Computational Biology, Part 3Sequence Alignment

Computational Biology, Part 3Sequence Alignment

Robert F. MurphyRobert F. Murphy

Copyright Copyright 1996, 1999-2009. 1996, 1999-2009.

All rights reserved.All rights reserved.

Sequence AlignmentSequence Alignment

Definition: Procedure for comparing two or Definition: Procedure for comparing two or more sequences by searching for a series of more sequences by searching for a series of individual characters or character patterns individual characters or character patterns that are that are in the same orderin the same order in the sequences in the sequences Pair-wise alignmentPair-wise alignment: compare two sequences: compare two sequences Multiple sequence alignmentMultiple sequence alignment: compare more : compare more

than two sequencesthan two sequences

Example sequence alignmentExample sequence alignment

Task: align Task: align “abcdef”“abcdef” with with “abdgf”“abdgf” Write second sequence below the firstWrite second sequence below the first

abcdefabcdefabdgfabdgf

Move sequences to give maximum match between Move sequences to give maximum match between themthem

Show characters that match using vertical barShow characters that match using vertical bar

Example sequence alignmentExample sequence alignment

abcdefabcdef

||||

abdgfabdgf Insert gap between Insert gap between bb and and dd on lower on lower

sequence to allow sequence to allow dd and and ff to align to align

Example sequence alignmentExample sequence alignment

abcdefabcdef

|| | ||| | |

ab-dgfab-dgf

Example sequence alignmentExample sequence alignment

abcdefabcdef

|| | ||| | |

ab-dgfab-dgf Note Note ee and and gg don’t match don’t match

Matching Similarity vs. IdentityMatching Similarity vs. Identity

Alignments can be based on finding only Alignments can be based on finding only identical characters, or (more commonly) identical characters, or (more commonly) can be based on finding can be based on finding similarsimilar characters characters

More on how to define More on how to define similaritysimilarity later later

Global vs. Local AlignmentGlobal vs. Local Alignment

We distinguishWe distinguish GlobalGlobal alignment algorithms which optimize alignment algorithms which optimize

overall overall alignment between two sequences alignment between two sequences LocalLocal alignment algorithms which seek only alignment algorithms which seek only

relatively relatively conservedconserved pieces of sequence pieces of sequence Alignment stops at the ends of regions of strong Alignment stops at the ends of regions of strong

similaritysimilarity Favors finding conserved patterns in otherwise Favors finding conserved patterns in otherwise

different pairs of sequencesdifferent pairs of sequences

Global vs. Local AlignmentGlobal vs. Local Alignment

GlobalGlobal

LGPSSKQTGKGS-SRIWDNLGPSSKQTGKGS-SRIWDN| | ||| | | | | ||| | | LN-ITKSAGKGAIMRLGDALN-ITKSAGKGAIMRLGDA

LocalLocal

--------GKG----------------GKG-------- ||| ||| --------GKG----------------GKG--------

Global vs. Local AlignmentGlobal vs. Local Alignment

GlobalGlobal

LGPSSKQTGKGS-SRIWDNLGPSSKQTGKGS-SRIWDN| | ||| | | | | ||| | | LN-ITKSAGKGAIMRLGDALN-ITKSAGKGAIMRLGDA

LocalLocal

-------TGKG---------------TGKG-------- ||| ||| -------AGKG---------------AGKG--------

Why do sequence alignments?Why do sequence alignments?

To find whether two (or more) genes or To find whether two (or more) genes or proteins are evolutionarily related to each proteins are evolutionarily related to each otherother

To find structurally or functionally similar To find structurally or functionally similar regions within proteinsregions within proteins

Origin of similar genesOrigin of similar genes

Similar genes arise by Similar genes arise by gene gene duplicationduplication

Copy of a gene inserted next Copy of a gene inserted next to the originalto the original

Two copies mutate Two copies mutate independentlyindependently

Each can take on separate Each can take on separate functionsfunctions

All or part can be transferred All or part can be transferred from one part of genome to from one part of genome to anotheranother

http://fig.cox.miami.edu/~cmallery/150/gene/c7.19.19.gene.family.jpg

Methods for Pairwise AlignmentMethods for Pairwise Alignment

Dot matrix analysisDot matrix analysis Dynamic ProgrammingDynamic Programming Word or Word or k-k-tuple methods (FASTA and tuple methods (FASTA and

BLAST)BLAST)

Sequence comparison with dot matricesSequence comparison with dot matrices Goal: Goal: Graphically display regions of Graphically display regions of

similarity between two sequences (e.g., similarity between two sequences (e.g., domains in common between two proteins domains in common between two proteins of suspected similar function)of suspected similar function)

Sequence comparison with dot matricesSequence comparison with dot matrices Basic Method: Basic Method: For two sequences of For two sequences of

lengths M and N, lay out an M by N grid lengths M and N, lay out an M by N grid (matrix) with one sequence across the top (matrix) with one sequence across the top and one sequence down the left side. For and one sequence down the left side. For each position in the grid, compare the each position in the grid, compare the sequence elements at the top (column) and sequence elements at the top (column) and to the left (row). If and only if they are the to the left (row). If and only if they are the same, place a dot at that position.same, place a dot at that position.

