DNA Sequence Alignment Algorithms
DNA sequence is the way of knowing the order of nucleotides within the DNA strands. And DNA Sequence Alignment is to compare two sequences to search for the homology of a newly one of the reference sequence for analyzing the relation between the two DNA sequences. This analysis could lead to know the causative agents of some diseases and the relation between the organisms. This paper is focused on the methods of DNA sequence Alignment and their analysis and related Algorithms.
KEYWORDS
DNA Sequence, DNA alignment, Alignment Algorithms.
Introduction to DNA and its Chemical Composition
Deoxyribose nucleic acid (DNA) is a macro molecule or polymeric chemical compound composed of 4 types of building blocks called deoxyribotids or deoxyribonucleotides.
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Watson and Francis H.C Crick build a model at Cambridge University to explain how the adjacent deoxyribonucleotides are joined in a chain by Phosphodiester bond which links the 5’ Carbon of the deoxyribose sugar of one mononucleotide unit with 3’ carbon of the deoxyribose sugar of other mononucleotide …show more content…
Dot Plot Method
2. Dynamic Programming
3. Heuristic Method
Dot Plot Method: In Dot plot method the characters of two sequences are placed along x-axis and y-axis and a dot is placed when a match occur between the characters. A diagonal line indicate an aligned stretch when the diagonal is complete it means good match otherwise not. The Dot plot method can be used for sequence alignment but it does not give an optimal alignment.
Dynamic Programming:
The Dynamic programming is a method in which sub-problems are solved are solved first, these sub-problems are saved and then all the sub-answers are put together to find the solution of overall problem. The main dynamic programming algorithms for sequence alignment are Needleman-Wunsch algorithm for global alignment and Smith-Waterman algorithm for local alignment.
Needleman-Wunsch algorithm: Saul Needleman and Christian Wunsch developed an algorithm for global alignment called Needleman-Wunsch algorithm. This algorithm finds matches, mismatches and gaps using various scoring matrices. According to this algorithm matches are given higher values while mismatches and gaps are penalized, but gap is more penalized than mismatch. So an optimal alignment is that which has more matches, less mismatches and very less
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