Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
An experimental study of an opportunistic index
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Probabilistic Analysis of Generalized Suffix Trees (Extended Abstract)
CPM '92 Proceedings of the Third Annual Symposium on Combinatorial Pattern Matching
Opportunistic data structures with applications
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Succinct suffix arrays based on run-length encoding
Nordic Journal of Computing
Compressed indexing and local alignment of DNA
Bioinformatics
Encyclopedia of Algorithms
Orthogonal range searching in linear and almost-linear space
Computational Geometry: Theory and Applications
Storage and Retrieval of Individual Genomes
RECOMB 2'09 Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology
A faster grammar-based self-index
LATA'12 Proceedings of the 6th international conference on Language and Automata Theory and Applications
Fast relative lempel-ziv self-index for similar sequences
FAW-AAIM'12 Proceedings of the 6th international Frontiers in Algorithmics, and Proceedings of the 8th international conference on Algorithmic Aspects in Information and Management
Improved grammar-based compressed indexes
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
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To study the genetic variations of a species, one basic operation is to search for occurrences of patterns in a large number of very similar genomic sequences. To build an indexing data structure on the concatenation of all sequences may require a lot of memory. In this paper, we propose a new scheme to index highly similar sequences by taking advantage of the similarity among the sequences. To store r sequences with k common segments, our index requires only O(n +N log N) bits of memory, where n is the total length of the common segments and N is the total length of the distinct regions in all texts. The total length of all sequences is rn + N, and any scheme to store these sequences requires Ω(n + N) bits. Searching for a pattern P of length m takes O(m + mlogN + mlog(rk)psc(P) + occlogn), where psc(P) is the number of prefixes of P that appear as a suffix of some common segments and occ is the number of occurrences of P in all sequences. In practice, rk ≤ N, and psc(P) is usually a small constant. We have implemented our solution and evaluated our solution using real DNA sequences. The experiments show that the memory requirement of our solution is much less than that required by BWT built on the concatenation of all sequences. When compared to the other existing solution (RLCSA), we use less memory with faster searching time.