DNA-DRUG_FCP: AN EFFICIENT COMPUTATIONAL METHOD FOR DNA-DRUG DESIGN USING FREQUENTLY REPEATED PATTERNS IN A LARGE HUMAN GENOME DATABASE
Sanguthevar Rajasekaran, L. Arockiam · 2015
DNA-Drug design saves lives of millions of people by stopping mutated genes to code nonfunctional proteins. The proteins coded by mutated genes cause various genetic disorders like heart disease, hypertension, low IQ and diabetes. The function of nonfunctional proteins must be stopped to cure diseases. DNA-Drug is a DNA sequence which is made to bind with mRNA to stop the function of non-functional proteins. At the same time, the bonded/attached DNA-Drug should not affect the important part of molecules. This can be done by sequence homology. Many DNA/protein sequence analysis processes like motif finding, DNA binding sites, active sites, regulatory regions require sequence patterns matching and sequence homology/similarity. The main objective of sequence homology process is to find frequently occurred repeated contiguous patterns called FCP in the DNA/mRNA sequences. These bio-sequences (DNA/Protein sequences) data are huge in size and large in volume and having widely spread frequently repeated short length patterns. This paper proposes an efficient computational method to find DNA-Drug FCP with minimum execution time and computer memory usage for very large DNA/mRNA sequences. This method selects minimum number of positionally related corresponding patterns to search patterns instead of whole DNA/mRNA sequences. This method can be used in computational modelling like Hidden Markov Model (HMM) to predict DNA-Drugs.