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Material Type: Assignment; Class: Algorithms; Subject: Computer Science; University: University of Illinois - Urbana-Champaign; Term: Fall 2008;
Typology: Assignments
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bub + baseesab + anana b + u + bb + a + sees + aba + nan + a b + u + bb + a + sees + a + b + anana b + u + b + b + a + s + e + e + s + a + b + a + n + a + n + a
Describe and analyze an efficient algorithm to find the smallest number of palindromes that make up a given input string. For example, given the input string bubbaseesabanana, your algorithm would return the integer 3.
CS 573 Homework 2 (due October 1, 2008) Fall 2008
The last restriction allows us to visualize RNA secondary structure as a sort of fat tree.
A U G A G U A U A (^) A G U U A A U (^) G G U
U
A
A
A
C
U
A
A A U G^ U
U U A C A
C (^) U^ U
C
C C C^ A
C G U^ A C U A C U C A U G^ C
A
G U C
A U
G U U C
U
C U G A
Example RNA secondary structure with 21 base pairs, indicated by heavy red lines. Gaps are indicated by dotted curves. This structure has score 22 + 22 + 82 + 12 + 72 + 42 + 72 = 187
(a) Describe and analyze an algorithm that computes the maximum possible number of base pairs in a secondary structure for a given RNA sequence. (b) A gap in a secondary structure is a maximal substring of unpaired bases. Large gaps lead to chemical instabilities, so secondary structures with smaller gaps are more likely. To account for this preference, let’s define the score of a secondary structure to be the sum of the squares of the gap lengths.^1 Describe and analyze an algorithm that computes the minimum possible score of a secondary structure for a given RNA sequence.
(^1) This score function has absolutely no connection to reality; I just made it up. Real RNA structure prediction requires much more complicated scoring functions.