Examples for protein sequencesExamples for protein sequences (Demonstration A6, Sequence 1 vs. 2)(Demonstration A6, Sequence 1 vs. 2)

abcdaefghbijklcmnopdabcdaefghbijklcmnopd

abcdaefghbijklcmnopdabcdaefghbijklcmnopd

Interpretation of dot matricesInterpretation of dot matrices

Regions of similarity appear as diagonal Regions of similarity appear as diagonal runs of dotsruns of dots

Reverse diagonals (perpendicular to Reverse diagonals (perpendicular to diagonal) indicate inversionsdiagonal) indicate inversions

Reverse diagonals crossing diagonals (Xs) Reverse diagonals crossing diagonals (Xs) indicate palindromesindicate palindromes

Examples for protein sequencesExamples for protein sequences (Demonstration A6, Sequence 4 vs. 4)(Demonstration A6, Sequence 4 vs. 4)

abcdeedcbafghijklmno abcdeedcbafghijklmno abcdeedcbafghijklmnoabcdeedcbafghijklmno

Interpretation of dot matricesInterpretation of dot matrices

Can link or "join" separate diagonals to Can link or "join" separate diagonals to form form alignmentalignment with "gaps" with "gaps" Each a.a. or base can only be used onceEach a.a. or base can only be used once

Can't trace vertically or horizontallyCan't trace vertically or horizontally Can't double backCan't double back

A gap is introduced by each vertical or A gap is introduced by each vertical or horizontal skiphorizontal skip

Examples for protein sequencesExamples for protein sequences (Demonstration A6, Sequence 2 vs. 3)(Demonstration A6, Sequence 2 vs. 3)

abcdaefghbijklcmnopdabcdaefghbijklcmnopd

abcdefghijklmnopqrstabcdefghijklmnopqrst

Uses for dot matricesUses for dot matrices

Can use dot matrices to align two proteins Can use dot matrices to align two proteins or two nucleic acid sequencesor two nucleic acid sequences

Can use to find amino acid repeats within a Can use to find amino acid repeats within a protein by comparing a protein sequence to protein by comparing a protein sequence to itselfitself Repeats appear as a set of diagonal runs stacked Repeats appear as a set of diagonal runs stacked

vertically and/or horizontallyvertically and/or horizontally

Examples for protein sequencesExamples for protein sequences (Demonstration A6, Sequence 5 vs. 5)(Demonstration A6, Sequence 5 vs. 5)

abcdabcdabcdabcdabcdabcdabcdabcdabcdabcd

abcdabcdabcdabcdabcdabcdabcdabcdabcdabcd

Uses for dot matricesUses for dot matrices

Can use to find self base-pairing of an RNA Can use to find self base-pairing of an RNA (e.g., tRNA) by comparing a sequence to (e.g., tRNA) by comparing a sequence to itself complemented and reverseditself complemented and reversed

Excellent approach for finding sequence Excellent approach for finding sequence transpositionstranspositions

Filtering to remove “noise”Filtering to remove “noise”

A problem with dot matrices for long A problem with dot matrices for long sequences is that they can be very noisy due sequences is that they can be very noisy due to lots of insignificant matches (i.e., one A)to lots of insignificant matches (i.e., one A)

Solution use a window and a thresholdSolution use a window and a threshold compare character by character within a compare character by character within a

window (have to choose window size)window (have to choose window size) require certain fraction of matches within require certain fraction of matches within

window in order to display it with a “dot”window in order to display it with a “dot”

Example spreadsheet with windowExample spreadsheet with window (Demonstration A7)(Demonstration A7)

How do we choose a window size?How do we choose a window size? Window size changes with goal of analysisWindow size changes with goal of analysis

size of average exonsize of average exon size of average protein structural elementsize of average protein structural element size of gene promotersize of gene promoter size of enzyme active sitesize of enzyme active site

How do we choose a threshold value?How do we choose a threshold value? Threshold based on statisticsThreshold based on statistics

using shuffled actual sequenceusing shuffled actual sequence find average (find average (mm) and s.d. () and s.d. () of match scores of ) of match scores of

shuffled sequenceshuffled sequence convert original (unshuffled) scores (convert original (unshuffled) scores (xx) to) to ZZ scoresscores

