Suffix tree

About: Suffix tree is a research topic. Over the lifetime, 1155 publications have been published within this topic receiving 43454 citations. The topic is also known as: PAT tree & position tree.

Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology

Abstract: Part I. Exact String Matching: The Fundamental String Problem: 1. Exact matching: fundamental preprocessing and first algorithms 2. Exact matching: classical comparison-based methods 3. Exact matching: a deeper look at classical methods 4. Semi-numerical string matching Part II. Suffix Trees and their Uses: 5. Introduction to suffix trees 6. Linear time construction of suffix trees 7. First applications of suffix trees 8. Constant time lowest common ancestor retrieval 9. More applications of suffix trees Part III. Inexact Matching, Sequence Alignment and Dynamic Programming: 10. The importance of (sub)sequence comparison in molecular biology 11. Core string edits, alignments and dynamic programming 12. Refining core string edits and alignments 13. Extending the core problems 14. Multiple string comparison: the Holy Grail 15. Sequence database and their uses: the motherlode Part IV. Currents, Cousins and Cameos: 16. Maps, mapping, sequencing and superstrings 17. Strings and evolutionary trees 18. Three short topics 19. Models of genome-level mutations.

Algorithms on strings, trees, and sequences

Abstract: Linear-Time Construction of Suffix Trees We will present two methods for constructing suffix trees in detail, Ukkonen’s method and Weiner’s method. Weiner was the first to show that suffix trees can be built in linear time, and his method is presented both for its historical importance and for some different technical ideas that it contains. However, lJkkonen’s method is equally fast and uses far less space (i.e., memory) in practice than Weiner’s method Hence Ukkonen is the method of choice for most problems requiring the construction of a suffix tree. We also believe that Ukkonen’s method is easier to understand. Therefore, it will be presented first A reader who wishes to study only one method is advised to concentrate on it. However, our development of Weiner’s method does not depend on understanding Ukkonen’s algorithm, and the two algorithms can be read independently (with one small shared section noted in the description of Weiner’s method).

Suffix arrays: a new method for on-line string searches

Abstract: A new and conceptually simple data structure, called a suffix array, for on-line string searches is introduced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees is that, in practice, they use three to five times less space. From a complexity standpoint, suffix arrays permit on-line string searches of the type, “Is W a substring of A?” to be answered in time $O(P + \log N)$, where P is the length of W and N is the length of A, which is competitive with (and in some cases slightly better than) suffix trees. The only drawback is that in those instances where the underlying alphabet is finite and small, suffix trees can be constructed in $O(N)$ time in the worst case, versus $O(N\log N)$ time for suffix arrays. However, an augmented algorithm is given that, regardless of the alphabet size, constructs suffix arrays in $O(N)$expected time, albeit with lesser space efficiency. It is ...

A Space-Economical Suffix Tree Construction Algorithm

Abstract: A new algorithm is presented for constructing auxiliary digital search trees to aid in exact-match substring searching. This algorithm has the same asymptotic running time bound as previously published algorithms, but is more economical in space. Some implementation considerations are discussed, and new work on the modification of these search trees in response to incremental changes in the strings they index (the update problem) is presented.