Identification of a new motif on nucleic acid sequence data using Kohonen's self-organizing map

Here we present a performance test of a Kohonen features map applied to the fast extraction of uncommon sequences from the coding region of the human insulin receptor gene. We used a network with 30 neurons and with a variable input window. The program was aimed at detecting unique or uncommon DNA regions present in crude sequence data and was able to automatically detect the signal peptide coding regions of a set of human insulin receptor gene data. The testing of this program with HSIRPR cDNA release (EMBL data bank) indicated the presence of unique features in the signal peptide coding region. On the basis of our results this program can automatically detect 'singularity' from crude sequencing data and it does not require knowledge of the features to be found.