1. Projects…

2. FASTA Lookup Tables ACNGTSCHQE C S Q GCHCLSAGQD ACNGTSCHQE G C
sequence 1: ACNGTSCHQE
sequence 2: GCHCLSAGQD
ACNGTSCHQE
C S Q
GCHCLSAGQD
ACNGTSCHQE CH
GCHCLSAGQD
ACNGTSCHQE
G C
GCHCLSAGQD

3. SSEARCH Smith-Waterman local alignment pairwise on entire database
Extremely slow
Best for identifying weak, distant relationships
Review of Scoring

4. Scoring
Normal
Scores collected from SW matches against a database of sequences are the BEST scores for each pair, not random
Thus, distribution is not normal, but skewed positively.
For database searches, we can use the actual scores of all pairwise comparisions in DB as the set of scores.
Knowing the distribution allows us to compute P(Score≥x)
Gumbel Extreme Value Distribution has 2 parameters m(center) and l (scaling)
Extreme
Value

5. Scoring, cont. Parameter Estimation [m(center) and l (scaling)]
Estimate from moments [m = x s and l = s]
Maximum likelihood estimation
[SSEARCH, FASTA] scores between random sequences increase with sequence length.
For each seq. near length L, plot SW-score vs. log(avg.LENGTH)
Fit scores by linear regression
High scores and low outliers are trimmed from regression fit.
“normalize”: subtract predicted value from real value
Compute z-score: how many standard deviations away is normalized score
Z-scores have known extreme value distribution parameters.

6. Profile/Scoring Matrixes
So far, query is single sequence
Compare: query as regular expression or other generalized pattern
Example: Position-Specific Scoring Matrix (PSSM)
WHY?
Motifs
Multiple sequence alignments

7. PSSM A M P G V A M P G V A M P G V A 4 . . . A 4 . . . A 4 . . .C G M P V - A C G M P V - A C G M P V - =5 =5
0+0+0=0