Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
On the sorting-complexity of suffix tree construction
Journal of the ACM (JACM)
Database indexing for large DNA and protein sequence collections
The VLDB Journal — The International Journal on Very Large Data Bases
A Database Index to Large Biological Sequences
Proceedings of the 27th International Conference on Very Large Data Bases
An Efficient Index-based Protein Structure Database Searching Method
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Towards Index-based Similarity Search for Protein Structure Databases
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
PSIST: Indexing Protein Structures Using Suffix Trees
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Genome-scale disk-based suffix tree indexing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
OASIS: an online and accurate technique for local-alignment searches on biological sequences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Practical suffix tree construction
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Approaches for indexing proteins and for fast and scalable searching for structures similar to a query structure have important applications such as protein structure and function prediction, protein classification and drug discovery. In this paper, we develop a new method for extracting local structural (or geometric) features from protein structures. These feature vectors are in turn converted into a set of symbols, which are then indexed using a suffix tree. For a given query, the suffix tree index can be used effectively to retrieve the maximal matches, which are then chained to obtain the local alignments. Finally, similar proteins are retrieved by their alignment score against the query. Our results show classification accuracy up to 50% and 92.9% at the topology and class level according to the CATH classification. These results outperform the best previous methods. We also show that PSIST is highly scalable due to the external suffix tree indexing approach it uses; it is able to index about 70,500 domains from SCOP in under an hour.