Abstract
Untargeted metabolomics can detect more than 10 000 peaks in a single LC–MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ∼2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220
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- Developers:
-
Xing, Xi ; Wang, Lin ; Chen, Li ; Yang, Lifeng ; Su, Xiaoyang ; Rabitz, Herschel ; Lu, Wenyun ; Rabinowitz, Joshua [1]
- Princeton Univ., NJ (United States)
- Release Date:
- 2018-08-03
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
MIT License
- Sponsoring Org.:
-
USDOE Office of Science (SC), Biological and Environmental Research (BER)Primary Award/Contract Number:SC0018420
- Code ID:
- 146422
- Research Org.:
- Center for Advanced Bioenergy and Bioproduct Innovation
- Country of Origin:
- United States
Citation Formats
Xing, Xi, Wang, Lin, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, and Rabinowitz, Joshua.
xxing9703/PAVE.
Computer Software.
https://github.com/xxing9703/PAVE.
USDOE Office of Science (SC), Biological and Environmental Research (BER).
03 Aug. 2018.
Web.
doi:10.11578/dc.20241031.1.
Xing, Xi, Wang, Lin, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, & Rabinowitz, Joshua.
(2018, August 03).
xxing9703/PAVE.
[Computer software].
https://github.com/xxing9703/PAVE.
https://doi.org/10.11578/dc.20241031.1.
Xing, Xi, Wang, Lin, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, and Rabinowitz, Joshua.
"xxing9703/PAVE." Computer software.
August 03, 2018.
https://github.com/xxing9703/PAVE.
https://doi.org/10.11578/dc.20241031.1.
@misc{
doecode_146422,
title = {xxing9703/PAVE},
author = {Xing, Xi and Wang, Lin and Chen, Li and Yang, Lifeng and Su, Xiaoyang and Rabitz, Herschel and Lu, Wenyun and Rabinowitz, Joshua},
abstractNote = {Untargeted metabolomics can detect more than 10 000 peaks in a single LC–MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ∼2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Thus, PAVE enables systematic annotation of LC–MS metabolomics data with only ∼4% of peaks annotated as apparent metabolites.},
doi = {10.11578/dc.20241031.1},
url = {https://doi.org/10.11578/dc.20241031.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20241031.1}},
year = {2018},
month = {aug}
}