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Lecture 6 Sequence Alignment. Bioinformatics. Dr. Aladdin HamwiehKhalid Al- shamaa Abdulqader Jighly. Aleppo University Faculty of technical engineering Department of Biotechnology. 2010-2011. Gene prediction: Methods. Gene Prediction can be based upon: Coding statistics

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Bioinformatics

Lecture 6

Sequence Alignment

Bioinformatics

Dr. Aladdin HamwiehKhalid Al-shamaa

Abdulqader Jighly

Aleppo University

Faculty of technical engineering

Department of Biotechnology

2010-2011


Gene prediction methods

Gene prediction: Methods

  • Gene Prediction can be based upon:

    • Coding statistics

    • Gene structure

    • Comparison

Statistical approach

Similarity-based approach


Gene prediction methods1

Gene prediction: Methods

  • Gene Prediction can be based upon:

    • Coding statistics

    • Gene structure

    • Comparison

Statistical approach

Similarity-based approach


Alignment

Alignment

  • Sequence alignment involves the identification of the correct location of deletions and insertions that have occurred in either of the two lineages since their divergence from a common ancestor.

  • Dynamic programming is the standard approach to sequence alignment

  • Global alignment: optimize the overall similarity of the two sequences

  • Local alignment: find only relatively conserved subsequences

  • Pairwise alignment: is the alignment between two sequences

  • Multiple alignment: is the alignment between more than two sequences


Methods of alignment

Methods of alignment:

  • Dot matrix

  • Distance Matrix


Dot plot algorithm

Dot Plot Algorithm

  • Take two sequences (A & B), write sequence A out as a row (length=m) and sequence B as a column (length =n)

  • Create a table or “matrix” of “m” columns and “n” rows

  • Compare each letter of sequence A with every letter in sequence B. If there’s a match mark it with a dot, if not, leave blank


Dot plot algorithm1

Dot Plot Algorithm

A C D E F G H G G

A

C

D

E

F

G

H

G

A

Complete identity

X

Not Matched


Dot plots internal repeats

Dot Plots & Internal Repeats


Bioinformatics

The vertical gap indicates that a coding region corresponding to ~75 amino acids has either been deleted from the human gene or inserted into the bacterial gene.

Advantages:

Highlighting Information


Bioinformatics

Advantages:

Highlighting Information

The two pairs of diagonally oriented parallel lines most probably indicate that two small internal duplications occurred in the bacterial gene.


Scoring matrices

Scoring Matrices

  • Scoring matrices are created based on biological evidence.

  • To generalize scoring, consider a (4+1) x (4+1) scoring matrixδ.

  • In the case of an amino acid sequence alignment, the scoring matrix would be a (20+1)x(20+1) size.

  • The addition of 1 is to include the score for comparison of a gap character “-”.


Scoring matrice elements

Scoring Matrice Elements

Input: two sequences over the same alphabet

Output: an alignment of the two sequences

Example:

  • GCGCATGGATTGAGCGAandTGCGCCATTGATGACCA

  • A possible alignment:

-GCGC-ATGGATTGAGCGA

TGCGCCATTGAT-GACC-A

Three elements:

  • Perfect matches

  • Mismatches

  • Insertions & deletions (indel)


Scoring scheme

scoring scheme

A G C T -

A +1 –1 –1 -1 -2

G –1 +1 –1 -1 -2

C –1 –1 +1 -1 -2

T –1 –1 –1 +1 -2

- -2 -2 -2 -2 *

Score each position independently:

  • Match: +1

  • Mismatch: -1

  • Indel: -2

    Score of an alignment is sum of position scores

Example:-GCGC-ATGGATTGAGCGA

TGCGCCATTGAT-GACC-A

Score: (+1x13) + (-1x2) + (-2x4) = 3

------GCGCATGGATTGAGCGA

TGCGCC----ATTGATGACCA--

Score:(+1x5) + (-1x6) + (-2x11)= -23


Transition and transversion

Transition and Transversion

  • Matrix Example:

A C G T

A +3 –2 –1 -2

C –2 +3 –2 -1

G –1 –2 +3 -2

T –2 –1 –2 +3


The global alignment problem

The Global Alignment Problem

Find the best alignment between two strings under a given scoring schema

Input : Strings v and w and a scoring schema

Output : Alignment of maximum score

↑← = -б

= 1 if match

= -µ if mismatch

si-1,j-1 +1 if vi = wj

si,j= max si-1,j-1 -µ if vi ≠ wj

si-1,j - σ

si,j-1 - σ

W

Wj-1Wj

m : mismatch penalty

σ : indelpenalty

V

ViVi-1

{


Longest common subsequences practice 1

Longest Common Subsequences – Practice 1

  • Mismatches are not allowed (μ = -∞)

  • No indels penalties (σ = 0)

  • and matches are rewarded with +1

  • V = ATCTGAT

  • W = TGCAT


Longest common subsequences practice 2

Longest Common Subsequences – Practice 2


Longest common subsequences practice 3

Longest Common Subsequences – Practice 3


Longest common subsequences practice 4

Longest Common Subsequences – Practice 4


Longest common subsequences practice 5

Longest Common Subsequences – Practice 5


Longest common subsequences practice 6

Longest Common Subsequences – Practice 6


Longest common subsequences practice 7

Longest Common Subsequences – Practice 7


Longest common subsequences practice 8

Longest Common Subsequences – Practice 8


Longest common subsequences practice 9

Longest Common Subsequences – Practice 9


Longest common subsequences practice 10

Longest Common Subsequences – Practice 10

  • Computing similarity s(V,W) = 4

  • Computing distance d(V,W) = n + m – 2 s(V,M) = 5


Longest common subsequences practice 101

Longest Common Subsequences – Practice 10

  • Alignment:– T G C A T – A –A T – C – T G A T


Protein substitution matrix

Protein Substitution Matrix

Identity Scoring Matrix

Percent Accepted Mutation (PAM)

Blocks Substitution Matrix (BLOSUM)


Identity scoring matrix

Identity Scoring Matrix


Percent accepted mutation pam

Percent Accepted Mutation (PAM)

  • 1 PAM is the amount of evolutionary change that yields, on average, one substitution in 100 amino acid residues.

  • PAM250 matrix assumes/is optimized for sequences separated by 250 PAM, i.e. 250 substitutions in 100 amino acids (longer evolutionary time)

  • To derive a mutational probability matrix for a protein sequence that has undergone N percent accepted mutations, a PAM-N matrix, the PAM-1 matrix is multiplied by itself N times

  • PAM250 is suitable for comparing distantly related sequences, while a lower PAM is suitable for comparing more closely related sequences.


Selecting a pam matrix

Selecting a PAM Matrix

  • Low PAM numbers: short sequences, strong local similarities.

  • High PAM numbers: long sequences, weak similarities.

    • PAM60 for close relations (60% identity)

    • PAM120 recommended for general use (40% identity)

    • PAM250 for distant relations (20% identity)

  • If uncertain, try several different matrices

    • PAM40, PAM120, PAM250 recommended.


A better matrix pam250

A Better Matrix - PAM250


Blosum blo cks su bstitution m atrix

BLOSUM:BlocksSubstitutionMatrix

  • 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 acid long without gaps

  • Each block represent sequences alignment with different identity percentage

AABCDA … BBCDA

DABCDA. A. BBCBB

BBBCDABA.BCCAA

AAACDAC.DCBCDB

CCBADAB.DBBDCC

AAACAA … BBCCC


Blosum matrices

BLOSUM Matrices

  • For each block the amino-acid substitution rates were calculated to create BLOSUM matrix

  • Different BLOSUMn matrices are calculated independently from BLOCKS

  • BLOSUMn is based on sequences that shared at least n percent identical

  • BLOSUM62 represents closer sequences than BLOSUM45


Selecting a blosum matrix

Selecting a BLOSUM Matrix

  • For BLOSUMn, higher n suitable for sequences which are more similar

    • BLOSUM62 recommended for general use

    • BLOSUM80 for close relations

    • BLOSUM45 for distant relations


Bioinformatics

  • Equivalent PAM and Blosum matricesThe following matrices are roughly equivalent...

