On Optimizing Distance-Based Similarity Search for Biological Databases
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Indexing schemes for similarity search in datasets of short protein fragments
Information Systems
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Hierarchical metric-pace clustering methods have been commonly used to organize proteomes into taxonomies. Consequently, it is often anticipated that hierarchical clustering can be leveraged as a basis for scalable database index structures capable of managing the hyper-exponential growth of sequence data. M-tree is one such data structure specialized for the management of large data set on disk.We explore the application of M-trees to the storage and retrieval of peptide sequence data. Exploiting a technique first suggested by Myers, we organize the database as records of fixed length substrings. Empirical results are promising.However,metric-space indexes are subject to "the curse of dimensionality" and the ultimate performance of an index is sensitive to the quality of the initial construction of the index. We introduce new hierarchical bulk-load algorithm that alternates between top-down and bottom-up clustering to initialize the index.Using the Yeast Proteomes, the bi-directional bulk load produces a more effective index than the existing M-tree initialization algorithms.