• Z = (x - m)/Z = (x - m)/ use threshold Z of of 3 to 6use threshold Z of of 3 to 6

using analysis of other sets of sequencesusing analysis of other sets of sequences provides “objective” standard of significanceprovides “objective” standard of significance

Dot matrix analysis with Matlab bioinformatics toolboxDot matrix analysis with Matlab bioinformatics toolbox Get phage Get phage cI and phage P22 c2 repressor cI and phage P22 c2 repressor

sequences from Genbank (X00166 and sequences from Genbank (X00166 and V01153 respectively)V01153 respectively)

Use window size of 11 and stringency of 7Use window size of 11 and stringency of 7

Matlab codeMatlab code

getgenbank('X00166', 'TOFILE', 'HGENBANKX00166.GBK');getgenbank('X00166', 'TOFILE', 'HGENBANKX00166.GBK');

getgenbank('V01153', 'TOFILE', 'HGENBANKV01153.GBK');getgenbank('V01153', 'TOFILE', 'HGENBANKV01153.GBK');

seq1 = genbankread('HGENBANKX00166.GBK');seq1 = genbankread('HGENBANKX00166.GBK');

seq2 = genbankread('HGENBANKV01153.GBK');seq2 = genbankread('HGENBANKV01153.GBK');

window=11; num=7;window=11; num=7;

seqdotplot(seq1,seq2,window,num)seqdotplot(seq1,seq2,window,num)

xlabel('X00166');xlabel('X00166');

ylabel('V01153');ylabel('V01153');

title('Window 11 Num 7');title('Window 11 Num 7');

Dot matrixDot matrix

Note set of Note set of diagonals diagonals in lower in lower right that right that do not line do not line up due to up due to insertion insertion near 475 near 475 on cIon cI

Dot matrix analysis with DotmatcherDot matrix analysis with Dotmatcher Get the corresponding protein sequence of Get the corresponding protein sequence of

phage phage cI and phage P22 c2 repressor cI and phage P22 c2 repressor sequences (CAA24991 and CAA24470 sequences (CAA24991 and CAA24470 respectively)respectively)

Use Emboss Dotmatcher online:Use Emboss Dotmatcher online: emboss.bioinformatics.nlemboss.bioinformatics.nl

under ‘under ‘ALIGNMENT DOT PLOTS’ALIGNMENT DOT PLOTS’

Use window size of 10 and threshold of 23 Use window size of 10 and threshold of 23 BLOSUM62 units (default parameters)BLOSUM62 units (default parameters)

Dot matrix analysis with DotmatcherDot matrix analysis with Dotmatcher

Dot matrixDot matrix Similarity in Similarity in

the carboxy-the carboxy-terminal terminal domains of domains of the proteins the proteins agrees with agrees with the similarity the similarity in 3’ends of in 3’ends of the two DNA the two DNA sequences.sequences.

Dot matrix analysis with Matlab bioinformatics toolboxDot matrix analysis with Matlab bioinformatics toolbox Get human LDL receptor protein sequence Get human LDL receptor protein sequence

from Genbank (P01130)from Genbank (P01130) Use window size of 1 and stringency of 1Use window size of 1 and stringency of 1 Use window size of 23 and stringency of 7Use window size of 23 and stringency of 7

Matlab codeMatlab code

getgenpept('P01130', 'TOFILE', 'HGENBANKP01130.GBK');getgenpept('P01130', 'TOFILE', 'HGENBANKP01130.GBK'); seq5 = genbankread('HGENBANKP01130.GBK');seq5 = genbankread('HGENBANKP01130.GBK'); window=1; num=1; seqdotplot(seq5,seq5,window,num)window=1; num=1; seqdotplot(seq5,seq5,window,num) xlabel('P01130 Human LDL receptor');xlabel('P01130 Human LDL receptor'); ylabel('P01130 Human LDL receptor');ylabel('P01130 Human LDL receptor'); title('Window 1 Num 1');title('Window 1 Num 1'); window=23; num=7; seqdotplot(seq5,seq5,window,num)window=23; num=7; seqdotplot(seq5,seq5,window,num) xlabel('P01130 Human LDL receptor');xlabel('P01130 Human LDL receptor'); ylabel('P01130 Human LDL receptor');ylabel('P01130 Human LDL receptor'); title('Window 23 Num 7');title('Window 23 Num 7');

Dot matrixDot matrix

W=1 S=1W=1 S=1 Note set of Note set of

stacked stacked diagonals diagonals in upper in upper leftleft

Dot matrixDot matrix

W=23 S=7W=23 S=7 Note set of Note set of

stacked stacked diagonals diagonals in upper in upper leftleft