  • PAM100 Blosum90

  • PAM120 Blosum80

  • PAM160 Blosum60

  • PAM200 Blosum52

  • PAM250 Blosum45Generally speaking...

  • The Blosum matrices are best for detecting local alignments.

  • The Blosum62 matrix is the best for detecting the majority of weak protein similarities.

  • The Blosum45 matrix is the best for detecting long and weak alignments.

Less divergent

More divergent


Blosum62

Common amino acids have low weights

Rare amino acids have high weights

BLOSUM62

A4

R -1 5

N -2 0 6

D -2 -2 1 6

C 0 -3 -3 -3 9

Q -1 1 0 0 -3 5

E -1 0 0 2 -4 2 5

G 0 -2 0 -1 -3 -2 -2 6

H -2 0 1 -1 -3 0 0 -2 8

I -1 -3 -3 -3 -1 -3 -3 -4 -3 4

L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4

K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5

M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5

F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6

P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7

S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5

W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11

Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7

V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4

X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1

A R N D C Q E G H I L K M F P S T W Y V X


Blosum621

BLOSUM62

A 4

R -1 5

N -2 0 6

D -2 -2 1 6

C 0 -3 -3 -3 9

Q -1 1 0 0 -3 5

E -1 0 0 2 -4 2 5

G 0 -2 0 -1 -3 -2 -2 6

H -2 0 1 -1 -3 0 0 -2 8

I -1 -3 -3 -3 -1 -3 -3 -4 -3 4

L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4

K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5

M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5

F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6

P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7

S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5

W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11

Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7

V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4

X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1

A R N D C Q E G H I L K M F P S T W Y V X

Positive for more likely substitution


Blosum622

BLOSUM62

A 4

R -1 5

N -2 0 6

D -2 -2 1 6

C 0 -3 -3 -3 9

Q -1 1 0 0 -3 5

E -1 0 0 2 -4 2 5

G 0 -2 0 -1 -3 -2 -2 6

H -2 0 1 -1 -3 0 0 -2 8

I -1 -3 -3 -3 -1 -3 -3 -4 -3 4

L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4

K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5

M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5

F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6

P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7

S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5

W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11

Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7

V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4

X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1

A R N D C Q E G H I L K M F P S T W Y V X

Negative for less likely substitution


A lignment s core

alignment score

A4

R -1 5

N -2 0 6

D -2 -2 1 6

C 0 -3 -3 -3 9

Q -1 1 0 0 -3 5

E -1 0 0 2 -4 2 5

G 0 -2 0 -1 -3 -2 -2 6

H -2 0 1 -1 -3 0 0 -2 8

I -1 -3 -3 -3 -1 -3 -3 -4 -3 4

L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4

K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5

M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5

F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6

P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7

S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4

T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5

W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11

Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7

V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4

X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1

A R N D C Q E G H I L K M F P S T W Y V X

…PQG…

…PQG…

7+5+6

=18

..PQG..

..PEG..

7+2+6

=15

…PQG…

…PQA…

7+5+0

=12


Affine gap penalties

This is more likely

This is less likely

Affine Gap Penalties

  • In nature, a series of k indels often come as a single event rather than a series of k single nucleotide events:

ATA__GC

ATATTGC

ATAG_GC

AT_GTGC

Normal scoring would give the same score for both alignments


Accounting for gaps

Accounting for Gaps

  • Gaps- contiguous sequence of spaces in one of the rows

  • Score for a gap of length x is:

    -(ρ +σx)

    where ρ >0 is the penalty for introducing a gap:

    gap opening penalty

    ρ will be large relative to σ:

    gap extension penalty

    because you do not want to add too much of a penalty for extending the gap.


Multiple sequence alignment

Multiple Sequence Alignment

  • All sequences are compared to each other (pairwise alignments)

  • A dendrogram (like a phylogenetic tree) is constructed, describing the approximate groupings of the sequences by similarity (stored in a file).

  • The final multiple alignment is carried out, using the dendrogram as a guide.


Bioinformatics

Applications of multiple alignments


Thank you

Thank you


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