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Sequencing Sequence Alignment
David Wishart, University of Alberta
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Objectives

• Understand how DNA sequence data is collected and
  prepared
• Be aware of the importance of sequence searching
  and sequence alignment in biology and medicine
• Be familiar with the different algorithms and
  scoring schemes used in sequence searching and
  sequence alignment

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High Throughput DNA Sequencing
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30,000
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Shotgun Sequencing
Isolate Chromosome
ShearDNA into Fragments
Clone into Seq. Vectors
Sequence
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Principles of DNA Sequencing
Primer
DNA fragment
Amp
PBR322
Tet
Ori
Denature with heat to produce ssDNA
Klenow ddNTP dNTP primers
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The Secret to Sanger Sequencing
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Principles of DNA Sequencing
3 Template
G C A T G C
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5 Primer
dATP dCTP dGTP dTTP
ddCTP
GddC
GCddA
GCAddT
ddG
GCATGddC
GCATddG
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Principles of DNA Sequencing
G
T
short
_
_
C
A
G C A T G C


long
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Capillary Electrophoresis
Separation by Electro-osmotic Flow
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Multiplexed CE with Fluorescent detection
ABI 3700
96x700 bases
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Shotgun Sequencing
Assembled Sequence
Sequence Chromatogram
Send to Computer
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Shotgun Sequencing

• Very efficient process for small-scale (10 kb)
  sequencing (preferred method)
• First applied to whole genome sequencing in 1995
  (H. influenzae)
• Now standard for all prokaryotic genome
  sequencing projects
• Successfully applied to D. melanogaster
• Moderately successful for H. sapiens

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The Finished Product
GATTACAGATTACAGATTACAGATTACAGATTACAG ATTACAGATTACA
GATTACAGATTACAGATTACAGA TTACAGATTACAGATTACAGATTACA
GATTACAGAT TACAGATTAGAGATTACAGATTACAGATTACAGATT AC
AGATTACAGATTACAGATTACAGATTACAGATTA CAGATTACAGATTAC
AGATTACAGATTACAGATTAC AGATTACAGATTACAGATTACAGATTAC
AGATTACA GATTACAGATTACAGATTACAGATTACAGATTACAG ATTA
CAGATTACAGATTACAGATTACAGATTACAGA TTACAGATTACAGATTA
CAGATTACAGATTACAGAT
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Sequencing Successes
T7 bacteriophage completed in 1983 39,937 bp, 59
coded proteins Escherichia coli completed in
1998 4,639,221 bp, 4293 ORFs Sacchoromyces
cerevisae completed in 1996 12,069,252 bp, 5800
genes
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Sequencing Successes
Caenorhabditis elegans completed in
1998 95,078,296 bp, 19,099 genes Drosophila
melanogaster completed in 2000 116,117,226 bp,
13,601 genes Homo sapiens completed in
2003 3,201,762,515 bp, 31,780 genes
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Genomes to Date

• 8 vertebrates (human, mouse, rat, fugu,
  zebrafish)
• 3 plants (arabadopsis, rice, poplar)
• 2 insects (fruit fly, mosquito)
• 2 nematodes (C. elegans, C. briggsae)
• 1 sea squirt
• 4 parasites (plasmodium, guillardia)
• 4 fungi (S. cerevisae, S. pombe)
• 200 bacteria and archebacteria
• 2000 viruses

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So what do we do with all this sequence data?
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Sequence Alignment
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Alignments tell us about...

• Function or activity of a new gene/protein
• Structure or shape of a new protein
• Location or preferred location of a protein
• Stability of a gene or protein
• Origin of a gene or protein
• Origin or phylogeny of an organelle
• Origin or phylogeny of an organism

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Factoid
Sequence comparisons lie at the heart of
all bioinformatics
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Similarity versus Homology

• Similarity refers to the likeness or identity
  between 2 sequences
• Similarity means sharing a statistically
  significant number of bases or amino acids
• Similarity does not imply homology

• Homology refers to shared ancestry
• Two sequences are homologous is they are derived
  from a common ancestral sequence
• Homology usually implies similarity

