. Taylor & Francis, 2005.
6. Fahy E, Subramaniam S, Murphy RC, Nishijima M, Raetz CR, Shimizu T, Spener F, van Meer G, Wakelam MJ, Dennis EA: Update of the LIPID MAPS comprehensive classification system for lipids. J Lipid Res. 2009, 50(Supplement):9-14.
7. Kraegen EW, Cooney GJ, Ye JM, Thompson AL, Furler SM: The Role of Lipids in the Pathogenesis of Muscle Insulin Resistance and Beta Cell Faiture in Type II Diabetes and Obesity. Experimental and Clinical Endocrinology & Diabetes 2001, 109(Suppl 2):189-201.
8. Morris M, Watkins SM: Focused metabolomic profiling in the drug development process: advances from lipid profiling. Current Opinion in Chemical Biology 2005, 9(4):407-412.
9. Fiehn O: Metabolomics-the link between genotypes and phenotypes. Plant Molecular Biology 2002, 48(1-2):155-171.
10. Samuelsson J, Dalevi D, Levander F, Rögnvaldsson T: Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting. Bioinformatics 2004, 20(18):3628-3635.
11. Murray KK, Boyd RK, Eberlin MN, Langley GJ, Li L, Naito Y, et al: Definitions of terms relating to mass spectrometry (IUPAC Recommendations 2013). Pure and Applied Chemistry 2013, (None).
12. Domon B, Aebersold R: Mass spectrometry and protein analysis. Science Signaling 2006, 312(5771):212.
13. Dakna M, He Z, Yu WC, Mischak H, Kolch W, et al: Technical, bioinformatical and statistical aspects of liquid chromatography/mass spectrometry (LC-MS) and capillary electrophoresis-mass spectrometry (CE-MS) based clinical proteomics: A critical assessment. Journal of Chromatography B 2009, 877(13):1250-1258.
14. Mischak H, Coon JJ, Novak J, Weissinger EM, Schanstra JP, Dominiczak AF: Capillary electrophoresis-mass spectrometry as a powerful tool in biomarker discovery and clinical diagnosis: an update of recent developments. Mass Spectrometry Reviews 2009, 28(5):703-724.
15. Han X, Gross RW: Electrospray Ionization Mass Spectroscopic Analysis of Human Erythrocyte Plasma Membrane Phospholipids. Proceedings of the National Academy of Sciences of the United States of America 1994, 91(22):10635-10639.
16. Cole RB: Electrospray Ionization Mass Spectrometry: Fundamentals, Instrumentation, and Applications. Wiley-Interscience, New York; 1997.
17. Griffiths WJ, Wang Y: Mass spectrometry: from proteomics to metabolomics and lipidomics. Chem Soc Rev 2009, 38:1882-1896.
18. Michalski A, Cox J, Mann M: More than 100,000 Detectable Peptide Species Elute in Single Shotgun Proteomics Runs but the Marjority is Inaccessible to Data-Dependent LC-MS/MS. Journal of Proteome Research 2011, 10:1785-1793.
19. Eng JK, McCormack AL, Yates JR III: An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. Journal of the American Society for Mass Spectrometry 1994, 5(11):976-989.
20. Schmelzer K, Fahy E, Subramaniam S, Dennis EA: The Lipid Maps Initiative in Lipidomics. In Lipidomics and Bioactive Lipids: Mass-Spectrometry-Based Lipid Analysis Methods in Enzymology Brown, H.A 2007, 432:171-183.
21. Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G: METLIN: a metabolite mass spectral database. Therapeutic Drug Monitoring 2005, 27(6):747-751.
22. Brusniak MY, Bodenmiller B, Campbell D, Cooke K, Eddes J, Garbutt A, Lau H, Letarte S, Mueller L, Sharma V, et al: Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics. BMC Bioinformatics 2008, 9(1):542.
23. Podwojski K, Fritsch A, Chamrad DC, Paul W, Sitek B, Stühler K, Mutzel P, Stephan C, Meyer HE, Urfer W: Retention time alignment algorithms for LC/MS data must consider non-linear shifts. Bioinformatics 2009, 25(6):758-764.
24. Lange E, Tautenhahn R, Neumann S, Gröpl C: Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements. BMC Bioinformatics 2008, 9(1):375.
25. Listgarten J, Neal RM, Roweis ST, Wong P, Emili A: Difference detection in LCMS data for protein biomarker discovery. Bioinformatics 2007, 23(2):198-204.
26. Jeffries N: Algorithms for alignment of mass spectrometry proteomic data. Bioinformatics 2005, 21(14):3066-3073.
27. Mueller LN, Rinner O, Schmidt A, Letarte S, Bodenmiller B, Brusniak MY, Vitek O, Aebersold R, Müller M: SuperHirn-a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 2007, 7(19):3470-3480.
