Abstract
Metabolomics relies on a variety of bioinformatics tools aiming to derive information from data. Data acquisition with chromatography-coupled mass spectrometry yields high volumes of raw data that need to be processed to achieve de-noised andmeaningful biochemical data that subsequently can be presented for statistics and pathway analysis in a coherent manner. A variety of new tools and algorithms have recently been developed that help in this process. Nevertheless, a surprisingly low number of metabolic signals can be unambiguously identified. If cutting-edgemethods areused, we can confidently interpret signals that can neither be identified by reference standards nor annotated by databasematching as 'novel metabolites'. Such methods may comprise high-resolution, high-accurate mass data in conjunction with good data alignment programs that perform peak picking and deconvolution, calculation of elemental formulas, database queries and constraining hit lists by interpreting mass spectral fragmentations and retention-based metabolomic libraries. In an effort to improve standardizations formetabolomic reports, we propose that not only metabolites must be named but also, for all reported metabolites, identifiers or structure codes (InChI keys) from public (bio)chemical databases need to be detailed. Machine-readable structures will vastly accelerate our knowledge of the magnitude and importance of the metabolome in various plant species, organs and physiological conditions. We detail how well-annotated metabolome data can then be used to visualize and interrogate biochemical pathways by matching information to the broad plant databases that currently exist.
| Original language | English (US) |
|---|---|
| Title of host publication | Annual Plant Reviews Volume 43: Biology of Plant Metabolomics |
| Publisher | wiley |
| Pages | 367-406 |
| Number of pages | 40 |
| Volume | 43 |
| ISBN (Print) | 9781444339956, 9781405199544 |
| DOIs | |
| State | Published - Mar 17 2011 |
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Keywords
- Enzyme pathways
- Genomic reconstruction
- Ligand databases
- Lipidomics
- Natural products
- Structure dereplication
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
Cite this
Data Processing, Metabolomic Databases and Pathway Analysis. / Fiehn, Oliver; Kind, Tobias; Barupal, Dinesh Kumar.
Annual Plant Reviews Volume 43: Biology of Plant Metabolomics. Vol. 43 wiley, 2011. p. 367-406.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Data Processing, Metabolomic Databases and Pathway Analysis
AU - Fiehn, Oliver
AU - Kind, Tobias
AU - Barupal, Dinesh Kumar
PY - 2011/3/17
Y1 - 2011/3/17
N2 - Metabolomics relies on a variety of bioinformatics tools aiming to derive information from data. Data acquisition with chromatography-coupled mass spectrometry yields high volumes of raw data that need to be processed to achieve de-noised andmeaningful biochemical data that subsequently can be presented for statistics and pathway analysis in a coherent manner. A variety of new tools and algorithms have recently been developed that help in this process. Nevertheless, a surprisingly low number of metabolic signals can be unambiguously identified. If cutting-edgemethods areused, we can confidently interpret signals that can neither be identified by reference standards nor annotated by databasematching as 'novel metabolites'. Such methods may comprise high-resolution, high-accurate mass data in conjunction with good data alignment programs that perform peak picking and deconvolution, calculation of elemental formulas, database queries and constraining hit lists by interpreting mass spectral fragmentations and retention-based metabolomic libraries. In an effort to improve standardizations formetabolomic reports, we propose that not only metabolites must be named but also, for all reported metabolites, identifiers or structure codes (InChI keys) from public (bio)chemical databases need to be detailed. Machine-readable structures will vastly accelerate our knowledge of the magnitude and importance of the metabolome in various plant species, organs and physiological conditions. We detail how well-annotated metabolome data can then be used to visualize and interrogate biochemical pathways by matching information to the broad plant databases that currently exist.
AB - Metabolomics relies on a variety of bioinformatics tools aiming to derive information from data. Data acquisition with chromatography-coupled mass spectrometry yields high volumes of raw data that need to be processed to achieve de-noised andmeaningful biochemical data that subsequently can be presented for statistics and pathway analysis in a coherent manner. A variety of new tools and algorithms have recently been developed that help in this process. Nevertheless, a surprisingly low number of metabolic signals can be unambiguously identified. If cutting-edgemethods areused, we can confidently interpret signals that can neither be identified by reference standards nor annotated by databasematching as 'novel metabolites'. Such methods may comprise high-resolution, high-accurate mass data in conjunction with good data alignment programs that perform peak picking and deconvolution, calculation of elemental formulas, database queries and constraining hit lists by interpreting mass spectral fragmentations and retention-based metabolomic libraries. In an effort to improve standardizations formetabolomic reports, we propose that not only metabolites must be named but also, for all reported metabolites, identifiers or structure codes (InChI keys) from public (bio)chemical databases need to be detailed. Machine-readable structures will vastly accelerate our knowledge of the magnitude and importance of the metabolome in various plant species, organs and physiological conditions. We detail how well-annotated metabolome data can then be used to visualize and interrogate biochemical pathways by matching information to the broad plant databases that currently exist.
KW - Enzyme pathways
KW - Genomic reconstruction
KW - Ligand databases
KW - Lipidomics
KW - Natural products
KW - Structure dereplication
UR - http://www.scopus.com/inward/record.url?scp=84948952994&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84948952994&partnerID=8YFLogxK
U2 - 10.1002/9781444339956.ch12
DO - 10.1002/9781444339956.ch12
M3 - Chapter
AN - SCOPUS:84948952994
SN - 9781444339956
SN - 9781405199544
VL - 43
SP - 367
EP - 406
BT - Annual Plant Reviews Volume 43: Biology of Plant Metabolomics
PB - wiley
ER -