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)
Reducing the space requirement of suffix trees
Software—Practice & Experience
Suffix vector: space- and time-efficient alternative to suffix trees
ACSC '02 Proceedings of the twenty-fifth Australasian conference on Computer science - Volume 4
Database indexing for large DNA and protein sequence collections
The VLDB Journal — The International Journal on Very Large Data Bases
Constructing large suffix trees on a computational grid
Journal of Parallel and Distributed Computing
Genome-scale disk-based suffix tree indexing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Serial and parallel methods for i/o efficient suffix tree construction
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Indexing genomic sequences on the IBM Blue Gene
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
I/O efficient algorithms for serial and parallel suffix tree construction
ACM Transactions on Database Systems (TODS)
Parallel construction of large suffix trees on a PC cluster
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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Suffix trees have been the focus of significant research interest as they permit very efficient solutions to a range of string and sequence searching problems. Given a suffix tree that encodes a particular string, it is possible to solve problems such as searching for a specific pattern in time proportional to the length of the pattern rather than the length of the string. Suffix trees can also support inexact matching by dramatically improving the performance of dynamic programming. Therefore, suffix trees may enable a number of large scale bioinformatics problems to be solved more efficiently than previously thought. However, these benefits presume that a suffix tree of sufficient scale can be constructed. An inherent difficulty in suffix tree construction is that the tree construction requires a semi random walk over the tree as it is constructed. Therefore very large trees that will not fit in memory could take an unacceptably long time to construct due to excessive page faulting. In this paper we present a linear time construction algorithm that can construct suffix trees larger than memory using discrete sub-trees. The sub-trees can be constructed in paralel. The perforance of the algorithm is evaluated using suffix trees constructed for chromosomes 1 and 12 of the human genome.