Presentation on theme: "1 Lesson 2 Aligning sequences and searching databases."— Presentation transcript:
1 Lesson 2 Aligning sequences and searching databases
2 Homology and sequence alignment.
Homology Homology = Similarity between objects due to a common ancestry Hund = Dog, Schwein = Pig
4 Sequence homology VLSPAVKWAKVGAHAAGHG ||| || |||| | |||| VLSEAVLWAKVEADVAGHG Similarity between sequences as a result of common ancestry.
5 Sequence alignment Alignment: Comparing two (pairwise) or more (multiple) sequences. Searching for a series of identical or similar characters in the sequences.
6 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.
7 Insertions, deletions, and substitutions
8 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
9 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).
10 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).
11 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
12 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
13 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?
14 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
15 Global and Local pairwise alignments
16 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
17 Global alignment PTK2 protein tyrosine kinase 2 of human and rhesus monkey
18 Proteins are comprised of domains Domain B Protein tyrosine kinase domain Domain A Human PTK2 :
19 Protein tyrosine kinase domain In leukocytes, a different gene for tyrosine kinase is expressed. Domain X Protein tyrosine kinase domain Domain A
20 Domain X Protein tyrosine kinase domain Domain B Protein tyrosine kinase domain Domain A Leukocyte TK PTK2 The sequence similarity is restricted to a single domain
21 Global alignment of PTK and LTK
22 Local alignment of PTK and LTK
23 Conclusions Use global alignment when the two sequences share the same overall sequence arrangement. Use local alignment to detect regions of similarity.
24 How alignments are computed
25 Pairwise alignment AAGCTGAATTCGAA AGGCTCATTTCTGA AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- One possible alignment:
27 Choosing an alignment for a pair of sequences AAGCTGAATTCGAA AGGCTCATTTCTGA AAGCTGAATT-C-GAA AGGCT-CATTTCTGA- A-AGCTGAATTC--GAA AG-GCTCA-TTTCTGA- Which alignment is better? Many different alignments are possible for 2 sequences:
29 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 scores Can be positive or negative
30 Scoring systems
31 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…
32 Scoring matrix TCGA 2A 2-6G 2 C 2 T 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
33 DNA scoring matrices Uniform substitutions between all nucleotides: TCGAFrom To 2A 2-6G 2 C 2 T Match Mismatch
34 DNA scoring matrices Can take into account biological phenomena such as: Transition-transversion
35 Amino-acid scoring matrices Take into account physico-chemical properties
36 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
37 Scoring gaps (II) The dependency between the penalty and the length of the gap need not to be linear. AGGGTTC—GA AGGGTTCTGA Score = -2 AGGGTT-—GA AGGGTTCTGA Score = -4 AGGGT--—GA AGGGTTCTGA Score = -6 AGGG---—GA AGGGTTCTGA Score = -8 Linear penalty
38 Scoring gaps (II) The dependency between the penalty and the length of the gap need not to be linear. AGGGTTC—GA AGGGTTCTGA Score = -4 AGGGTT-—GA AGGGTTCTGA Score = -6 AGGGT--—GA AGGGTTCTGA Score = -7 AGGG---—GA AGGGTTCTGA Score = -8 Non-linear penalty
39 PAM AND BLOSUM
40 Amino-acid substitution matrices Actual substitutions: –Based on empirical data –Commonly used by many bioinformatics programs –PAM & BLOSUM
41 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 E M G Y E E M G Y D E M G Y Q E M G Y D E M G Y E E In the fourth column E and D are found in 7 / 8
42 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
43 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
44 Example: PAM 250 Similar amino acids have greater score
45 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
46 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
47 BLOSUM: Blo cks Su bstitution M atrix 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----BBCDA DABCDA----BBCBB BBBCDA-AA-BCCAA AAACDA-A--CBCDB CCBADA---DBBDCC AAACAA----BBCCC
48 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
49 BLOSUM Matrices BLOSUMn is based on sequences that share at least n percent identity BLOSUM62 represents closer sequences than BLOSUM45
50 Example : Blosum62 Derived from blocks where the sequences share at least 62% identity
52 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