Sequence Alignments

Sequence AlignmentsSequence Alignments

Introduction to BioinformaticsIntroduction to Bioinformatics

Intro to Bioinformatics – Sequence Alignment 2

Sequence AlignmentsSequence Alignments Cornerstone of bioinformatics What is a sequence?

• Nucleotide sequence• Amino acid sequence

Pairwise and multiple sequence alignments• We will focus on pairwise alignments

What alignments can help• Determine function of a newly discovered gene sequence• Determine evolutionary relationships among genes, proteins,

and species• Predicting structure and function of protein

Acknowledgement: This notes is adapted from lecture notes of both Wright State University’s Bioinformatics Program and Professor Laurie Heyer of Davidson College with permission.


DNA ReplicationDNA 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


Over time, genes accumulate Over time, genes accumulate mutationsmutations Environmental factors

• Radiation• Oxidation

Mistakes in replication or repair Deletions, Duplications Insertions, Inversions Translocations Point mutations


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

DeletionsDeletions


IndelsIndels 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:

ACGTCTGATACGCCGTATCGTCTATCTACGTCTGAT---CCGTATCGTCTATCT


The Genetic CodeThe Genetic Code

SubstitutionsSubstitutions are mutations accepted by natural selection.

Synonymous: CGC CGA

Non-synonymous: GAU GAA


Comparing Two SequencesComparing Two Sequences Point mutations, easy:ACGTCTGATACGCCGTATAGTCTATCTACGTCTGATTCGCCCTATCGTCTATCT

Indels are difficult, must align sequences:ACGTCTGATACGCCGTATAGTCTATCTCTGATTCGCATCGTCTATCT

ACGTCTGATACGCCGTATAGTCTATCT----CTGATTCGC---ATCGTCTATCT


Why Align Sequences?Why Align Sequences? The draft human genome is available Automated gene finding is possible Gene: AGTACGTATCGTATAGCGTAA

• What does it do?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

Examples


Gaps or No GapsGaps or No Gaps


Scoring a Sequence AlignmentScoring a Sequence AlignmentGivenMatch score: +1Mismatch score: +0Gap penalty: –1SequencesACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || ||||||||----CTGATTCGC---ATCGTCTATCTMatches: 18 × (+1)Mismatches: 2 × 0Gaps: 7 × (– 1)

Score = +11Score = +11


Origination and Length PenaltiesOrigination and Length Penalties Note that a bioinformatics computational model or

algorithm must be “biologically meaningful” or even “biologically significant”

We want to find alignments that are evolutionarily likely.

Which of the following alignments seems more likely to you?ACGTCTGATACGCCGTATAGTCTATCTACGTCTGAT-------ATAGTCTATCTACGTCTGATACGCCGTATAGTCTATCTAC-T-TGA--CG-CGT-TA-TCTATCT

We can achieve this by penalizing more for a new gap, than for extending an existing gap


Scoring a Sequence Alignment (2)Scoring a Sequence Alignment (2)Given Match/mismatch score: +1/+0Gap origination/length penalty: –2/–1Sequences ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || ||||||||----CTGATTCGC---ATCGTCTATCTMatches: 18 × (+1)Mismatches: 2 × 0Origination: 2 × (–2)Length: 7 × (–1)

Caution: Sometime “gap extension” used instead of “gap length” …

Score = +7Score = +7


How can we find an optimal alignment?How can we find an optimal alignment? Finding the alignment is computationally hard:ACGTCTGATACGCCGTATAGTCTATCTCTGAT---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


Dynamic ProgrammingDynamic Programming Technique of solving optimization problems

• Find and memorize solutions for subproblems• Use those solutions to build solutions for larger

subproblems• Continue until the final solution is found

Recursive computation of cost function in a non-recursive fashion


Dynamic ProgrammingDynamic Programming Example

• Solving Fibonacci number f(n) = f(n-1) + f(n-2) recursively takes exponential time

Because many numbers are recalculated • Solving it using dynamic programming only takes a

linear time Use an array f[] to store the numbers

f[0] = 1;f[1] = 1;for (i = 2; i <= n; i++) f[i] = f[i – 1] + f[i – 2];


