Truncated Suffix Tree Research Articles

A compressed dynamic self-index for highly repetitive text collections

We present a novel compressed dynamic self-index for highly repetitive text collections. ESP-index is a static self-index of this type and has a large disadvantage of slow pattern search for short patterns. We obtain faster pattern search by leveraging the idea behind a truncated suffix tree (TST) to develop the first compressed dynamic self-index, called TST-index, that supports not only fast pattern search but also dynamic update operations for highly repetitive texts. Experiments with a benchmark dataset show that the pattern search performance of the TST-index is significantly better than that of ESP-index for short patterns.

Efficient Algorithms for Sequence Analysis with Entropic Profiles.

Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA). We performed an extensive experimental campaign showing that our algorithms, beside being faster, make it possible the analysis of longer sequences, even for high degrees of resolution, than state of the art algorithms.

Linking indexing data structures to de Bruijn graphs: Construction and update

DNA sequencing technologies have tremendously increased their throughput, and hence complicated DNA assembly. Numerous assembly programs use de Bruijn graphs (dBG) built from short reads to merge these into contigs, which represent putative DNA segments. In a dBG of order k, nodes are substrings of length k of reads (or k-mers), while arcs are their k+1-mers. As analysing reads often require to index all their substrings, it is interesting to exhibit algorithms that directly build a dBG from a pre-existing index, and especially a contracted dBG, where non-branching paths are condensed into single nodes. Here, we exhibit linear time algorithms for constructing the full or contracted dBGs from suffix trees, suffix arrays, and truncated suffix trees. With the latter the construction uses a space that is linear in the size of the dBG. Finally, we also provide algorithms to dynamically update the order of the graph without reconstructing it.

Space-efficient representation of truncated suffix trees, with applications to Markov order estimation

Suffix trees (ST) are useful in many text processing applications, for example, to determine the number of occurrences of patterns of arbitrary length in an input string x. If the length n, of x, is large, the memory required to represent the ST may become a practical performance bottleneck. This problem can be alleviated, in cases where a nontrivial upper bound is known on the lengths of the patterns of interest, by using a truncated ST (TST). However, conventional TST implementations still require Ω(n) bits of memory, since they store x. We describe a new TST representation that avoids this limitation by storing all the information necessary to reconstruct the TST edge labels in a string y that is often much shorter than x. We apply TSTs to the implementation of Markov order estimators, where an upper bound kn on the estimated order can be derived or it is imposed (for consistency, for example). The new representation allows for estimator implementations with sublinear space complexity in some cases of interest. In other cases we show, experimentally, that even when the new representation does not have an asymptotic advantage, it still achieves very significant memory savings in practice.

An efficient algorithm for systematic analysis of nucleotide strings suitable for siRNA design

BackgroundThe "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques.FindingsThe proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.phpConclusionsIn attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.

Truncated suffix trees and their application to data compression

The suffix tree is a fundamental data structure in the area of string algorithms and it has been used in many applications including data compression. In this paper we propose a data structure called the truncated suffix tree, which is a truncated version of the suffix tree. We also present two linear-time construction algorithms for truncated suffix trees and two algorithms that delete suffixes from truncated suffix trees.The truncated suffix tree is particularly a useful data structure for LZ77 that compresses using a sliding window of a fixed size. Our algorithms lead to two implementations of LZ77 that maintain sliding windows by truncated suffix trees. We also present a technique of finding the longest match in a sliding window, which is a crucial step in LZ77.