scoring matrices
DESCRIPTION
Scoring Matrices. April 23, 2009 Learning objectives- Last word on Global Alignment Understand how the Smith-Waterman algorithm can be applied to perform local alignment. Have a general understanding about PAM and BLOSUM scoring matrices. Homework 3 and 4 due today Quiz 1 today - PowerPoint PPT PresentationTRANSCRIPT
Scoring MatricesApril 23, 2009Learning objectives-
1) Last word on Global Alignment2) Understand how the Smith-Waterman algorithm can be applied to perform local alignment. 3) Have a general understanding about PAM and BLOSUM scoring matrices.
Homework 3 and 4 due todayQuiz 1 todayWriting topic due todayHomework 5 due Thursday, April 30.
Global Alignment output file
Global: HBA_HUMAN vs HBB_HUMANScore: 290.50
HBA_HUMAN 1 VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFP 44 |:| :|: | | |||| : | | ||| |: : :| |: :|HBB_HUMAN 1 VHLTPEEKSAVTALWGKV..NVDEVGGEALGRLLVVYPWTQRFFE 43
HBA_HUMAN 45 HF.DLS.....HGSAQVKGHGKKVADALTNAVAHVDDMPNALSAL 83 | ||| |: :|| ||||| | :: :||:|:: : |HBB_HUMAN 44 SFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATL 88
HBA_HUMAN 84 SDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKF 128 |:|| || ||| ||:|| : |: || | |||| | |: |HBB_HUMAN 89 SELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKV 133
HBA_HUMAN 129 LASVSTVLTSKYR 141 :| |: | ||HBB_HUMAN 134 VAGVANALAHKYH 146
%id = 45.32 %similarity = 63.31 (88/139 *100)Overall %id = 43.15; Overall %similarity = 60.27 (88/146 *100)
Smith-Waterman Algorithm Advances inApplied Mathematics, 2:482-489 (1981)
Smith-Waterman algorithm –can be used for local alignment
-Memory intensive
-Common searching programs such as BLAST use SW algorithm
Smith-Waterman (cont. 1)
a. Initializes edges of the matrix with zerosb. It searches for sequence matches.c. Assigns a score to each pair of amino acids
-uses similarity scores-uses positive scores for related residues-uses negative scores for substitutions and gaps
d. Scores are summed for placement into Mi,j. If any sum result is below 0, a 0 is placed into Mi,j.e. Backtracing begins at the maximum value found anywhere in the matrix.f. Backtrace continues until the it meets an Mi,j value of 0.
0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 00 0 0 5 0 5 0 0 0 0 00 0 0 0 3 0 2012 4 0 00 10 2 0 0 1 12182214 60 2 16 8 0 0 4101828 200 0 82113 5 0 41020 270 0 6131912 4 0 416 26
H E A G A W G H E E
PAWHEAE
Smith-Waterman (cont. 2)
Put zeros on top row and left column. Assign initial scoresbased on a scoringmatrix. Calculate new scores based onadjacent cell scores.If sum is less thanzero or equal to zerobegin new scoring with next cell.
This example uses the BLOSUM45 Scoring Matrix with a gap penalty of -8.
0 0 0 0 0 0 0 0 0 0 00 0 0 0 0 0 0 0 0 0 00 0 0 5 0 5 0 0 0 0 00 0 0 0 3 0 2012 4 0 00 10 2 0 0 1 12182214 60 2 16 8 0 0 4101828 200 0 82113 5 0 41020 270 0 6131912 4 0 416 26
H E A G A W G H E E
PAWHEAE
Smith-Waterman (cont. 3)
Begin backtrace at themaximum value foundanywhere on the matrix.Continue the backtraceuntil score falls to zero
AWGHE|| ||AW-HE
Score=28
Calculation of similarity score and percent similarity
A W G H EA W - H E
Blosum45 SCORES 5 15 -8 10 6
GAP PENALTY (novel)
% SIMILARITY = NUMBER OF POS. SCORESDIVIDED BY NUMBER OF AAsIN REGION x 100
% SIMILARITY = 4/5 x 100= 80%
Similarity Score= 28
Why search sequence databases?
1. I have just sequenced something. What is known about the thing I sequenced?
2. I have a unique sequence. Does it have similarity to another gene of known function?
3. I found a new protein sequence in a lower organism. Is it similar to a protein from another species?
Perfect searches for similar sequences in a database
First “hit” should be an exact match.
Next “hits” should contain all of the genes that are related to your gene (homologs).
Next “hits” should be similar but are not homologs
How does one achieve the “perfect search”?
Consider the following:Scoring Matrices (PAM vs. BLOSUM)Local alignment algorithmDatabaseSearch Parameters Expect Value-change threshold for score
reporting Translation-of DNA sequence into protein Filtering-remove repeat sequences
Which Scoring Matrix to use?
PAM-1
BLOSUM-100
Small evolutionary distance
High identity within short sequences
PAM-250
BLOSUM-20
Large evolutionary distance
Low identity within long sequences
BLOSUM Scoring Matrices
Which BLOSUM Matrix to use?
BLOSUM Identity (up to)
80 80% 62 62% (usually default value) 35 35%
If you are comparing sequences that are very similar, useBLOSUM 80. Sequences that are more divergent (dissimilar)than 20% are given very low scores in this matrix.