30. Sartain MJ, Dick DL, Rithner CD, Crick DC,
Belisle JT (2011) Lipidomic analyses of Mycobacterium tuberculosis based on accurate mass
measurements and the novel “Mtb LipidDB”. J
Lipid Res 52(5):861–872. https://doi.org/
10.1194/jlr.M010363
31. Galagan JE, Minch K, Peterson M,
Lyubetskaya A, Azizi E, Sweet L, Gomes A,
Rustad T, Dolganov G, Glotova I, Abeel T,
Mahwinney C, Kennedy AD, Allard R,
Brabant W, Krueger A, Jaini S, Honda B, Yu
WH, Hickey MJ, Zucker J, Garay C, Weiner B,
Sisk P, Stolte C, Winkler JK, Van de Peer Y,
Iazzetti P, Camacho D, Dreyfuss J, Liu Y,
Dorhoi A, Mollenkopf HJ, Drogaris P,
Lamontagne J, Zhou Y, Piquenot J, Park ST,
Raman S, Kaufmann SH, Mohney RP,
Chelsky D, Moody DB, Sherman DR, Schoolnik GK (2013) The Mycobacterium tuberculosis regulatory network and hypoxia. Nature
499(7457):178–183.
https://doi.org/10.
1038/nature12337
32. Layre E, Lee HJ, Young DC, Martinot AJ,
Buter J, Minnaard AJ, Annand JW, Fortune
SM, Snider BB, Matsunaga I, Rubin EJ,
Alber T, Moody DB (2014) Molecular
profiling of Mycobacterium tuberculosis identifies tuberculosinyl nucleoside products of the
virulence-associated enzyme Rv3378c. Proc
Natl Acad Sci U S A 111(8):2978–2983.
https://doi.org/10.1073/pnas.1315883111
33. Sabareesh V, Singh G (2013) Mass spectrometry based lipid(ome) analyzer and molecular
platform: a new software to interpret and analyze electrospray and/or matrix-assisted laser
desorption/ionization mass spectrometric
data of lipids: a case study from Mycobacterium
tuberculosis. J Mass Spectrometry 48
(4):465–477. https://doi.org/10.1002/jms.
3163
34. Smith CA, Want EJ, O’Maille G, Abagyan R,
Siuzdak G (2006) XCMS: processing mass
spectrometry data for metabolite profiling
using nonlinear peak alignment, matching,
and
identification.
Anal
Chem
78
(3):779–787.
https://doi.org/10.1021/
ac051437y
35. Pluskal T, Castillo S, Villar-Briones A, Oresic
M (2010) MZmine 2: modular framework for
processing, visualizing, and analyzing mass
spectrometry-based molecular profile data.
BMC Bioinformatics 11:395. https://doi.
org/10.1186/1471-2105-11-395
36. 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. https://doi.org/10.1038/
nmeth.3393
37. Adusumilli R, Mallick P (2017) Data conversion with ProteoWizard msConvert. Methods
Mol Biol 1550:339–368. https://doi.org/10.
1007/978-1-4939-6747-6_23
38. Kessner D, Chambers M, Burke R, Agus D,
Mallick P (2008) ProteoWizard: open source
software for rapid proteomics tools development. Bioinformatics 24(21):2534–2536.
https://doi.org/10.1093/bioinformatics/
btn323
Targeted Lipidomics
577
Belisle JT (2011) Lipidomic analyses of Mycobacterium tuberculosis based on accurate mass
measurements and the novel “Mtb LipidDB”. J
Lipid Res 52(5):861–872. https://doi.org/
10.1194/jlr.M010363
31. Galagan JE, Minch K, Peterson M,
Lyubetskaya A, Azizi E, Sweet L, Gomes A,
Rustad T, Dolganov G, Glotova I, Abeel T,
Mahwinney C, Kennedy AD, Allard R,
Brabant W, Krueger A, Jaini S, Honda B, Yu
WH, Hickey MJ, Zucker J, Garay C, Weiner B,
Sisk P, Stolte C, Winkler JK, Van de Peer Y,
Iazzetti P, Camacho D, Dreyfuss J, Liu Y,
Dorhoi A, Mollenkopf HJ, Drogaris P,
Lamontagne J, Zhou Y, Piquenot J, Park ST,
Raman S, Kaufmann SH, Mohney RP,
Chelsky D, Moody DB, Sherman DR, Schoolnik GK (2013) The Mycobacterium tuberculosis regulatory network and hypoxia. Nature
499(7457):178–183.
https://doi.org/10.
1038/nature12337
32. Layre E, Lee HJ, Young DC, Martinot AJ,
Buter J, Minnaard AJ, Annand JW, Fortune
SM, Snider BB, Matsunaga I, Rubin EJ,
Alber T, Moody DB (2014) Molecular
profiling of Mycobacterium tuberculosis identifies tuberculosinyl nucleoside products of the
virulence-associated enzyme Rv3378c. Proc
Natl Acad Sci U S A 111(8):2978–2983.
https://doi.org/10.1073/pnas.1315883111
33. Sabareesh V, Singh G (2013) Mass spectrometry based lipid(ome) analyzer and molecular
platform: a new software to interpret and analyze electrospray and/or matrix-assisted laser
desorption/ionization mass spectrometric
data of lipids: a case study from Mycobacterium
tuberculosis. J Mass Spectrometry 48
(4):465–477. https://doi.org/10.1002/jms.
3163
34. Smith CA, Want EJ, O’Maille G, Abagyan R,
Siuzdak G (2006) XCMS: processing mass
spectrometry data for metabolite profiling
using nonlinear peak alignment, matching,
and
identification.
Anal
Chem
78
(3):779–787.
https://doi.org/10.1021/
ac051437y
35. Pluskal T, Castillo S, Villar-Briones A, Oresic
M (2010) MZmine 2: modular framework for
processing, visualizing, and analyzing mass
spectrometry-based molecular profile data.
BMC Bioinformatics 11:395. https://doi.
org/10.1186/1471-2105-11-395
36. 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. https://doi.org/10.1038/
nmeth.3393
37. Adusumilli R, Mallick P (2017) Data conversion with ProteoWizard msConvert. Methods
Mol Biol 1550:339–368. https://doi.org/10.
1007/978-1-4939-6747-6_23
38. Kessner D, Chambers M, Burke R, Agus D,
Mallick P (2008) ProteoWizard: open source
software for rapid proteomics tools development. Bioinformatics 24(21):2534–2536.
https://doi.org/10.1093/bioinformatics/
btn323
Targeted Lipidomics
577