1. Indiana University, Bloomington, IN
Sequence Homology
M.M. Dalkilic, PhD
Monday, September 08, 2008
Class II
Indiana University, Bloomington, IN
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

2. Outline Sword of Damocles New Reading Posted on Website
New Lab II / Homework Posted on Website
Readings [Mount] Chap 3, [R] Chaps 3-4
Most Important Aspect of Bioinformatics—homology search through sequence similarity
Lab I ?
Skeptical
Due
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

3. Sequence Similarity Most common Bioinformatics tool in use
Outline (cont’d)
Sequence Similarity
Most common Bioinformatics tool in use
One of the most misunderstood tools in use
Requires a great deal of background in ancillary disciplines—especially computation
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

4. Computational Elements of Sequence Similarity
Outline (cont’d)
Computational Elements of Sequence Similarity
Algorithms
Complexity
Recursion & recurrence relations
Program strategies to reduce complexity of algorithms
Divide and Conquer
Dynamic Programming
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

5. How do we compare two “things”
Glutumate Neg. charged R-group
Biochemcal Example
“representation of sequence of molecules”
Aspartate Neg. Charged R-group
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

6. Algorithm Computation
“process or rules for (esp. machine) calculations. The execution of an algorithm must not include any subjective decisions, nor must it require the use of intuition or creativity” [Brassard & Bratley]
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

7. Algorithm Computation
When we set out to solve a problem, it is important to decide…on
(1) size of the instances
(2) representation
(3) time/memory considerations
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

8. Algorithm Computation Size of the instances Representation
Time/Memory considerations
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

9. Computation constant Upper bound starts Upper bound
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

10. Computation
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

11. Computation Recurrence relations—relations defined by initial point
recursive (self-depreciation)
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

12. Computation
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

13. Computation Though every recursive program can be written as
as a while
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

14. Computation
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

15. Computation
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©

16. Computation (Next Lecture)
Divide and Conquer gives rise to
Dynamic Programming—the approach
used in sequence comparison
Sequence Similiarty (Computation) M.M. Dalkilic, PhD SoI Indiana University, Bloomington, IN 2008 ©