M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Unified framework for fast exact and approximate search in dissimilarity spaces
ACM Transactions on Database Systems (TODS)
NM-Tree: Flexible Approximate Similarity Search in Metric and Non-metric Spaces
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Proceedings of the Third International Conference on SImilarity Search and APplications
Non-metric similarity search of tandem mass spectra including posttranslational modifications
Journal of Discrete Algorithms
Survey of clustering algorithms
IEEE Transactions on Neural Networks
SimTandem: similarity search in tandem mass spectra
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
Hi-index | 0.00 |
Tandem mass spectrometry is a well-known technique for identification of protein sequences from an "in vitro" sample. To identify the sequences from spectra captured by a spectrometer, the similarity search in a database of hypothetical mass spectra is often used. For this purpose, a database of known protein sequences is utilized to generate the hypothetical spectra. Since the number of sequences in the databases grows rapidly over the time, several approaches have been proposed to index the databases of mass spectra. In this paper, we improve an approach based on the non-metric similarity search where the M-tree and the TriGen algorithm are employed for fast and approximative search. We show that preprocessing of mass spectra by clustering speeds up the identification of sequences more than 100× with respect to the sequential scan of the entire database. Moreover, when the protein candidates are refined by sequential scan in the postprocessing step, the whole approach exhibits precision similar to that of sequential scan over the entire database (over 90%).