Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

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

For all author emails, please log on.

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

Published: 30 July 2010

Abstract

Background

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.

Results

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).

Conclusions

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.