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Sequence Alignments.

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Presentation on theme: "Sequence Alignments."— Presentation transcript:

1 Sequence Alignments

2 DNA Replication Prior to cell division, all the genetic instructions must be “copied” so that each new cell will have a complete set DNA polymerase is the enzyme that copies DNA Reads the old strand in the 3´ to 5´ direction

3 Over time, genes accumulate mutations
Environmental factors Radiation Oxidation Mistakes in replication or repair Deletions, Duplications Insertions Inversions Point mutations

4 Deletions Codon deletion: ACG ATA GCG TAT GTA TAG CCG…
Effect depends on the protein, position, etc. Almost always deleterious Sometimes lethal Frame shift mutation: ACG ATA GCG TAT GTA TAG CCG… ACG ATA GCG ATG TAT AGC CG?… Almost always lethal

5 Why align sequences? The draft human genome is available
Automated gene finding is possible Gene: AGTACGTATCGTATAGCGTAA What does it do? One approach: Is there a similar gene in another species? Align sequences with known genes Find the gene with the “best” match

6 Are there other sequences like this one?
1) Huge public databases - GenBank, Swissprot, etc. 2) Sequence comparison is the most powerful and reliable method to determine evolutionary relationships between genes 3) Similarity searching is based on alignment 4) BLAST and FASTA provide rapid similarity searching a. rapid = approximate (heuristic) b. false + and - scores

7 Similarity ≠ Homology 1) 25% similarity ≥ 100 AAs is strong evidence for homology 2) Homology is an evolutionary statement which means “descent from a common ancestor” common 3D structure usually common function homology is all or nothing, you cannot say "50% homologous"

8 Global vs Local similarity
1) Global similarity uses complete aligned sequences - total % matches GCG GAP program, Needleman & Wunch algorithm 2) Local similarity looks for best internal matching region between 2 sequences GCG BESTFIT program, Smith-Waterman algorithm, BLAST and FASTA 3) dynamic programming optimal computer solution, not approximate

9 Search with Protein, not DNA Sequences
1) 4 DNA bases vs. 20 amino acids - less chance similarity 2) can have varying degrees of similarity between different AAs - # of mutations, chemical similarity, PAM matrix 3) protein databanks are much smaller than DNA databanks

10 Similarity is Based on Dot Plots
1) two sequences on vertical and horizontal axes of graph 2) put dots wherever there is a match 3) diagonal line is region of identity (local alignment) 4) apply a window filter - look at a group of bases, must meet % identity to get a dot

11 Simple Dot Plot

12 Dot plot filtered with 4 base window and 75% identity

13 Dot plot of real data

14 Indels Comparing two genes it is generally impossible to tell if an indel is an insertion in one gene, or a deletion in another, unless ancestry is known: ACGTCTGATACGCCGTATCGTCTATCT ACGTCTGAT---CCGTATCGTCTATCT

15 The Genetic Code Substitutions are mutations accepted by natural selection. Synonymous: CGC  CGA Non-synonymous: GAU  GAA

16 Comparing two sequences

17 Scoring a sequence alignment
Match score: +1 Mismatch score: +0 Gap penalty: –1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Mismatches: 2 × 0 Gaps: 7 × (– 1) Score = +11

18 Origination and length penalties
We want to find alignments that are evolutionarily likely. Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGAT ATAGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT AC-T-TGA--CG-CGT-TA-TCTATCT We can achieve this by penalizing more for a new gap, than for extending an existing gap

19 Scoring a sequence alignment (2)
Match/mismatch score: +1/+0 Origination/length penalty: –2/–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Mismatches: 2 × 0 Origination: 2 × (–2) Length: 7 × (–1) Score = +7

20 Scoring Similarity 1) Can only score aligned sequences
2) DNA is usually scored as identical or not 3) modified scoring for gaps - single vs. multiple base gaps (gap extension) 4) AAs have varying degrees of similarity a. # of mutations to convert one to another b. chemical similarity c. observed mutation frequencies 5) PAM matrix calculated from observed mutations in protein families

21 DNA Scoring Matrix A T C G 1 A T C G 5 -4 A T C G 1 -5 -1 Identity
A T C G 5 -4 A T C G 1 -5 -1 Identity BLAST Transition/Transversion

22 The PAM 250 scoring matrix

23 How can we find an optimal alignment?
Finding the alignment is computationally hard: ACGTCTGATACGCCGTATAGTCTATCT CTGAT---TCG—CATCGTC--T-ATCT C(27,7) gap positions = ~888,000 possibilities It’s possible, as long as we don’t repeat our work! Dynamic programming: The Needleman & Wunsch algorithm

24 What is the optimal alignment?
ACTCG ACAGTAG Match: +1 Mismatch: 0 Gap: –1

25 The dynamic programming concept
Suppose we are aligning: ACTCG ACAGTAG Last position choices: G +1 ACTC G ACAGTA G -1 ACTC - ACAGTAG ACTCG G ACAGTA

26 We can use a table Suppose we are aligning: A with A… A -1 A -1

27 Needleman-Wunsch: Step 1
Each sequence along one axis Mismatch penalty multiples in first row/column 0 in [1,1]

28 Needleman-Wunsch: Step 2
Vertical/Horiz. move: Score + (simple) gap penalty Diagonal move: Score + match/mismatch score Take the MAX of the three possibilities

29 Needleman-Wunsch: Step 2 (cont’d)
Fill out the rest of the table likewise…

30 Needleman-Wunsch: Step 2 (cont’d)
Fill out the rest of the table likewise… The optimal alignment score is calculated in the lower-right corner

31 But what is the optimal alignment
To reconstruct the optimal alignment, we must determine of where the MAX at each step came from…

32 A path corresponds to an alignment
= GAP in top sequence = GAP in left sequence = ALIGN both positions One path from the previous table: Corresponding alignment (start at the end): AC--TCG ACAGTAG Score = +2

33 Semi-global alignment
Suppose we are aligning: GCG GGCG Which do you prefer? G-CG -GCG GGCG GGCG Semi-global alignment allows gaps at the ends for free.

34 Semi-global alignment
Semi-global alignment allows gaps at the ends for free. Initialize first row and column to all 0’s Allow free horizontal/vertical moves in last row and column

35 Local alignment Global alignments – score the entire alignment
Semi-global alignments – allow unscored gaps at the beginning or end of either sequence Local alignment – find the best matching subsequence CGATG AAATGGA This is achieved by allowing a 4th alternative at each position in the table: zero, if alternative neg. Smith-Waterman Algorithm (1981).

36 Local alignment Mismatch = –1 this time CGATG AAATGGA

37 Practice Problem Find an optimal alignment for these two sequences: GCGGTT GCGT Match: +1 Mismatch: 0 Gap: –1

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