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Similarity versus Homology

• Similarity can be quantified
• It is correct to say that two sequences are X
  identical
• It is correct to say that two sequences have a
  similarity score of Z
• It is generally incorrect to say that two
  sequences are X similar

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Similarity versus Homology

• Homology cannot be quantified
• If two sequences have a high identity it is OK
  to say they are homologous
• It is incorrect to say two sequences have a
  homology score of Z
• It is incorrect to say two sequences are X
  homologous

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Homologues All That

• Homologue (or Homolog)
• Protein/gene that shares a common ancestor and
  which has good sequence and/or structure
  similarity to another (general term)
• Paralogue (or Paralog)
• A homologue which arose through gene duplication
  in the same species/chromosome
• Orthologue (or Ortholog)
• A homologue which arose through speciation (found
  in different species)

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Sequence Complexity
MCDEFGHIKLAN. High Complexity
ACTGTCACTGAT. Mid Complexity
NNNNTTTTTNNN. Low Complexity
Translate those DNA sequences!!!
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Assessing Sequence Similarity
THESTORYOFGENESIS THISBOOKONGENETICS THESTORYOFGE
NESI-S THISBOOKONGENETICS THE STORY OF
GENESIS THIS BOOK ON GENETICS
Two Character Strings
Character Comparison


Context Comparison
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Assessing Sequence Similarity
is this alignment significant?
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Is This Alignment Significant?
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Some Simple Rules

• If two sequence are gt 100 residues and gt
  25 identical, they are likely related
• If two sequences are 15-25 identical they may be
  related, but more tests are needed
• If two sequences are lt 15 identical they are
  probably not related
• If you need more than 1 gap for every 20 residues
  the alignment is suspicious

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Doolittles Rules of Thumb
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Sequence Alignment - Methods

• Dot Plots
• Dynamic Programming
• Heuristic (Fast) Local Alignment
• Multiple Sequence Alignment
• Contig Assembly

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Dot Plots
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Dot Plots

• Invented in 1970 by Gibbs McIntyre
• Good for quick graphical overview
• Simplest method for sequence comparison
• Inter-sequence comparison
• Intra-sequence comparison
• Identifies internal repeats
• Identifies domains or modules

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Dot Plots Internal Repeats
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Dot Plot Algorithm

• Take two sequences (A B), write sequence A out
  as a row (lengthm) 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 theres a match mark it
  with a dot, if not, leave blank

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Dot Plot Algorithm
A C D E F G H G
A C D E F G H G
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Dot Plots

• Most commercial programs offer pretty good dot
  plot programs including
• GCG/Omiga (Pharmacopeia)
• PepTool (BioTools Inc.)
• LaserGene (DNAStar)
• Popular freeware package is Dotter
  www.cgr.ki.se/cgr/groups/sonnhammer/Dotter.html
• Dotlet http//www.isrec.isb-sib.ch/java/dotlet/Dot
  let.html
• JDotter http//athena.bioc.uvic.ca/sars/jdotter/ma
  in.php

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Dynamic Programming
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Dynamic Programming

• Developed by Needleman Wunsch (1970)
• Refined by Smith Waterman (1981)
• Ideal for quantitative assessment
• Guaranteed to be mathematically optimal
• Slow N2 algorithm
• Performed in 2 stages
• Prepare a scoring matrix using recursive function
• Scan matrix diagonally using traceback protocol

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The Recursive Function
Si-1,j-1 or max Si-x,j-1 wx-1
or max Si-1,j-y wy-1
Sij sij max
2ltxlti
2ltyltj
W gap penalty S alignment score
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Identity Scoring Matrix (Sij)
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A Simple Example...
A A T V D A 1 V V D
A A T V D A 1 1 V V D
A A T V D A 1 1 0 0 0 V V D
A A T V D A 1 1 0 0 0 V 0 V D
A A T V D A 1 1 0 0 0 V 0 1 1 V D
A A T V D A 1 1 0 0 0 V 0 1 1 2 V D
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A Simple Example...
A A T V D A 1 1 0 0 0 V 0 1 1 2 1 V D
A A T V D A 1 1 0 0 0 V 0 1 1 2 1 V 0 1
1 2 2 D 0 1 1 1 3
A A T V D A 1 1 0 0 0 V 0 1 1 2 1 V 0 1
1 2 2 D 0 1 1 1 3
A A T V D A - V V D
A A T V D A V V D
A A T V D A V - V D
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Could We Do Better?

