FORRepeats: detects repeats on entire chromosomes and between genomes

Lefèbvre, Arnaud; Lecroq, Thierry; Dauchel, Hélène; Alexandre, Joël

doi:10.1093/bioinformatics/btf843

Cited by 57 publications

(35 citation statements)

References 20 publications

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“…In recent decades, growing quantities of DNA and protein sequence data have been and are being generated, increasing the need for compact and efficient representations to store and search such data [1,2]. Similar developments can be found for natural language processing as well.…”

Section: Introductionmentioning

confidence: 81%

“…Firstly, factors that occur multiple times within a given DNA sequence may indicate interesting genetic phenomena such as tandem repeats and micro-satellites [3]. To detect such factors, an index representing all the factors of a given sequence can be constructed in such a way that it provides information about repeated factors, including where they are located in the given sequence [1,4].…”

Section: Introductionmentioning

confidence: 99%

“…1 Among the most efficient approaches for such pattern matching are those that require to know that a subsequence read in the subject text is not a factor of the pattern [5,6,7,8]. For a given position in the subject, such efficient pattern matching algorithms read backward from that position, trying to match (the reverse of) the pattern against what is read; if it can be determined that what has been read backward so far is not a (reverse) factor of the pattern, certainly no match of the pattern will be found by reading further backward, and a forward jump or shift to another position in the subject can be made, using the knowledge of what was read to allow shifts that skip over some positions of the subject string.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weak factor automata: the failure of failure factor oracles?

Cleophas¹,

Kourie²,

Watson³

2014

SACJ

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In indexing of, and pattern matching on, DNA and text sequences, it is often important to represent all factors of a sequence. One efficient, compact representation is the factor oracle (FO). At the same time, any classical deterministic finite automata (DFA) can be transformed to a so-called failure one (FDFA), which may use failure transitions to replace multiple symbol transitions, potentially yielding a more compact representation. We combine the two ideas and directly construct a failure factor oracle (FFO) from a given sequence, in contrast to ex post facto transformation to an FDFA. The algorithm is suitable for both short and long sequences. We empirically compared the resulting FFOs and FOs on number of transitions for many DNA sequences of lengths 4 − 512, showing gains of up to 10% in total number of transitions, with failure transitions also taking up less space than symbol transitions. The resulting FFOs can be used for indexing, as well as in a variant of the FO-using backward oracle matching algorithm. We discuss and classify this pattern matching algorithm in terms of the keyword pattern matching taxonomies of Watson, Cleophas and Zwaan. We also empirically compared the use of FOs and FFOs in such backward reading pattern matching algorithms, using both DNA and natural language (English) data sets. The results indicate that the decrease in pattern matching performance of an algorithm using an FFO instead of an FO may outweigh the gain in representation space by using an FFO instead of an FO.

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Section: Introductionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weak factor automata: the failure of failure factor oracles?

Cleophas¹,

Kourie²,

Watson³

2014

SACJ

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“…Several algorithmic methods have been proposed for efficiently detecting tandem or dispersed repeats in DNA sequences; see [3], [4], [5], [6], and [7]. But, a relevant statistical analysis of these repeats (count, length, location) should then be done in order to distinguish the significant repeats from those that can be just obtained 'by chance'.…”

mentioning

confidence: 99%

Poisson Approximation for the Number of Repeats in a Stationary Markov Chain

et al. 2008

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Detection of repeated sequences within complete genomes is a powerful tool to help understanding genome dynamics and species evolutionary history. To distinguish significant repeats from those that can be obtained just by chance, statistical methods have to be developed. In this paper we show that the distribution of the number of long repeats in long sequences generated by stationary Markov chains can be approximated by a Poisson distribution with explicit parameter. Thanks to the Chen-Stein method we provide a bound for the approximation error; this bound converges to 0 as soon as the length n of the sequence tends to ∞ and the length t of the repeats satisfies n 2 ρ t = O(1) for some 0 < ρ < 1. Using this Poisson approximation, p-values can then be easily calculated to determine if a given genome is significantly enriched in repeats of length t.

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“…The study of approximate repeats is important in functional genomics and several bioinformatics tools are available exclusively focused on this subject [17,18]. The SimSearch's similarity matrices also record the pattern interruptions.…”

Section: Approximate Repetitionsmentioning

confidence: 99%

SimSearch: A New Variant of Dynamic Programming Based on Distance Series for Optimal and Near-Optimal Similarity Discovery in Biological Sequences

Deusdado

Carvalho

Advances in Soft Computing

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Abstract. In this paper, we propose SimSearch, an algorithm implementing a new variant of dynamic programming based on distance series for optimal and near-optimal similarity discovery in biological sequences. The initial phase of SimSearch is devoted to fulfil the binary similarity matrices by signalling the distances between occurrences of the same symbol. The scoring scheme is applied further, when analysed the maximal extension of the pattern. Employing bit parallelism to analyse the global similarity matrix's upper triangle, the new methodology searches the sequence(s) for all the exact and approximate patterns in regular or reverse order. The algorithm accepts parameterization to work with greater seeds for near-optimal results. Performance tests show significant efficiency improvement over traditional optimal methods based on dynamic programming. Comparing the new algorithm's efficiency against heuristic based methods, equalizing the required sensitivity, the proposed algorithm remains acceptable.

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FORRepeats: detects repeats on entire chromosomes and between genomes

Cited by 57 publications

References 20 publications

Weak factor automata: the failure of failure factor oracles?

Weak factor automata: the failure of failure factor oracles?

Poisson Approximation for the Number of Repeats in a Stationary Markov Chain

SimSearch: A New Variant of Dynamic Programming Based on Distance Series for Optimal and Near-Optimal Similarity Discovery in Biological Sequences

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