Computational model for prokaryotic and eukaryotic gene prediction

Abstract

In this paper we have design a computational model for prokaryotic and eukaryotic gene prediction by using the clustering algorithm. The input DNA (Deoxyribonucleic Acid) sequence is spliced and the open reading frames are identified. For identification of consensus sequences various data mining algorithm is applied for creation of clusters. This model saves the implementation time, as whole of the database is present online so the sequence to be predicted is just taken from any one of the available database. Several experiments have been done where the parameters of gene prediction are changed manually. The performance has been tested on different unknown DNA sequences found on the internet. The sequences having score greater than or equal to the threshold value are entered into one cluster and rest of the sequences having score less than the given threshold are entered into second cluster and GC (Guanine and cytosine)-content percentage is calculated. © 2011 Springer-Verlag.