1 lesson 2 aligning sequences and searching databases
TRANSCRIPT
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Lesson 2
Aligning sequences and searching databases
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Homology and sequence alignment.
HomologyHomology = Similarity between objects due to a common ancestry
Hund = Dog,Schwein = Pig
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Sequence homology
VLSPAVKWAKVGAHAAGHG||| || |||| | ||||VLSEAVLWAKVEADVAGHG
Similarity between sequences as a result of common ancestry.
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Sequence alignment
Alignment: Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences.
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Why align?VLSPAVKWAKV||| || |||| VLSEAVLWAKV
1. To detect if two sequences are homologous. If so, homology may indicate similarity in function (and structure).
2. Required for evolutionary studies (e.g., tree reconstruction).
3. To detect conservation (e.g., a tyrosine that is evolutionary conserved is more likely to be a phosphorylation site).
4. Given a sequenced DNA, from an unknown region, align it to the genome.
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Insertions, deletions, and substitutions
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Sequence alignment
If two sequences share a common ancestor – for example human and dog hemoglobin, we can represent their evolutionary relationship using a tree
VLSPAV-WAKV||| || |||| VLSEAVLWAKV
VLSPAV-WAKV VLSEAVLWAKV
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Perfect match
VLSPAV-WAKV||| || |||| VLSEAVLWAKV
VLSPAV-WAKV VLSEAVLWAKV
A perfect match suggests that no change has occurred from the common ancestor (although this is not always the case).
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A substitution
VLSPAV-WAKV||| || |||| VLSEAVLWAKV
VLSPAV-WAKV VLSEAVLWAKV
A substitution suggests that at least one change has occurred since the common ancestor (although we cannot say in which lineage it has occurred).
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Indel
VLSPAV-WAKV||| || |||| VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVLWAKV
Option 1: The ancestor had L and it was lost here. In such a case, the event was a deletion.
VLSEAVLWAKV
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Indel
VLSPAV-WAKV||| || |||| VLSEAVLWAKV
VLSPAV-WAKV
VLSEAVWAKV
Option 2: The ancestor was shorter and the L was inserted here. In such a case, the event was an insertion.
VLSEAVLWAKV
L
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Indel
VLSPAV-WAKV
Normally, given two sequences we cannot tell whether it was an insertion or a deletion, so we term the event as an indel.
VLSEAVLWAKV
Deletion? Insertion?
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Indels in protein coding genes
Indels in protein coding genes are often of 3bp, 6bp, 9bp, etc...
Gene Search
In fact, searching for indels of length 3K (K=1,2,3,…) can help algorithms that search a genome for coding regions
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Global and Local pairwise alignments
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Global vs. Local
• Global alignment – finds the best alignment across the entire two sequences.
• Local alignment – finds regions of similarity in parts of the sequences.
ADLGAVFALCDRYFQ|||| |||| |ADLGRTQN-CDRYYQ
ADLG CDRYFQ|||| |||| |ADLG CDRYYQ
Global alignment:
forces alignment in
regions which differ
Local alignment will
return only regions of
good alignment
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Global alignment
PTK2 protein tyrosine kinase 2 of human and rhesus monkey
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Proteins are comprised of domains
Domain B
Protein tyrosine kinase domain
Domain A
Human PTK2 :
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Protein tyrosine kinase domain
In leukocytes, a different gene for tyrosine kinase is expressed.
Domain X
Protein tyrosine kinase domain
Domain A
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Domain X
Protein tyrosine kinase domain
Domain BProtein tyrosine kinase domain
Domain A
Leukocyte TK
PTK2 The sequence similarity is restricted to a single domain
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Global alignment of PTK and LTK
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Local alignment of PTK and LTK
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Conclusions
Use global alignment when the two sequences share the same overall sequence arrangement.
Use local alignment to detect regions of similarity.
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How alignments are computed
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Pairwise alignment
AAGCTGAATTCGAAAGGCTCATTTCTGA
AAGCTGAATT-C-GAAAGGCT-CATTTCTGA-
One possible alignment:
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AAGCTGAATT-C-GAAAGGCT-CATTTCTGA-
This alignment includes:2 mismatches 4 indels (gap)
10 perfect matches
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Choosing an alignment for a pair of sequences
AAGCTGAATTCGAAAGGCTCATTTCTGA
AAGCTGAATT-C-GAAAGGCT-CATTTCTGA-
A-AGCTGAATTC--GAAAG-GCTCA-TTTCTGA-
Which alignment is better?
