Journal
METABOLITES
Volume 9, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/metabo9110251
Keywords
LC-MS; metabolomics; mass spectral deconvolution; chemical library; all ion fragmentation
Categories
Funding
- JSPS KAKENHI [JP18J23133, JP17H03621, JP15H05898, JP15K21738, JP18H02432, JP18K19155, JP19K17662]
- Gunma University Initiative for Advanced Research (GIAR)
- STINT Foundation
- Swedish Heart Lung Foundation [HLF 20170734, HLF 20180290]
- Swedish Research Council [2016-02798]
- AMED [JP17gm1010006]
- Environment Research and Technology Development Fund (ERTDF) [5-1752]
- Japan Society for the Promotion of Science (JSPS) postdoctoral fellowship [P17774]
- NBDC
Ask authors/readers for more resources
Accurate metabolite identification remains one of the primary challenges in a metabolomics study. A reliable chemical spectral library increases the confidence in annotation, and the availability of raw and annotated data in public databases facilitates the transfer of Liquid chromatography coupled to mass spectrometry (LC-MS) methods across laboratories. Here, we illustrate how the combination of MS2 spectra, accurate mass, and retention time can improve the confidence of annotation and provide techniques to create a reliable library for all ion fragmentation (AIF) data with a focus on the characterization of the retention time. The resulting spectral library incorporates information on adducts and in-source fragmentation in AIF data, while noise peaks are effectively minimized through multiple deconvolution processes. We also report the development of the Mass Spectral LIbrary MAnager (MS-LIMA) tool to accelerate library sharing and transfer across laboratories. This library construction strategy improves the confidence in annotation for AIF data in LC-MS-based metabolomics and will facilitate the sharing of retention time and mass spectral data in the metabolomics community.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available