ArticleJournal of computational biology : a journal of computational molecular cell biology2014
A coverage criterion for spaced seeds and its applications to support vector machine string kernels and k-mer distances.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Entropy predicts sensitivity of pseudorandom seeds.Genome research · 2023Article
- How to optimally sample a sequence for rapid analysis.Bioinformatics (Oxford, England) · 2023Article
- Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice.Genes · 2022Article
- Mismatch-tolerant, alignment-free sequence classification using multiple spaced seeds and multiindex Bloom filters.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- SWeeP: representing large biological sequences datasets in compact vectors.Scientific reports · 2020Article
- A review of methods and databases for metagenomic classification and assembly.Briefings in bioinformatics · 2019Review
- PhylOligo: a package to identify contaminant or untargeted organism sequences in genome assemblies.Bioinformatics (Oxford, England) · 2017Article
- Best hits of 11110110111: model-free selection and parameter-free sensitivity calculation of spaced seeds.Algorithms for molecular biology : AMB · 2017Article
- rasbhari: Optimizing Spaced Seeds for Database Searching, Read Mapping and Alignment-Free Sequence Comparison.PLoS computational biology · 2016Article
- Estimating evolutionary distances between genomic sequences from spaced-word matches.Algorithms for molecular biology : AMB · 2015Article
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2 authors.
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Abstract
Spaced seeds have been recently shown to not only detect more alignments, but also to give a more accurate measure of phylogenetic distances, and to provide a lower misclassification rate when used with Support Vector Machines (SVMs). We confirm by independent experiments these two results, and propose in this article to use a coverage criterion to measure the seed efficiency in both cases in order to design better seed patterns. We show first how this coverage criterion can be directly measured by a full automaton-based approach. We then illustrate how this criterion performs when compared with two other criteria frequently used, namely the single-hit and multiple-hit criteria, through correlation coefficients with the correct classification/the true distance. At the end, for alignment-free distances, we propose an extension by adopting the coverage criterion, show how it performs, and indicate how it can be efficiently computed.
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