Many different alignments are
possible for 2 sequences:
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Scoring system (naïve)
AAGCTGAATT-C-GAAAGGCT-CATTTCTGA-
Score: = (+1)x10 + (-2)x2 + (-1)x4 = 2 Score: = (+1)x9 + (-2)x2 + (-1)x6 = -1
A-AGCTGAATTC--GAAAG-GCTCA-TTTCTGA-
Higher score Better alignment
Perfect match: +1
Mismatch: -2
Indel (gap): -1
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Alignment scoring - scoring of sequence similarity:
Assumes independence between positions:each position is considered separately
Scores each position:• Positive if identical (match)• Negative if different (mismatch or gap)
Total score = sum of position scoresCan be positive or negative
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Scoring systems
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Scoring system
•In the example above, the choice of +1 for match,-2 for mismatch, and -1 for gap is quite arbitrary
•Different scoring systems different alignments
•We want a good scoring system…
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Scoring matrix
A G C T
A 2
G -6 2
C -6 -6 2
T -6 -6 -6 2
•Representing the scoring system as a table or matrix n X n (n is the number of letters the alphabet contains. n=4 for nucleotides, n=20 for amino acids)
•symmetric
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DNA scoring matrices
• Uniform substitutions between all nucleotides:
From
To
A G C T
A 2
G -6 2
C -6 -6 2
T -6 -6 -6 2
MatchMismatch
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DNA scoring matrices
Can take into account biological phenomena such as:
• Transition-transversion
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Amino-acid scoring matrices• Take into account physico-chemical properties
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Scoring gaps (I)
In advanced algorithms, two gaps of one amino-acid are given a different score than one gap of two amino acids. This is solved by giving a penalty to each gap that is opened.
Gap extension penalty < Gap opening penalty
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Scoring gaps (II)
The dependency between the penalty and the length of the gap need not to be linear.
AGGGTTC—GAAGGGTTCTGA Score = -2
AGGGTT-—GAAGGGTTCTGA Score = -4
AGGGT--—GAAGGGTTCTGA Score = -6
AGGG---—GAAGGGTTCTGA Score = -8
Linear penalty
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Scoring gaps (II)
The dependency between the penalty and the length of the gap need not to be linear.
AGGGTTC—GAAGGGTTCTGA Score = -4
AGGGTT-—GAAGGGTTCTGA Score = -6
AGGGT--—GAAGGGTTCTGA Score = -7
AGGG---—GAAGGGTTCTGA Score = -8
Non-linear penalty
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PAM AND BLOSUM
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Amino-acid substitution matrices
• Actual substitutions:– Based on empirical data– Commonly used by many bioinformatics
programs– PAM & BLOSUM
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Protein matrices – actual substitutions
The idea: Given an alignment of a large number of closely related sequences we can score the relation between amino acids based on how
frequently they substitute each other M G Y D EM G Y D EM G Y E EM G Y D EM G Y Q EM G Y D EM G Y E EM G Y E E
In the fourth columnE and D are found in 7 / 8
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PAM Matrix - Point Accepted Mutations
• The Dayhoff PAM matrix is based on a database of 1,572 changes in 71 groups of closely related proteins (85% identity => Alignment was easy and reliable).
• Counted the number of substitutions per amino-acid pair (20 x 20)
• Found that common substitutions occurred between chemically similar amino acids
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PAM Matrices
• Family of matrices PAM 80, PAM 120, PAM 250
• The number on the PAM matrix represents evolutionary distance
• Larger numbers are for larger distances
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Example: PAM 250
Similar amino acids have greater score
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PAM - limitations
• Based only on a single, and limited dataset
• Examines proteins with few differences (85% identity)
• Based mainly on small globular proteins so the matrix is biased
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BLOSUM
• Henikoff and Henikoff (1992) derived a set of matrices based on a much larger dataset
• BLOSUM observes significantly more replacements than PAM, even for infrequent pairs
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BLOSUM: Blocks Substitution Matrix
• Based on BLOCKS database – ~2000 blocks from 500 families of related
proteins– Families of proteins with identical function
• Blocks are short conserved patterns of 3-60 amino acids without gaps
AABCDA----BBCDADABCDA----BBCBBBBBCDA-AA-BCCAAAAACDA-A--CBCDBCCBADA---DBBDCCAAACAA----BBCCC
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BLOSUM
• Each block represents a sequence alignment with different identity percentage
• For each block the amino-acid substitution rates were calculated to create the BLOSUM matrix
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BLOSUM Matrices
• BLOSUMn is based on sequences that share at least n percent identity
• BLOSUM62 represents closer sequences than BLOSUM45
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Example : Blosum62
Derived from blocks where the sequencesshare at least 62% identity
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PAM vs. BLOSUM
More distant sequences
PAM100 = BLOSUM90
PAM120 = BLOSUM80
PAM160 = BLOSUM60
PAM200 = BLOSUM52
PAM250 = BLOSUM45
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Intermediate summary
1. Scoring system = substitution matrix + gap penalty.
2. Used for both global and local alignment
3. For amino acids, there are two types of substitution matrices: PAM and Blosum