28. Smith R, Prince JT: JAMSS: Proteomics mass spectrometry simulation in java. Bioinformatics (in review).
29. Noyce AB, Smith R, Dalgliesh J, Taylor RM, Erb KC, Okuda N, Prince JT: Mspire-Simulator: LC-MS Shotgun Proteomic Simulator for Creating Realistic Gold Standard Data. Journal of Proteome Research 2013.
30. Bielow C, Aiche S, Andreotti S, Reinert K: MSSimulator: Simulation of mass spectrometry data. Journal of Proteome Research 2011, 10(7):2922-2929.
31. Schulz-Trieglaff O, Pfeifer N, Gröpl C, Kohlbacher O, Reinert K: LC-MSsim-a simulation software for liquid chromatography mass spectrometry data. BMC Bioinformatics 2008, 9(1):423.
32. Egertson JD, Eng JK, Bereman MS, Hsieh EJ, Merrihew GE, MacCoss MJ: De Novo Correction of Mass Measurement Error in Low Resolution Tandem MS Spectra for Shotgun Proteomics. Journal of The American Society for Mass Spectrometry 2012, 1-8.
33. Smith R, Ventura D, Prince JT: LC-MS Alignment in Theory and Practice: A Comprehensive Algorithmic Review. Briefings in Bioinformatics 2013.
34. Smith R, Ventura D, Prince JT: Novel Algorithms and the Benefits of Comparative Validation. Bioinformatics 2013, 29(12):1583-1585.
35. Conley C, Smith R, Torgrip RJO, Taylor RM, Tautenhahn R, Prince JT: Massifquant: Open-source Kalman filter based XC-MS feature detection. Bioinformatics (in review).
36. Smith R, Anthonymuthu TS, Ventura D, Prince JT: Statistical Agglomeration: Peak Summarization for Direct Infusion Lipidomics. Bioinformatics 2013.
37. Sugimoto M, Kawakami M, Robert M, Soga T, Tomita M: Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis. Current Bioinformatics 2012, 7(1):96.
38. Babushok VI, Zenkevich IG: Retention characteristics of peptides in RP-LC: Peptide retention prediction. Chromatographia 2010, 72(9-10):781-797.
39. Wolski WE, Farrow M, Emde AK, Lehrach H, Lalowski M, Reinert K: Analytical model of peptide mass cluster centres with applications. Proteome Science 2006, 4(1):18.
40. Köfeler HC, Fauland A, Rechberger GN, Trötzmüller M: Mass Spectrometry Based Lipidomics: An Overview of Technological Platforms. Metabolites 2012, 2(1):19-38.
41. Taylor CF, Hermjakob H, Julian RK Jr, Garavelli JS, Aebersold R, Apweiler R: The work of the human proteome organisation's proteomics standards initiative (HUPO PSI). Omics: a Journal of Integrative Biology 2006, 10(2):145-151.
42. Whetzel PL, Parkinson H, Causton HC, Fan L, Fostel J, Fragoso G, Game L, Heiskanen M, Morrison N, Rocca-Serra P, et al: The MGED Ontology: a resource for semantics-based description of microarray experiments. Bioinformatics 2006, 22(7):866-873.
43. Li XJ, Zhang H, Ranish JA, Aebersold R: Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Analytical Chemistry 2003, 75(23):6648-6657.
44. Liu H, Sadygov RG, Yates JR III: A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Analytical Chemistry 2004, 76(14):4193-4201.
45. Fischer B, Grossmann J, Roth V, Gruissem W, Baginsky S, Buhmann JM: Semi-supervised LC/MS alignment for differential proteomics. Bioinformatics 2006, 22(14):132-140.
46. Annesley TM: Ion suppression in mass spectrometry. Clinical Chemistry 2003, 49(7):1041-1044.
47. Mueller LN, Brusniak MY, Mani DR, Aebersold R: An Assessment of Software Solutions for the Analysis of Mass Spectrometry Based Quantitative Proteomics Data. Journal of Proteome Research 2008, 7(1):51-61.
48. Wong JW, Sullivan MJ, Cagney G: Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments. Briefings in Bioinformatics 2008, 9(2):156-165.
49. Wang W, Zhou H, Lin H, Roy S, Shaler TA, Hill LR, Norton S, Kumar P, Anderle M, Becker CH: Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. Analytical Chemistry 2003, 75(18):4818-4826.
50. Biemann K: Mass spectrometry of peptides and proteins. Annual Review of Biochemistry 1992, 61(1):977-1010.
51. Elias JE, Gibbons FD, King OD, Roth FP, Gygi SP: Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nature Biotechnology 2004, 22(2):214-219.
52. Arnold RJ, Jayasankar N, Aggarwal D, Tang H, Radivojac P: A machine learning approach to predicting peptide fragmentation spectra. Pacific Symposium on Biocomputing 2006, 11:219-230.
53. Frank A, Pevzner P: PepNovo: de novo peptide sequencing via probabilistic network modeling. Analytical Chemistry 2005, 77(4):964-973.
54. Chong KF, Leong HW: Tutorial on de novo peptide sequencing using MS/ MS mass spectrometry. Journal of Bioinformatics and Computational Biology 2012, 10(06).