Statistical agglomeration: Peak summarization for direct infusion lipidomics

Bioinformatics

Volumn 29, Issue 19, 2013, Pages 2445-2451

  Smith, Rob ^a   Anthonymuthu, Tamil S ^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

LIPID;

ALGORITHM; ARTICLE; CHEMISTRY; COMPUTER LANGUAGE; COMPUTER PROGRAM; GENOMICS; INTERNET; MASS SPECTROMETRY; METHODOLOGY; PROCEDURES;

ALGORITHMS; GENOMICS; INTERNET; LIPIDS; MASS SPECTROMETRY; PROGRAMMING LANGUAGES; SOFTWARE;

EID: 84897366782     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt376     Document Type: Article

References (18)

1. Deutsch,E. (2008) mzML: a single, unifying data format for mass spectrometer output. Proteomics, 8, 2776-2777.
2. Ejsing,C.S. et al. (2006) Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. Anal. Chem., 78, 6202-6214.
3. Ekroos,K. et al. (2002) Quantitative profiling of phospholipids by multiple precursor ion scanning on a hybrid quadrupole time-of-flight mass spectrometer. Anal. Chem., 74, 941-949.
4. Frank,A.M. et al. (2008) Clustering millions of tandem mass spectra. J. Proteome Res., 7, 113-122.
5. Han,X. and Gross,R.W. (2005) Shotgun lipidomics: electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrom. Rev., 24, 367-412.
6. Herzog,R. et al. (2011) A novel informatics concept for high-throughput shotgun lipidomics based on the molecular fragmentation query language. Genome Biol., 12, R8.
7. Liu,J. et al. (2007) Methods for peptide identification by spectral comparison. Proteome Sci., 5, 3.
8. Noyce,A.B. et al. (2013) Mspire-simulator: LC-MS shotgun proteomic simulator for creating realistic gold standard data. J. Proteome Res, epub ahead of print.
9. Orešič,M. (2009) Bioinformatics and computational approaches applicable to lipidomics. Eur. J. Lipid Sci. Technol., 111, 99-106.
10. Samuelsson,J. et al. (2004) Modular, scriptable and automated analysis tools for high-throughput peptide mass fingerprinting. Bioinformatics, 20, 3628-3635.
11. Satterthwaite,F.E. (1946) An approximate distribution of estimates of variance components. Biometrics, 2, 110-114.
12. Schwudke,D. et al. (2006) Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. Anal. Chem., 78, 585-595.
13. Schwudke,D. et al. (2011) Shotgun lipidomics on high resolution mass spectrometers. Cold Spring Harb. Perspect. Biol., 3, a004614.
14. Song,H. et al. (2007) Algorithm for processing raw mass spectrometric data to identify and quantitate complex lipid molecular species in mixtures by datadependent scanning and fragment ion database searching. J. Am. Soc. Mass Spectrom., 18, 1848-1858.
15. Sturm,M. and Kohlbacher,O. (2009) Toppview: An open-source viewer for mass spectrometry data. J. Proteome Res., 8, 3760-3763.
16. Watson,A.D. (2006) Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems. J. Lipid Res., 47, 2101-2111.
17. Welch,B.L. (1947) The generalization of Student's problem when several different population varlances are involved. Biometrika, 34, 28-35.
18. Wilkins,M.R. et al. (2006) Guidelines for the next 10 years of proteomics. Proteomics, 6, 4-8.