Representation of DNA sequences with virtual potentials and their processing by (SEQREP) Kohonen self-organizing maps.

Abstract

We propose representing individual positions in DNA sequences by virtual potentials generated by other bases of the same sequence. This is a compact representation of the neighbourhood of a base. The distribution of the virtual potentials over the whole sequence can be used as a representation of the entire sequence (SEQREP code). It is a flexible code, with a length independent of the sequence size, does not require previous alignment, and is convenient for processing by neural networks or statistical techniques. To evaluate its biological significance, the SEQREP code was used for training Kohonen self-organizing maps (SOMs) in two applications: (a) detection of Alu sequences, and (b) classification of sequences encoding for HIV-1 envelope glycoprotein (env) into subtypes A-G. It was demonstrated that SOMs clustered sequences belonging to different classes into distinct regions. For independent test sets, very high rates of correct predictions were obtained (97% in the first application, 91% in the second). Possible areas of application of SEQREP codes include functional genomics, phylogenetic analysis, detection of repetitions, database retrieval, and automatic alignment. Software for representing sequences by SEQREP code, and for training Kohonen SOMs is made freely available from http://www.dq.fct.unl.pt/qoa/jas/seqrep. Supplementary material is available at http://www.dq.fct.unl.pt/qoa/jas/seqrep/bioinf2002