“A structure based flexible search method for motifs in RNA” Isana ...

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The Tree Alignment Algorithm � We define score(u,v) to be: a subtree of P rooted in node u�P a subtree of T rooted in node v�T
The two-stage DP approach to the tree alignment The compared trees Large DP - m*n table = score(a,1) Activated during computation of each non-leaf entry (u,v) in the L DP in order to compute the optimal mapping between the children of u and the children of v. Small DP - comparing subtrees of f and 9 ( second-level dynamic programming )
Page 1 and 2: STRMS “A structure based flexible
Page 3 and 4: The Central Dogma of DNA transcript
Page 5 and 6: Non Coding RNA Families �They are
Page 7 and 8: Our Goal Discover ncRNA motifs in a
Page 9 and 10: Our method consists of two phases:
Page 11 and 12: RNA’s Secondary Structure (((((((
Page 13 and 14: Ordered rooted tree Shapiro, 1988:
Page 15 and 16: Our tree representation origin of a
Page 17 and 18: Comparison of ordered rooted trees
Page 19 and 20: 1. Edit distance � An edit script
Page 21 and 22: 1. Edit distance
Page 23 and 24: 1. Edit distance
Page 25 and 26: 2. Tree Inclusion T1 is included in
Page 27 and 28: � Polynomial time algorithms exis
Page 29 and 30: The Algorithm � The subtree isomo
Page 31 and 32: The Algorithm - Motivation � In s
Page 33 and 34: Subtree Homeomorphism Score � Giv
Page 35: The Tree Alignment Algorithm � A
Page 39 and 40: Dealing with Potential Pseudoknots
Page 41 and 42: Updating the S DP X : pseudoknot in
Page 43 and 44: Experimental Results � Riboswitch
Page 45 and 46: Purine Riboswitch The secondary str
Page 47 and 48: Results - Second dataset � The se
Page 49: Searching for riboswitches in newly

“A structure based flexible search method for motifs in RNA” Isana ...

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