study. They provide integrative metabolite information retrieving
from public databases, such as KEGG, SMPDB, HMDB, Pub-
Chem, and ChEBI. In short, these databases and tools can help
understand the biological roles of metabolites play in living systems.7 Conclusions
Metabolomics is an important “omic” technology that can discover
clinic biomarker and provide deep mechanistic insights into
biological process for a living system. Here we review the experi-
mental design, sample collection and preparation methods, analyti-
cal platform of data acquisition (such as GC-MS, LC-MS, CE-MS,
NMR), the data preprocessing and analyzing methods, as well as
mechanism interpretation and hypothesis generation approaches
for metabolomics study. The challenges in metabolomics are theTable 1
Software of mechanism interpretation for metabolites
Name URL Reference
MSEA http://www.msea.ca [122]
MBRole http://csbg.cnb.csic.es/mbrole [123]
MPEA http://ekhidna.biocenter.helsinki.fi/poxo/mpea/ [124]
IMPaLA http://impala.molgen.mpg.de [125]
KEGG http://www.genome.jp/kegg/pathway.html [126]
BioCyc—Omics
Viewerhttp://biocyc.org [127]iPath http://pathways.embl.de [128]
KaPPA-View http://kpv.kazusa.or.jp/en/ [129]
MapMan http://mapman.gabipd.org/web/guest/mapman [130]
MetPA http://metpa.metabolomics.ca [131]
Metscape http://metscape.ncibi.org [132]
MGV http://www.microarray-analysis.org/mayday [133]
Paintomics http://www.paintomics.org [134]
Pathos http://motif.gla.ac.uk/Pathos/ [135]
PathVisio http://www.pathvisio.org/[136]
ProMeTra http://www.cebitec.uni-bielefeld.de/groups/brf/software/
prometra_info/Reactome[137]Reactome http://www.reactome.org [138]
VANTED http://vanted.ipk-gatersleben.de [139]284 Jing Cheng et al.