Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

Abstract:

:Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics.

journal_name

Curr Opin Chem Biol

authors

Zhou Z,Tu J,Zhu ZJ

doi

10.1016/j.cbpa.2017.10.033

subject

Has Abstract

pub_date

2018-02-01 00:00:00

pages

34-41

eissn

1367-5931

issn

1879-0402

pii

S1367-5931(17)30123-0

journal_volume

42

pub_type

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