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HAMMER: automated operation of mass frontier to construct in silico mass spectral fragmentation libraries.

Zhou J, Weber RJ, Allwood JW, Mistrik R, Zhu Z, Ji Z, Chen S, Dunn WB, He S, Viant MR.

Bioinformatics. 2014 Feb 15;30(4):581-3. doi: 10.1093/bioinformatics/btt711.


In silico fragmentation for computer assisted identification of metabolite mass spectra.

Wolf S, Schmidt S, Müller-Hannemann M, Neumann S.

BMC Bioinformatics. 2010 Mar 22;11:148. doi: 10.1186/1471-2105-11-148.


Metabolite identification using automated comparison of high-resolution multistage mass spectral trees.

Rojas-Cherto M, Peironcely JE, Kasper PT, van der Hooft JJ, de Vos RC, Vreeken R, Hankemeier T, Reijmers T.

Anal Chem. 2012 Jul 3;84(13):5524-34. doi: 10.1021/ac2034216.


Substructure-based annotation of high-resolution multistage MS(n) spectral trees.

Ridder L, van der Hooft JJ, Verhoeven S, de Vos RC, van Schaik R, Vervoort J.

Rapid Commun Mass Spectrom. 2012 Oct 30;26(20):2461-71. doi: 10.1002/rcm.6364.


Automated pipeline for de novo metabolite identification using mass-spectrometry-based metabolomics.

Peironcely JE, Rojas-Chertó M, Tas A, Vreeken R, Reijmers T, Coulier L, Hankemeier T.

Anal Chem. 2013 Apr 2;85(7):3576-83. doi: 10.1021/ac303218u.


MetiTree: a web application to organize and process high-resolution multi-stage mass spectrometry metabolomics data.

Rojas-Chertó M, van Vliet M, Peironcely JE, van Doorn R, Kooyman M, te Beek T, van Driel MA, Hankemeier T, Reijmers T.

Bioinformatics. 2012 Oct 15;28(20):2707-9. doi: 10.1093/bioinformatics/bts486.


mSpecs: a software tool for the administration and editing of mass spectral libraries in the field of metabolomics.

Thielen B, Heinen S, Schomburg D.

BMC Bioinformatics. 2009 Jul 22;10:229. doi: 10.1186/1471-2105-10-229.


MetaboMiner--semi-automated identification of metabolites from 2D NMR spectra of complex biofluids.

Xia J, Bjorndahl TC, Tang P, Wishart DS.

BMC Bioinformatics. 2008 Nov 28;9:507. doi: 10.1186/1471-2105-9-507.


In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids.

Kangas LJ, Metz TO, Isaac G, Schrom BT, Ginovska-Pangovska B, Wang L, Tan L, Lewis RR, Miller JH.

Bioinformatics. 2012 Jul 1;28(13):1705-13. doi: 10.1093/bioinformatics/bts194.


The volatile compound BinBase mass spectral database.

Skogerson K, Wohlgemuth G, Barupal DK, Fiehn O.

BMC Bioinformatics. 2011 Aug 4;12:321. doi: 10.1186/1471-2105-12-321.


Metab: an R package for high-throughput analysis of metabolomics data generated by GC-MS.

Aggio R, Villas-Bôas SG, Ruggiero K.

Bioinformatics. 2011 Aug 15;27(16):2316-8. doi: 10.1093/bioinformatics/btr379.


xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data.

Uppal K, Soltow QA, Strobel FH, Pittard WS, Gernert KM, Yu T, Jones DP.

BMC Bioinformatics. 2013 Jan 16;14:15. doi: 10.1186/1471-2105-14-15.


Role of liquid chromatography-high-resolution mass spectrometry (LC-HR/MS) in clinical toxicology.

Wu AH, Gerona R, Armenian P, French D, Petrie M, Lynch KL.

Clin Toxicol (Phila). 2012 Sep;50(8):733-42. doi: 10.3109/15563650.2012.713108. Review.


Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets.

Brown M, Wedge DC, Goodacre R, Kell DB, Baker PN, Kenny LC, Mamas MA, Neyses L, Dunn WB.

Bioinformatics. 2011 Apr 15;27(8):1108-12. doi: 10.1093/bioinformatics/btr079. Erratum in: Bioinformatics. 2012 Jan 1;28(1):149.


Computational analyses of spectral trees from electrospray multi-stage mass spectrometry to aid metabolite identification.

Cao M, Fraser K, Rasmussen S.

Metabolites. 2013 Oct 31;3(4):1036-50. doi: 10.3390/metabo3041036.


Implementation of a semi-automated strategy for the annotation of metabolomic fingerprints generated by liquid chromatography-high resolution mass spectrometry from biological samples.

Courant F, Royer AL, Chéreau S, Morvan ML, Monteau F, Antignac JP, Le Bizec B.

Analyst. 2012 Nov 7;137(21):4958-67. doi: 10.1039/c2an35865d.


RAMClust: a novel feature clustering method enables spectral-matching-based annotation for metabolomics data.

Broeckling CD, Afsar FA, Neumann S, Ben-Hur A, Prenni JE.

Anal Chem. 2014 Jul 15;86(14):6812-7. doi: 10.1021/ac501530d.

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