Global Sequence AlignmentGlobal Sequence Alignment Needleman-Wunsch algorithm

Suppose we are aligning:a with a …

a0 -1

a -1

Di,j = max{ Di-1,j + d(Ai, –), Di,j-1 + d(–, Bj), Di-1,j-1 + d(Ai, Bj) }

i-1

j-1 j

i

Seq1

Seq2

A

B


Dynamic Programming (DP) ConceptDynamic Programming (DP) Concept Suppose we are aligning:

CACGA CGA


DP – Recursion PerspectiveDP – Recursion Perspective Suppose we are aligning:ACTCGACAGTAG

Last position choices:G +1 ACTCG ACAGTA

G -1 ACTC- ACAGTAG

- -1 ACTCGG ACAGTA


What is the optimal alignment?What is the optimal alignment? ACTCGACAGTAG

Match: +1 Mismatch: 0 Gap: –1


Needleman-Wunsch: Step 1Needleman-Wunsch: Step 1 Each sequence along one axis Gap penalty multiples in first row/column 0 in [1,1] (or [0,0] for the CS-minded)

A C T C G0 -1 -2 -3 -4 -5

A -1 1C -2A -3G -4T -5A -6G -7


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

A C T C G0 -1 -2 -3 -4 -5

A -1 1C -2A -3G -4T -5A -6G -7


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

a c t c g0 -1 -2 -3 -4 -5

a -1 1 0 -1 -2 -3c -2a -3g -4t -5a -6g -7


Needleman-Wunsch: Step 2 (cont’d)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

a c t c g0 -1 -2 -3 -4 -5

a -1 1 0 -1 -2 -3c -2 0 2 1 0 -1a -3 -1 1 2 1 0g -4 -2 0 1 2 2t -5 -3 -1 1 1 2a -6 -4 -2 0 1 1g -7 -5 -3 -1 0 2


a c t c g0 -1 -2 -3 -4 -5

a -1 1 0 -1 -2 -3c -2 0 2 1 0 -1a -3 -1 1 2 1 0g -4 -2 0 1 2 2t -5 -3 -1 1 1 2a -6 -4 -2 0 1 1g -7 -5 -3 -1 0 2

But what But what isis the optimal alignment the optimal alignment To reconstruct the optimal alignment, we must

determine of where the MAX at each step came from…


A path corresponds to an alignmentA 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--TCGACAGTAG

Score = +2


Algorithm AnalysisAlgorithm Analysis Brute force approach

• If the length of both sequences is n, number of possibility = C(2n, n) = (2n)!/(n!)2 22n / (n)1/2, using Sterling’s approximation of n! = (2n)1/2e-nnn.

• O(4n) Dynamic programming

• O(mn), where the two sequence sizes are m and n, respectively

• O(n2), if m is in the order of n


Practice ProblemPractice Problem Find an optimal alignment for these two

sequences:GCGGTTGCGT

Match: +1 Mismatch: 0 Gap: –1

g c g g t t0 -1 -2 -3 -4 -5 -6

g -1c -2g -3t -4


Practice ProblemPractice Problem Find an optimal alignment for these two

sequences:GCGGTTGCGT g c g g t t

0 -1 -2 -3 -4 -5 -6g -1 1 0 -1 -2 -3 -4c -2 0 2 1 0 -1 -2g -3 -1 1 3 2 1 0t -4 -2 0 2 3 3 2

GCGGTTGCG-T- Score = +2


g c g0 -1 -2 -3

g -1 1 0 -1g -2 0 1 1c -3 -1 1 1g -4 -2 0 2

Semi-global alignmentSemi-global alignment Suppose we are aligning:GCGGGCG

Which do you prefer?G-CG -GCGGGCG GGCG

Semi-global alignment allows gaps at the ends for free.• Terminal gaps are usually the result of incomplete

data acquisition no biologically significant


Semi-global alignmentSemi-global alignment

g c g0 0 0 0

g 0 1 0 1g 0 1 1 1c 0 0 2 1g 0 1 1 3

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


Local alignmentLocal 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 CGATGAAATGGA

This is achieved by allowing a 4th alternative at each position in the table: zero.


Local Sequence Alignment Local Sequence Alignment Why local sequence alignment?

