Assessment of metabolome annotation quality: a method for evaluating the false discovery rate of elemental composition searches.
BACKGROUND
In metabolomics researches using mass spectrometry (MS), systematic searching of high-resolution mass data against compound databases is often the first step of metabolite annotation to determine elemental compositions possessing similar theoretical mass numbers. However, incorrect hits derived from errors in mass analyses will be included in the results of elemental composition searches. To assess the quality of peak annotation information, a novel methodology for false discovery rates (FDR) evaluation is presented in this study. Based on the FDR analyses, several aspects of an elemental composition search, including setting a threshold, estimating FDR, and the types of elemental composition databases most reliable for searching are discussed.


RESULTS
The FDR can be determined from one measured value (i.e., the hit rate for search queries) and four parameters determined by Monte Carlo simulation. The results indicate that relatively high FDR values (30-50%) were obtained when searching time-of-flight (TOF)/MS data using the KNApSAcK and KEGG databases. In addition, searches against large all-in-one databases (e.g., PubChem) always produced unacceptable results (FDR >70%). The estimated FDRs suggest that the quality of search results can be improved not only by performing more accurate mass analysis but also by modifying the properties of the compound database. A theoretical analysis indicates that FDR could be improved by using compound database with smaller but higher completeness entries.


CONCLUSIONS
High accuracy mass analysis, such as Fourier transform (FT)-MS, is needed for reliable annotation (FDR <10%). In addition, a small, customized compound database is preferable for high-quality annotation of metabolome data.
Metabolome Research Group, RIKEN Plant Science Center, Yokohama, Kanagawa, Japan.
Fumio
F
Matsuda
Yoko
Y
Shinbo
Akira
A
Oikawa
Masami Yokota
MY
Hirai
Oliver
O
Fiehn
Shigehiko
S
Kanaya
Kazuki
K
Saito
D017360:Arabidopsis / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005189:False Positive Reactions; D005583:Fourier Analysis; D055442:Metabolome; D055432:Metabolomics / Q000379:methods*; D012275:Oryza / Q000378:metabolism; D010940:Plant Proteins / Q000378:metabolism; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software; D053719:Tandem Mass Spectrometry / Q000379:methods
D010940:Plant Proteins