SpEED: fast computation of sensitive spaced seeds

Ilie, Lucian; Ilie, Silvana; Bigvand, Anahita Mansouri

doi:10.1093/bioinformatics/btr368

Cited by 51 publications

(54 citation statements)

References 12 publications

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“…As a direct consequence of this work, spaced seeds are now used in the state-of-the-art homology search methods, such as Blast [1] or MegaBlast [26]. For more information about spaced seeds, we also refer the reader to [5,6,[11][12][13][14] and references therein.…”

Section: Preliminariesmentioning

confidence: 99%

“…The selection of specific spaced seed is critical to achieve high precision and sensitivity (see, e.g., [5,6,[11][12][13][14]17]). For contiguous k-mers, the classification precision increases as we increase k. However, the highest sensitivity occurs with somewhat shorter k-mers.…”

Section: Selection Of Optimal Spaced Seeds and Index Creationmentioning

confidence: 99%

“…We searched exhaustively through all the spaced seeds of length k = 31 and weight w = 22 (starting/ending with '1') using a similarity level of 95 %, and a random region of length 100 bp, by using the dynamic programming approach from [17] and implemented in [12]. The spaced seed with the highest hit probability [17], 0.998113, is 1111*111*111**1*111**1*11*11111.…”

Section: Selection Of Optimal Spaced Seeds and Index Creationmentioning

confidence: 99%

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Higher Classification Accuracy of Short Metagenomic Reads by Discriminative Spaced k-mers

Ounit

Lonardi

2015

Lecture Notes in Computer Science

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Abstract. The growing number of metagenomic studies in medicine and environmental sciences is creating new computational demands in the analysis of these very large datasets. We have recently proposed a timeefficient algorithm called Clark that can accurately classify metagenomic sequences against a set of reference genomes. The competitive advantage of Clark depends on the use of discriminative contiguous kmers. In default mode, Clark's speed is currently unmatched and its precision is comparable to the state-of-the-art, however, its sensitivity still does not match the level of the most sensitive (but slowest) metagenomic classifier. In this paper, we introduce an algorithmic improvement that allows Clark's classification sensitivity to match the best metagenomic classifier, without a significant loss of speed or precision compared to the original version. Finally, on real metagenomes, Clark can assign with high accuracy a much higher proportion of short reads than its closest competitor. The improved version of Clark, based on discriminative spaced k-mers, is freely available at

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Section: Preliminariesmentioning

confidence: 99%

Section: Selection Of Optimal Spaced Seeds and Index Creationmentioning

confidence: 99%

Section: Selection Of Optimal Spaced Seeds and Index Creationmentioning

confidence: 99%

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Higher Classification Accuracy of Short Metagenomic Reads by Discriminative Spaced k-mers

Ounit

Lonardi

2015

Lecture Notes in Computer Science

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“…For multiple seeds, since the relevant problems are hard [14,15], heuristic algorithms must be used. Among many such algorithms, such as Mandala [16] and Iedera [17], only one works in polynomial time: SpEED [18]. SpEED is based on the notion of overlap complexity [19], that is very well correlated with sensitivity but polynomial-time computable.…”

Section: Introductionmentioning

confidence: 99%

“…The problem of finding optimal seeds seems difficult and heuristic algorithms are employed. The only polynomial-time algorithm, implemented in SpEED [18], is based on the notion of overlap complexity [19], that captures the amount of overlaps between hits. Given two seeds s 1 and s 2 , for each of the possible | s 1 | + | s 2 |-1 overlaps between them, denote by σ i the number of overlapping positions where both seeds have a 1.…”

Section: Introductionmentioning

confidence: 99%

Efficient computation of spaced seeds

Ilie

2012

BMC Res Notes

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BackgroundThe most frequently used tools in bioinformatics are those searching for similarities, or local alignments, between biological sequences. Since the exact dynamic programming algorithm is quadratic, linear-time heuristics such as BLAST are used. Spaced seeds are much more sensitive than the consecutive seed of BLAST and using several seeds represents the current state of the art in approximate search for biological sequences. The most important aspect is computing highly sensitive seeds. Since the problem seems hard, heuristic algorithms are used. The leading software in the common Bernoulli model is the SpEED program.FindingsSpEED uses a hill climbing method based on the overlap complexity heuristic. We propose a new algorithm for this heuristic that improves its speed by over one order of magnitude. We use the new implementation to compute improved seeds for several software programs. We compute as well multiple seeds of the same weight as MegaBLAST, that greatly improve its sensitivity.ConclusionMultiple spaced seeds are being successfully used in bioinformatics software programs. Enabling researchers to compute very fast high quality seeds will help expanding the range of their applications.

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Sequence Comparison Without Alignment: The SpaM Approaches

Morgenstern

2020

Methods in Molecular Biology

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SpEED: fast computation of sensitive spaced seeds

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 51 publications

References 12 publications

Higher Classification Accuracy of Short Metagenomic Reads by Discriminative Spaced k-mers

Higher Classification Accuracy of Short Metagenomic Reads by Discriminative Spaced k-mers

Efficient computation of spaced seeds

Sequence Comparison Without Alignment: The SpaM Approaches

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