ArticleBMC bioinformatics2010
MS4--Multi-Scale Selector of Sequence Signatures: an alignment-free method for classification of biological sequences.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification.Nucleic acids research · 2014Article
- Fast alignment-free sequence comparison using spaced-word frequencies.Bioinformatics (Oxford, England) · 2014Article
- Automatic detection of anchor points for multiple sequence alignment.BMC bioinformatics · 2010Article
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6 authors.
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Abstract
backgroundWhile multiple alignment is the first step of usual classification schemes for biological sequences, alignment-free methods are being increasingly used as alternatives when multiple alignments fail. Subword-based combinatorial methods are popular for their low algorithmic complexity (suffix trees ...) or exhaustivity (motif search), in general with fixed length word and/or number of mismatches. We developed previously a method to detect local similarities (the N-local decoding) based on the occurrences of repeated subwords of fixed length, which does not impose a fixed number of mismatches. The resulting similarities are, for some "good" values of N, sufficiently relevant to form the basis of a reliable alignment-free classification. The aim of this paper is to develop a method that uses the similarities detected by N-local decoding while not imposing a fixed value of N. We present a procedure that selects for every position in the sequences an adaptive value of N, and we implement it as the MS4 classification tool.
resultsAmong the equivalence classes produced by the N-local decodings for all N, we select a (relatively) small number of "relevant" classes corresponding to variable length subwords that carry enough information to perform the classification. The parameter N, for which correct values are data-dependent and thus hard to guess, is here replaced by the average repetitivity kappa of the sequences. We show that our approach yields classifications of several sets of HIV/SIV sequences that agree with the accepted taxonomy, even on usually discarded repetitive regions (like the non-coding part of LTR).
conclusionsThe method MS4 satisfactorily classifies a set of sequences that are notoriously hard to align. This suggests that our approach forms the basis of a reliable alignment-free classification tool. The only parameter kappa of MS4 seems to give reasonable results even for its default value, which can be a great advantage for sequence sets for which little information is available.
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