Name
Abstract | ||
|---|---|---|
Suffix trees are one of the most versatile data structures in stringology, with many applications in bioinformatics. Their main drawback is their size, which can be tens of times larger than the input sequence. Much effort has been put into reducing the space usage, leading ultimately to compressed suffix trees. These compressed data structures can efficiently simulate the suffix tree, while using space proportional to a compressed representation of the sequence. In this work, we take a new approach to compressed suffix trees for repetitive sequence collections, such as collections of individual genomes. We compress the suffix trees of individual sequences relative to the suffix tree of a reference sequence. These relative data structures provide competitive time/space trade-offs, being almost as small as the smallest compressed suffix trees for repetitive collections, and almost as fast as the largest and fastest compressed suffix trees. They also provide access to the suffix trees of individual sequences, instead of storing all sequences in the same tree. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Data structure,Combinatorics,Suffix,Algorithm,Theoretical computer science,Generalized suffix tree,Suffix tree,FM-index,Compressed suffix array,Mathematics,Longest common substring problem,Fold (higher-order function) |
DocType | Volume | Citations |
Journal | abs/1508.02550 | 2 |
PageRank | References | Authors |
0.37 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Travis Gagie | 1 | 643 | 63.61 |
| Gonzalo Navarro | 2 | 109 | 11.07 |
| Simon J. Puglisi | 3 | 1132 | 75.14 |
| Jouni Sirén | 4 | 222 | 14.85 |