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Metabolite structure identification has become the major bottleneck of themass spectrometry based metabolomics research. Till now, number of mass spectradatabases and search algorithms have been developed to address this issue.However, two critical problems still exist: the low chemical component recordcoverage in databases and significant MS/MS spectra variations related toexperiment equipment and parameter settings. In this work, we considered themolecule fragment as basic building blocks of the metabolic components whichhad relatively consistent signatures in MS/MS spectra. And from a bottom-uppoint of view, we built a fragment centered database, MSFragDB, by reorganizingthe data from the Human Metabolome Database (HMDB) and developed anintensity-free searching algorithm to search and rank the most relativemetabolite according to the users' input. We also proposed the concept offragment network, a graph structure that encoded the relationship between themolecule fragments to find close motif that indicated a specific chemicalstructure. Although based on the same dataset as the HMDB, validation resultsimplied that the MSFragDB had a higher hit ratio and furthermore, estimatedpossible taxonomy that a query spectrum belongs to when the correspondingchemical component was missing in the database. Aid by the Fragment Network,the MSFragDB was also proved to be able to estimate the right structure whilethe MS/MS spectrum suffers from the precursor-contamination. The strategyproposed is general and can be adopted in existing databases. We believeMSFragDB and Fragment Network can improve the performance of structureidentification with existing data. The beta version of the database is freelyavailable at www.xrzhanglab.com/msfragdb/.

Estimate Metabolite Taxonomy and Structure with a Fragment-Centered Database and Fragment Network