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Open Access Highly Accessed Research article

MS4 - Multi-Scale Selector of Sequence Signatures: An alignment-free method for classification of biological sequences

Eduardo Corel13, Florian Pitschi2, Ivan Laprevotte3, Gilles Grasseau3, Gilles Didier4 and Claudine Devauchelle3*

Author Affiliations

1 Georg-August-Universität, Institut für Mikrobiologie und Genetik, Goldschmidtstraβe 1, 37077 Göttingen, Germany

2 Partner Institute for Computational Biology, CAS-MPG, 320 Yue Yang Rd, 200031 Shanghai, China

3 Laboratoire Statistique et Génome (LSG), CNRS UMR 8071, INRA 1152, Université d'Evry, Tour Evry2, Place des Terrasses, 91034 Evry Cedex, France

4 Institut de Mathématiques de Luminy, UMR 6206, Luminy, Marseille, France

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BMC Bioinformatics 2010, 11:406  doi:10.1186/1471-2105-11-406

Published: 30 July 2010



While 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.


Among 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 κ 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).


The 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 κ 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.