Lecture 7 questions

# An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)

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CSE182 Lecture 7 questions Vineet Bafna October 23, 2006 The questions are open ended, but should help you understand lectures better. Do these questions make sense? Are they helpful in following the lecture? Constructive feedback is appreciated. 1. Consider a proﬁle P of length m . For any position i , residue a , P i [ a ] is the score (not the frequency) of aligning residue a to the i -th position of the proﬁle. Describe an algorithm that ﬁnds the highest scoring local alignment of a sequence s [1 ..n ] allowing for gaps and with an indel score δ . 2. Consider the question posed in L7, slide 6. Suppose your friend uses a ’loaded’ coin in which the probability of Tails is 1. Assume also that he will switch the two coins with probability 0 . 3. (a) Describe an HMM that models the string of coin tosses
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Unformatted text preview: (b) Suppose you see the following sequence HTHTHTTTTTHHTTHHTH . Compute the maximum likelihood probability of number of times he cheated. 3. See L7, Slide 10. Construct an HMM for the 3 sequences in the family using the red ovals as match states. Compute the probability that ALIL is a member of the family using both the forward and the viterbi algorithm. (You can make up numbers for transition probabilities). 4. What is an EST? What is a 5 EST? What is a 3 EST? If you wanted to search for possible function using protein sequence analysis, would you prefer 5 ESTs, or 3ESTs? Why? If you wanted to cluster ESTs, would you prefer 5ESTs, or 3ESTs?...
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## This homework help was uploaded on 02/14/2008 for the course CSE 182 taught by Professor Bafna during the Fall '06 term at UCSD.

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