A Unifying Framework for Seed Sensitivity and Its Application to Subset Seeds

Kucherov, Grégory; Noé, Laurent; Roytberg, Mikhail A.

doi:10.1142/s0219720006001977

Cited by 66 publications

(89 citation statements)

References 13 publications

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“…The web interface provides a preselection of seeds including three transition-constrained seeds, one providing a good performance compromise between coding and non-coding sequences, and the other two tuned respectively for non-coding and coding regions. The accompanying Hedera program () is also provided for advanced users in order to design new seed patterns according to different probabilistic models of alignments (15). …”

Section: Descriptionmentioning

confidence: 99%

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YASS: enhancing the sensitivity of DNA similarity search

Noé

Kucherov

2005

Nucleic Acids Research

301

195

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YASS is a DNA local alignment tool based on an efficient and sensitive filtering algorithm. It applies transition-constrained seeds to specify the most probable conserved motifs between homologous sequences, combined with a flexible hit criterion used to identify groups of seeds that are likely to exhibit significant alignments. A web interface () is available to upload input sequences in fasta format, query the program and visualize the results obtained in several forms (dot-plot, tabular output and others). A standalone version is available for download from the web page.

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Section: Descriptionmentioning

confidence: 99%

“…The recently introduced spaced seeds technique (7,8) allows an increase in sensitivity without loss in selectivity. This innovation triggered various studies (9–15) related to the usage, design and generalizations of spaced seeds.…”

Section: Introductionmentioning

confidence: 99%

YASS: enhancing the sensitivity of DNA similarity search

Noé

Kucherov

2005

Nucleic Acids Research

301

195

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“…Noteworthy is the existence of at least two heuristic or exact algorithms to determine a good or an optimal seed for a given search problem, see for instance the Mandala [16] and Hedera [21] programs.…”

Section: Seed Designmentioning

confidence: 99%

“…In this work we concentrate on combinatorial seed design, which has also been addressed by several groups [1,8,10,12,21,22]. We consider formulations in which the set of sought similarities is either listed explicitly (RSOS, MWLS), or characterised by their length and maximal number of mismatches (NON-DETECTION).…”

Section: Seed Designmentioning

confidence: 99%

“…In two formulations (NON-DETECTION, MWLS), one searches for a seed that detects all similarities, while in the third one (RSOS) one seeks a seed of maximal sensitivity for a given specificity. Several algorithms whose complexities depend exponentially on the length of the seed have been proposed to solve these combinatorial problems [8,12,21], but it is not known to which complexity class the simplest forms of the seed design problem belong. Our article answers these questions by showing these problems are NP-hard, or even worse, difficult to approximate.…”

Section: Seed Designmentioning

confidence: 99%

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Hardness of optimal spaced seed design

Nicolas

Rivals

2008

Journal of Computer and System Sciences

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Speeding up approximate pattern matching is a line of research in stringology since the 80s. Practically fast approaches belong to the class of filtration algorithms, in which text regions dissimilar to the pattern are first excluded, and the remaining regions are then compared to the pattern by dynamic programming. Among the conditions used to test similarity between the regions and the pattern, many require a minimum number of common substrings between them. When only substitutions are taken into account for measuring dissimilarity, counting spaced subwords instead of substrings improves the filtration efficiency. However, a preprocessing step is required to design one or more patterns, called spaced seeds (or gapped seeds), for the subwords, depending on the search parameters. Two distinct lines of research appear the literature: one with probabilistic formulations of seed design problems, in which one wishes for instance to compute a seed with the highest probability to detect the desired similarities (lossy filtration), a second line with combinatorial formulations, where the goal is to find a seed that detects all or a maximum number of similarities (both lossless and lossy filtration). We concentrate on combinatorial seed design problems and consider formulations in which the set of sought similarities is either listed explicitly (RSOS), or characterised by their length and maximal number of mismatches (NON-DETECTION). Several articles exhibit exponential algorithms for these problems. In this work, we provide hardness and inapproximability results for several seed design problems, thereby justifying the complexity of known algorithms. Moreover, we introduce a new formulation of seed design (MWLS), in which the weight of the seed has to be maximised, and show it is as difficult to approximate as MAXIMUM INDEPENDENT SET.

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