  Metabolite structure identification has become the major bottleneck of the
mass spectrometry based metabolomics research. Till now, number of mass spectra
databases and search algorithms have been developed to address this issue.
However, two critical problems still exist: the low chemical component record
coverage in databases and significant MS/MS spectra variations related to
experiment equipment and parameter settings. In this work, we considered the
molecule fragment as basic building blocks of the metabolic components which
had relatively consistent signatures in MS/MS spectra. And from a bottom-up
point of view, we built a fragment centered database, MSFragDB, by reorganizing
the data from the Human Metabolome Database (HMDB) and developed an
intensity-free searching algorithm to search and rank the most relative
metabolite according to the users' input. We also proposed the concept of
fragment network, a graph structure that encoded the relationship between the
molecule fragments to find close motif that indicated a specific chemical
structure. Although based on the same dataset as the HMDB, validation results
implied that the MSFragDB had a higher hit ratio and furthermore, estimated
possible taxonomy that a query spectrum belongs to when the corresponding
chemical component was missing in the database. Aid by the Fragment Network,
the MSFragDB was also proved to be able to estimate the right structure while
the MS/MS spectrum suffers from the precursor-contamination. The strategy
proposed is general and can be adopted in existing databases. We believe
MSFragDB and Fragment Network can improve the performance of structure
identification with existing data. The beta version of the database is freely
available at www.xrzhanglab.com/msfragdb/.