• Key to the performance of Dynamic Programming is
  the scoring function
• Dynamic Programming always gives the
  mathematically correct answer
• Dynamic Programming does not always give the
  biologically correct answer
• The weakest link -- The Scoring Matrix

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Scoring Matrices

• An empirical model of evolution, biology and
  chemistry all wrapped up in a 20 X 20 table of
  integers
• Structurally or chemically similar residues
  should ideally have high diagonal or off-diagonal
  numbers
• Structurally or chemically dissimilar residues
  should ideally have low diagonal or off-diagonal
  numbers

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A Better Matrix - PAM250
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Using PAM250...
A T V D A 2 T 1 3 V 0 0 4 D 0 0-2 4
Gap Penalty -1
A A T V D A 2 V V D
A A T V D A 2 1 V V D
A A T V D A 2 1 0 -1 -1 V V D
A A T V D A 2 1 0 -1 -1 V -1 2 V D
A A T V D A 2 1 0 -1 -1 V -1 2 1
V D
A A T V D A 2 1 0 -1 -1 V -1 2 1 5 V D
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Using PAM250...
A T V D A 2 T 1 3 V 0 0 4 D 0 0-2 4
Gap Penalty -1
A A T V D A 2 1 0 -1 -1 V -1 2 1 5
-1 V D
A A T V D A 2 1 0 -1 -1 V -1 2 1 5 -1 V
-1 1 2 5 3 D -1 1 1 0 9
A A T V D A 2 1 0 -1 -1 V -1 2 1 5 -1 V
-1 1 2 5 3 D -1 1 1 0 9
A A T V D A V - V D
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PAM Matrices

• Developed by M.O. Dayhoff (1978)
• PAM Point Accepted Mutation
• Matrix assembled by looking at patterns of
  substitutions in closely related proteins
• 1 PAM corresponds to 1 amino acid change per 100
  residues
• 1 PAM 1 divergence or 1 million years in
  evolutionary history

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Dynamic Programming

• Great for doing pairwise global alignments
• Produces a quantitative alignment score
• Problems if one tries to do alignments with very
  large sequences (memory requirement grows as N2
  or as N x M)
• Serious problems if one tries to align one
  sequence against a database (10s of hours)
• Need an alternative..

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Fast Local Alignment Methods
ACDEAGHNKLM...
KKDEFGHPKLM...
SCDEFCHLKLM...
MCDEFGHNKLV...
ACDEFGHIKLM...
QCDEFGHAKLM...
AQQQFGHIKLPI...
WCDEFGHLKLM...
SMDEFAHVKLM...
ACDEFGFKKLM...
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Fast Local Alignment Methods

• Developed by Lipman Pearson (1985/88)
• Refined by Altschul et al. (1990/97)
• Ideal for large database comparisons
• Uses heuristics statistical simplification
• Fast N-type algorithm (similar to Dot Plot)
• Cuts sequences into short words (k-tuples)
• Uses Hash Tables to speed comparison

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Fast Alignment Algorithm
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Fast Alignment Algorithm
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Fast Alignment Algorithm
A C D E F G D E F...
L M R G CD D Y G
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Fast Alignment Algorithm
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FASTA

• Developed in 1985 and 1988 (W. Pearson)
• Looks for clusters of nearby or locally dense
  identical k-tuples
• init1 score score for first set of k-tuples
• initn score score for gapped k-tuples
• opt score optimized alignment score
• Z-score number of S.D. above random
• expect expected of random matches

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FASTA
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Multiple Sequence Alignment
Multiple alignment of Calcitonins
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Multiple Alignment Algorithm

• Take all n sequences and perform all possible
  pairwise (n/2(n-1)) alignments
• Identify highest scoring pair, perform an
  alignment create a consensus sequence
• Select next most similar sequence and align it to
  the initial consensus, regenerate a second
  consensus
• Repeat step 3 until finished

