Mining, compressing and classifying with extensible motifs

Alberto Apostolico¹^,2 , Matteo Comin¹ and Laxmi Parida³

¹Dipartimento di Ingegneria dell'lnformazione, Università di Padova, Padova, Italy

²College of Computing, Georgia Institute of Technology, 801 Atlantic Drive, Atlanta, GA 30332, USA

³IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, USA

author email corresponding author email

Algorithms for Molecular Biology 2006, 1:4doi:10.1186/1748-7188-1-4


Published:	23 March 2006

Abstract

Background

Motif patterns of maximal saturation emerged originally in contexts of pattern discovery in biomolecular sequences and have recently proven a valuable notion also in the design of data compression schemes. Informally, a motif is a string of intermittently solid and wild characters that recurs more or less frequently in an input sequence or family of sequences. Motif discovery techniques and tools tend to be computationally imposing, however, special classes of "rigid" motifs have been identified of which the discovery is affordable in low polynomial time.

Results

In the present work, "extensible" motifs are considered such that each sequence of gaps comes endowed with some elasticity, whereby the same pattern may be stretched to fit segments of the source that match all the solid characters but are otherwise of different lengths. A few applications of this notion are then described. In applications of data compression by textual substitution, extensible motifs are seen to bring savings on the size of the codebook, and hence to improve compression. In germane contexts, in which compressibility is used in its dual role as a basis for structural inference and classification, extensible motifs are seen to support unsupervised classification and phylogeny reconstruction.

Conclusion

Off-line compression based on extensible motifs can be used advantageously to compress and classify biological sequences.