An in silico MS/MS library for automatic annotation of novel FAHFA lipids

Journal of Cheminformatics

Volumn 7, Issue 1, 2015, Pages

  Ma, Yan ^a   Kind, Tobias ^a   Vaniya, Arpana ^a   Gennity, Ingrid ^a   Fahrmann, Johannes F ^a   Fiehn, Oliver ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

FAHFA; In silico library; Lipids; MS MS

Indexed keywords

EID: 84947485234     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/s13321-015-0104-4     Document Type: Article

References (18)

1. Yore MM, Syed I, Moraes-Vieira PM, Zhang T, Herman MA, Homan EA, Patel RT, Lee J, Chen S, Peroni OD (2014) Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell 159(2):318-332
2. Kind T, Liu KH, Lee do Y, DeFelice B, Meissen JK, Fiehn O (2013) Lipidblast in silico tandem mass spectrometry database for lipid identification. Nat Methods 10(8):755-758
3. Park HM, Shon JC, Lee MY, Liu K-H, Kim JK, Lee SJ, Lee CH (2014) Mass spectrometry-based metabolite profiling in the mouse liver following exposure to ultraviolet B radiation. PLoS One 9(10):e109479
4. Kim HY, Lee MY, Park HM, Park YK, Shon JC, Liu KH, Lee CH (2015) Urine and serum metabolite profiling of rats fed a high-fat diet and the anti-obesity effects of caffeine consumption. Molecules 20(2):3107-3128
5. Ogawa T, Furuhashi T, Okazawa A, Nakai R, Nakazawa M, Kind T, Fiehn O, Kanaya S, Arita M, Ohta D (2014) Exploration of polar lipid accumulation profiles in Euglena gracilis using LipidBlast, an MS/MS spectral library constructed in silico. Biosci Biotechnol Biochem 78(1):14-18
6. Kind T, Meissen JK, Yang D, Nocito F, Vaniya A, Cheng YS, VanderGheynst JS, Fiehn O (2012) Qualitative analysis of algal secretions with multiple mass spectrometric platforms. J Chromatogr A 1244:139-147
7. Tsugawa H, Cajka T, Kind T, Ma Y, Higgins B, Ikeda K, Kanazawa M, VanderGheynst J, Fiehn O, Arita M (2015) MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods 12(6):523-526
8. Kind T, Okazaki Y, Saito K, Fiehn O (2014) LipidBlast templates as flexible tools for creating new in-silico tandem mass spectral libraries. Anal Chem 86(22):11024-11027
9. McEvoy T, Coull G, Broadbent P, Hutchinson J, Speake B (2000) Fatty acid composition of lipids in immature cattle, pig and sheep oocytes with intact zona pellucida. J Reprod Fertil 118(1):163-170
10. Käkelä R, Hyvärinen H (1996) Site-specific fatty acid composition in adipose tissues of several northern aquatic and terrestrial mammals. Comp Biochem Physiol B: Biochem Mol Biol 115(4):501-514
11. Svennerholm L (1968) Distribution and fatty acid composition of phosphoglycerides in normal human brain. J Lipid Res 9(5):570-579
12. Kahn B, Herman M, Saghatelian A, Homan E Lipids that increase insulin sensitivity and methods of using the same. Google Patents, 2013
13. Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27(6):747-751
14. Exact Mass Calculator. http://www.sisweb.com/referenc/tools/exactmass.htm. Accessed 27 Feb 2015
15. Fahy E, Sud M, Cotter D, Subramaniam S (2007) LIPID MAPS online tools for lipid research. Nucleic Acids Res 35(suppl 2):W606-W612
16. ChemAxon. http://www.chemaxon.com. Accessed 9 Mar 2015
17. Lib2NIST. http://chemdata.nist.gov/. Accessed 3 Apr 2015
18. Stein SE, Scott DR (1994) Optimization and testing of mass spectral library search algorithms for compound identification. J Am Soc Mass Spectrom 5(9):859-866