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Multiple Sequence Alignment

• Developed and refined by many (Doolittle, Barton,
  Corpet) through the 1980s
• Used extensively for extracting hidden
  phylogenetic relationships and identifying
  sequence families
• Powerful tool for extracting new sequence motifs
  and signature sequences

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Multiple Alignment

• Most commercial vendors offer good multiple
  alignment programs including
• GCG (Accelerys)
• PepTool/GeneTool (BioTools Inc.)
• LaserGene (DNAStar)
• Popular web servers include T-COFFEE, MULTALIN
  and CLUSTALW
• Popular freeware includes PHYLIP PAUP

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Mutli-Align Websites

• Match-Box http//www.fundp.ac.be/sciences/biologie
  /bms/matchbox_submit.shtml
• MUSCA http//cbcsrv.watson.ibm.com/Tmsa.html
• T-Coffee http//www.ch.embnet.org/software/TCoffee
  .html
• MULTALIN http//www.toulouse.inra.fr/multalin.html
  
• CLUSTALW http//www.ebi.ac.uk/clustalw/

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T-Coffee

• Uses standard progressive alignment but with a
  twist to avoid local minima
• Allows the combination of a collection of
  multiple/pairwise, global or local alignments
  into a single model
• It also allows to estimate the level of
  consistency of each position within the new
  alignment with the rest of the alignments

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Multi-alignment Contig Assembly
ATCGATGCGTAGCAGACTACCGTTACGATGCCTT TAGCTACGCATCGT
CTGATGGCAATGCTACGGAA..
TAGCTACGCATCGT
TAGCAGACTACCGTT
ATCGATGCGTAGC
GTTACGATGCCTT
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Contig Assembly

• Read, edit trim DNA chromatograms
• Remove overlaps ambiguous calls
• Read in all sequence files (10-10,000)
• Reverse complement all sequences (doubles of
  sequences to align)
• Remove vector sequences (vector trim)
• Remove regions of low complexity
• Perform multiple sequence alignment

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Contig Assembly Multiple Alignment

• Only accept a very high sequence identity
• Accept unlimited number of end gaps
• Very high cost for opening internal gaps
• A short match with high score/residue is
  preferred over a long match with low score/residue

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Assembly Parameters

• User-selected parameters
• minimum length of overlap
• percent identity within overlap
• Non-adjustable parameters
• sequence quality factors

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Chromatogram Editing
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Sequence Loading
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Sequence Alignment
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Contig Alignment - Process
ATCGATGCGTAGC
TAGCAGACTACCGTT
GTTACGATGCCTT
TGCTACGCATCG
CGATGCGTAGCA
CGATGCGTAGCA
ATCGATGCGTAGC
TAGCAGACTACCGTT
GTTACGATGCCTT
ATCGATGCGTAGCAGACTACCGTTACGATGCCTT
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Problems for Assembly

• Repeat regions
• Capture sequences from non-contiguous regions
• Polymorphisms
• Cause failure to join correct regions
• Large data volume
• Requires large numbers of pair-wise comparisons

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Sequence Assembly Programs

• Phred - base calling program that does detailed
  statistical analysis (UNIX)
  http//www.phrap.org/
• Phrap - sequence assembly program (UNIX)
  http//www.phrap.org/
• TIGR Assembler - microbial genomes (UNIX)
  http//www.tigr.org/softlab/assembler/
• The Staden Package (UNIX)
• http//www.mrc-lmb.cam.ac.uk/pubseq/
• GeneTool/ChromaTool/Sequencher (PC/Mac)

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Phrap

• Phrap is a program for assembling shotgun DNA
  sequence data
• Uses a combination of user-supplied and
  internally computed data quality information to
  improve assembly accuracy in the presence of
  repeats
• Constructs the contig sequence as a mosaic of the
  highest quality read segments rather than a
  consensus
• Handles large datasets

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http//bio.ifom-firc.it/ASSEMBLY/assemble.html
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Conclusions

• Sequence alignments and database searching are
  key to all of bioinformatics
• There are four different methods for doing
  sequence comparisons 1) Dot Plots 2) Dynamic
  Programming 3) Fast Alignment and 4) Multiple
  Alignment
• Understanding the significance of alignments
  requires an understanding of statistics and
  distributions