Annotation of peaks and rapid identification of metabolites is currently still a major bottle-neck in mass spectrometry (MS) based metabolomics. Despite numerous new algorithms and software packages published in recent years, including attempts at de-novo identification and modeling, the workhorse of the trade remains mass-to-mass and retention time (RT) matching of observed and reference library peaks, including, when available, MS/MS data which is used for confirmation. In this work, we present a rational, statistically based expansion of the used approach using orthogonal computational modules and accumulation of independent evidence to achieve automatic high confidence identifications of metabolites in LC-MS data.
|Citation:||Shahaf, N.; Rogachev, I.; Meir, S.; Wehrens R.; Aharoni A. (2013). Integrated approach for metabolite identification in LC-MS. In: 26th Meeting of the Israeli Society for Mass Spectrometry, October 10th 2013, Rehovot. url: http://www.weizmann.ac.il/conferences/ISMS2013/ handle: http://hdl.handle.net/10449/22811|
|Organization unit:||Computational Biology # CRI_2011-JAN2016|
|Authors:||Shahaf, N.; Rogachev, I.; Meir, S.; Wehrens R.; Aharoni A.|
|Title:||Integrated approach for metabolite identification in LC-MS|
|Scientific Disciplinary Area:||Settore CHIM/01 - Chimica Analitica|
|Keywords ENG:||Data analysis|
|Appears in Collections:||03 - Conference object|