MidClustpy: A clustering approach to predict coding region in a biological sequence

Abstract

Data mining can act like a medium to discover new avenues in bioinformatics rather than just a pattern recognition in the biological sequences. It is useful in the sequence analysis, and clustering can be used to reduce the total number of operating sequences to perform this analysis. Expressed sequence tags (ESTs) are the complimentary DNA sequences, shorter in size and instrumental in locating coding region in genomic sequences. Clustering of these ESTs requires basic computer knowledge for sequence analysis and its relevance in the field of biology. MidClustpy is an algorithm specifically designed to cluster ESTs based on the most accurate part in the sequence. The similarity search for locating coding region in a query sequence can be assisted by MidClustpy algorithm. The research paper is, thus, focussed on the effective use of expressed sequence tags using MidClustpy for prediction of coding region.