Metabolite Identification Using Artificial Neural Network Metabolite identification is a major bottleneck in mass spectrometrybased metabolomic studies. In a typical untargeted metabolomic study that uses tandem MS (MSMS), the identities of metabolites are determined by matching the experimental MSMS data against those in spectral libraries. However, existing spectral libraries cover only a small fraction of known compounds. We introduce a neural network model to predict the identity of analytes whose reference measurements are not available in spectral libraries. Specifically, an artificial neural network model is trained to predict the fingerprints of compounds based on their experimental MSMS spectra. Candidates are retrieved from metabolite databases based on molecular formula or precursor mass. The candidate with the most similar fingerprint is chosen as the predicted one. The method was evaluated via an independent dataset. The results show that our neural network model improves MSMSbased metabolite identification accuracy by a considerable margin compared to previous computational methods, such as Metfrag and CSI:FingerID.

Efficient Shared Peak Counting in Database Peptide Search Using Compact Data Structure for FragmentIon Index Database search is the most commonly employed method for identification of peptides from MSMS spectra data. The search involves comparing experimentally obtained MSMS spectra against a set of theoretical spectra predicted from a protein sequence database. One of the most commonly employed similarity metrics for spectral comparison is the sharedpeak count between a pair of MSMS spectra. Most modern methods index all generated fragmention data from theoretical spectra to speed up the shared peak count computations between a given experimental spectrum and all theoretical spectra. However, the bottleneck for this method is the gigantic memory footprint of fragmention index that leads to nonscalable solutions. In this paper, we present a novel data structure, called Compact FragmentIon Index Representation (CFIR), that efficiently compresses highly redundant ionmass information in the data to reduce the index size. Our proposed data structure outperforms all existing fragmention indexing data structures by at least 2xc3x97 in memory consumption while exhibiting the same time complexity for index construction and peptide search. The results also show comparable indexing speed, search speed and speedup scalability for CFIRindex and the stateoftheart algorithms.