4. Calculate the score for the DNA sequence alignment below using an affine gap penalty and a linear gap penalty. The gap opening penalty is -3 and the length penalty is -2 for each gap position. Explain which alignment score reflects the less effective alignment. [3 marks] Match = +1 and Mismatch = 0 GAC TAA GGG CCG T-C ACG CAC A -- -- GGT TCA GAC GAC TAC AGC TCG TAT ACG CAC A C A T GGT TCA GAC 5. Using the following numbers 3 10 8 5 2 1 7 13 15 11 6 9 12 4 14 a) Illustrate a naïve search and a binary search algorithm for the number 8. [6 marks] b) Indicate the number of steps (iterations) that would be needed for each algorithm. [2 marks] c) Write each of your algorithms in (a) above in Big O notation. [2 marks] d) Explain using the number of iterations (Big O notation), and your worked example above, why one algorithm is superior to the other. [4 marks]
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To calculate the score for the DNA sequence alignment, we need to assign scores for matches, mismatches, and gaps. In this case, the match score is +1 and the mismatch score is 0. The given DNA sequence alignment is: GAC TAA GGG CCG T-C ACG CAC A--- GGT TCA GAC Show more…
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Individual assignment Consider global alignment with affine gap penalty function. Let the scoring scheme parameters be as follows: match score (ma) = +1; mismatch penalty (mi) = -2; gap opening penalty (h) = -5; gap extension penalty (g) = -1. Now, consider two alignments A1 and A2 between the same two DNA sequences: (A1) s1 : a c c a t t t t a - g - g - c t c s2 : a c g a - t - t a g g g g g c t c (A2) s1 : a c c a t t t t a - - - g g c t c s2 : a c g a - - t t a g g g g g c t c Compute the scores for these two alignments using the given scoring scheme.
Sri K.
Give two DNA sequences of length 6 each such that they have 4 best local alignments with a score of 12. (For alignment: Match = 6, Mismatch = -3, Gap = -4)
Shaiju T.
Due to the restrictions of current sequencing technology, we can only reliably "read" a short stretch of DNA at a time. One way we use to circumvent this issue is overlap assembly, where we identify overlaps between short regions of DNA sequences in a computational and algorithmic way. A second solution we use for this challenge is shotgun sequencing, which reduces the amount of manual work used to sequence a genome. A pairwise sequence alignment uses the Smith-Waterman algorithm to align sequences that are not related by a recent common ancestor, where a multiple sequence alignment uses the Needleman-Wunsch algorithm to align sequences that are related by a common ancestor. Pairwise alignment assumes that two sequences being compared are related by a recent common ancestor, where multiple alignment assumes the sequences being compared do not share a recent common ancestor. When scoring a pairwise sequence alignment, different types of gaps may be used. A fixed gap penalty gives equal weight to each gap found, treating each gap the same. An affine gap penalty rewards longer stretches of gaps by giving a larger penalty to a gap opening, but a smaller penalty to a gap extension, because this scenario is more biologically likely. Any portion of a DNA sequence that is especially important (such as a coding region) will accumulate changes very slowly over the course of evolution; this is called purifying selection.
Suman K.
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