A Coverage Criterion for Spaced Seeds and Its Applications to Support Vector Machine String Kernels and<i>k</i>-Mer Distances

Noé, Laurent; Martin, Donald E. K.

doi:10.1089/cmb.2014.0173

Cited by 23 publications

(29 citation statements)

References 72 publications

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“…In conclusion, while spaced seeds provide a much better estimator for alignments whose quality ranges over a large interval, for high-quality alignments (> 90% of identity), the hit number of contiguous seed becomes a better estimator. The superiority of hit-number over coverage for high-quality alignments has also been reported in [32]. Along with Spearman's correlation, we also made an analysis of mutual information computed on the same data (data not shown) that confirmed the above conclusions.…”

Section: Correlation Of Counts With Alignment Qualitysupporting

confidence: 81%

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Spaced seeds improvek-mer-based metagenomic classification

2015

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Section: Correlation Of Counts With Alignment Qualitysupporting

confidence: 81%

“…Another improvement considered in [21,32,29] is to use multiple seeds, i.e. several seeds simultaneously instead of a single one.…”

Section: Discussionmentioning

confidence: 99%

Spaced seeds improvek-mer-based metagenomic classification

2015

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“…To approximate a measure of conserved nucleotides, the coverage is projected over individual nucleotides rather than directly counting shared skip-mers which would introduce redundancy from phased matches. An equivalent coverage metric for spaced seeds can be found in Noé and Martin (2014) where it is also used to estimate distances. The score for each feature (i.e.…”

Section: Methodsmentioning

confidence: 99%

Skip-mers: increasing entropy and sensitivity to detect conserved genic regions with simple cyclic q-grams

Clavijo

Garcia

Yanes

et al. 2017

Preprint

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10Bioinformatic analyses and tools make extensive use of k-mers (fixed contiguous strings of k nucleotides) as an informational unit. K-mer analyses are both useful and fast, but are strongly affected by singlenucleotide polymorphisms or sequencing errors, effectively hindering direct-analyses of whole regions and decreasing their usability between evolutionary distant samples. Q-grams or spaced seeds, subsequences generated with a pattern of used-and-skipped nucleotides, overcome many of these limitations but introduce larger complexity which hinders their wider adoption. We introduce a concept of skip-mers, a cyclic pattern of used-and-skipped positions of k nucleotides spanning a region of size S ≥ k, and show how analyses are improved by using this simple subset of q-grams as a replacement for k-mers. The entropy of skip-mers increases with the larger span, capturing information from more distant positions and increasing the specificity, and uniqueness, of larger span skip-mers within a genome. In addition, skip-mers constructed in cycles of 1 or 2 nucleotides in every 3 (or a multiple of 3) lead to increased sensitivity in the coding regions of genes, by grouping together the more conserved nucleotides of the protein-coding regions. We implemented a set of tools to count and intersect skip-mers between different datasets, a simple task given that the properties of skip-mers make them a direct substitute for k-mers. We used these tools to show how skip-mers have advantages over k-mers in terms of entropy and increased sensitivity to detect conserved coding sequence, allowing better identification of genic matches between evolutionarily distant species. We then show benefits for multi-genome analyses provided by increased and better correlated coverage of conserved skip-mers across multiple samples.

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“…Spaced seeds are widely used for approximate sequence matching in bioinformatics and they have been increasingly applied to improve the sensitivity and specificity of homology search algorithms (Kucherov et al, 2006;Noé and Martin, 2014). Spaced seeds are now routinely used, instead of k-mers, in many problems involving sequence comparison such as multiple sequence alignment (Darling et al, 2006), protein classification (Onodera and Shibuya, 2013), read mapping (Rumble et al, 2009), phylogeny reconstruction (Leimeister et al, 2014), and metagenome reads clustering and classification (Binda et al, 2015;Ounit and Lonardi, 2016;Girotto et al, 2017c).…”

Section: Introductionmentioning

confidence: 99%

Iterative Spaced Seed Hashing: Closing the Gap Between Spaced Seed Hashing and k-mer Hashing

Petrucci

Noé

Pizzi

et al. 2020

Journal of Computational Biology

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Alignment-free classification of sequences has enabled high-throughput processing of sequencing data in many bioinformatics pipelines. Much work has been done to speed up the indexing of k-mers through hash-table and other data structures. These efforts have led to very fast indexes, but because they are k-mer based, they often lack sensitivity due to sequencing errors or polymorphisms. Spaced seeds are a special type of pattern that accounts for errors or mutations. They allow to improve the sensitivity and they are now routinely used instead of k-mers in many applications. The major drawback of spaced seeds is that they cannot be efficiently hashed and thus their usage increases substantially the computational time. In this article we address the problem of efficient spaced seed hashing. We propose an iterative algorithm that combines multiple spaced seed hashes by exploiting the similarity of adjacent hash values to efficiently compute the next hash. We report a series of experiments on HTS reads hashing, with several spaced seeds. Our algorithm can compute the hashing values of spaced seeds with a speedup in range of [3.5 3-7 3 ], outperforming previous methods. Software and data sets are available at Iterative Spaced Seed Hashing.

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A Coverage Criterion for Spaced Seeds and Its Applications to Support Vector Machine String Kernels andk-Mer Distances

Cited by 23 publications

References 72 publications

Spaced seeds improvek-mer-based metagenomic classification

Spaced seeds improvek-mer-based metagenomic classification

Skip-mers: increasing entropy and sensitivity to detect conserved genic regions with simple cyclic q-grams

Iterative Spaced Seed Hashing: Closing the Gap Between Spaced Seed Hashing and k-mer Hashing

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