Write a program to compute the optimal sequence alignment of two DNA strings. This program will introduce you to the field of computational biology in which computers are used to do research on biological systems. Further, you will be introduced to a powerful algorithmic design paradigm known as dynamic programming.
Biology review. A genetic sequence is a string formed from a four-letter alphabet {Adenine (A), Thymine (T), Guanine (G), Cytosine (C)} of biological macromolecules referred to together as the DNA bases. A gene is a genetic sequence that contains the information needed to construct a protein. All of your genes taken together are referred to as the human genome, a blueprint for the parts needed to construct the proteins that form your cells. Each new cell produced by your body receives a copy of the genome. This copying process, as well as natural wear and tear, introduces a small number of changes into the sequences of many genes. Among the most common changes are the substitution of one base for another and the deletion of a substring of bases; such changes are generally referred to as point mutations. As a result of these point mutations, the same gene sequenced from closely related organisms will have slight differences.
The problem. Through your research you have found the following sequence of a gene in a previously unstudied organism.
A A C A G T T A C C
What is the function of the protein that this gene encodes? You could begin a series of uninformed experiments in the lab to determine what role this gene plays. However, there is a good chance that it is a variant of a known gene in a previously studied organism. Since biologists and computer scientists have laboriously determined (and published) the genetic sequence of many organisms (including humans), you would like to leverage this information to your advantage. We’ll compare the above genetic sequence with one which has already been sequenced and whose function is well understood.
T A A G G T C A
If the two genetic sequences are similar enough, we might expect them to have similar functions. We would like a way to quantify “similar enough.”
Edit-distance. In this assignment we will measure the similarity of two genetic sequences by their edit distance, a concept first introduced in the context of coding theory, but which is now widely used in spell checking, speech recognition, plagiarism detection, file revisioning, and computational linguistics. We align the two sequences, but we are permitted to insert gaps in either sequence (e.g., to make them have the same length). We pay a penalty for each gap that we insert and also for each pair of characters that mismatch in the final alignment. Intuitively, these penalties model the relative likeliness of point mutations arising from deletion/insertion and substitution. We produce a numerical score according to the following table, which is widely used in biological applications:
Here are two possible alignments of the strings x = "AACAGTTACC" and y = "TAAGGTCA":
The first alignment has a score of 8, while the second one has a score of 7. The edit-distance is the score of the best possible alignment between the two genetic sequences over all possible alignments. In this example, the second alignment is in fact optimal, so the edit-distance between the two strings is 7. Computing the edit-distance is a nontrivial computational problem because we must find the best alignment among exponentially many possibilities. For example, if both strings are 100 characters long, then there are more than 10^75 possible alignments.
Not finished