• Subsequence comparison between a DNA sequence and a genome

• Protein function domains• Exons matching

Smith-Waterman algorithm

Di,j = max{ Di-1,j + d(Ai, –), Di,j-1 + d(–,Bj), Di-1,j-1 + d(Ai,Bj), 0 }

Initialization: D1,j = , Di,1 =


Local alignmentLocal alignment Score: Match = 1, Mismatch = -1, Gap = -1

CGATGAAATGGA

c g a t g0 0 0 0 0 0

a 0 0 0 1 0 0a 0 0 0 1 0 0a 0 0 0 1 0 0t 0 0 0 0 2 1g 0 0 1 0 1 3g 0 0 1 0 0 2a 0 0 0 2 1 1


Local alignmentLocal alignment Another example


More ExampleMore Example Align

ATGGCCTCACGGCTCMismatch = -3Gap = -4

-- A C G G C T C0 -4 -8 -12 -16 -20 -24 -28

1 -3-3

--ATGGCCTC

-4-8-12-16-20-24-28-32

-7 -11 -15 -19 -23-2 -6 -10 -14 -14 -18

-7 -6 -1 -5 -9 -13 -17-11 -10 -5 0 -4 -8 -12-15 -10 -9 -4 1 -3 -7-19 -14 -13 -8 -3 -2 -2-23 -18 -17 -12 -7 -2 -5-27 -22 -21 -16 -11 -6 -1

GlobalAlignment:

ATGGCCTCACGGC-TC


More ExampleMore Example Local

Alignment:

ATGGCCTCACGG CTC

or

ATGGCCTCACGGC TC

-- A C G G C T C0 0 0 0 0 0 0 0

1 00

--ATGGCCTC

00000000

0 0 0 0 00 0 0 0 1 0

0 0 1 1 0 0 00 0 1 2 0 0 00 1 0 0 3 0 10 1 0 0 1 0 10 0 0 0 0 2 00 1 0 0 1 0 3


Scoring Matrices for DNA SequencesScoring Matrices for DNA Sequences Transition: A G C T Transversion: a purine (A or G) is replaced by a

pyrimadine (C or T) or vice versa


Scoring Matrices for Protein Sequence Scoring Matrices for Protein Sequence PAM 250


Scoring Matrices for Protein Sequence Scoring Matrices for Protein Sequence PAM (Percent Accepted Mutations)

1 PAM unit can be thought of as the amount of time in which an “average” protein mutates 1 out of every 100 amino acids.

Perform multiple sequence alignment on a “family” of proteins that are at least 85% similar. Find the frequency of amino acid i and j are aligned to each other and normalize it.

Entry mij in PAM1 represents the probability of amino acid i substituted by amino acid j in 1 PAM unit PAM 2 = PAM 1 × PAM 1 PAM n = (PAM 1)n, e.g., PAM 250 = (PAM 1)250

Questions Why are values in a PAM matrix integers? Shouldn’t a probability be

between 0 and 1? Why is PAM250 possible? Shouldn’t a probability be less than or equal

to 100%?


Scoring Matrices for Protein Sequence Scoring Matrices for Protein Sequence BLOSUM (BLOcks SUbstitution Matrix) 62


Using Protein Scoring MatricesUsing Protein Scoring Matrices Divergence

BLOSUM 80 BLOSUM 62 BLOSUM 45PAM 1 PAM 120 PAM 250Closely related Distantly relatedLess divergent More divergentLess sensitive More sensitive

Looking for• Short similar sequences → use less sensitive matrix• Long dissimilar sequences → use more sensitive matrix• Unknown → use range of matrices

Comparison• PAM – designed to track evolutionary origin of proteins• BLOSUM – designed to find conserved regions of proteins

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Multiple Sequence AlignmentMultiple Sequence Alignment Why multiple sequence alignment (MSA)

• Two sequences might not look very similar.• But, some “similarity” emerges with more sequences,

however. Is dynamic programming still applicable? CLUSTALW

• One of most popular tools for MSA • Heuristic-based approach• Basic

1) Calculate the distance matrix based on pairwise alignments2) Construct a guide tree

NJ (unrooted tree) Mid-point (rooted tree)3) Progressive alignment using the guide tree

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Sequence Alignments