Multiple Sequence Alignment A survey of the various programs available and application of MSA in add

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Multiple Sequence Alignment A survey of the
various programs available and application of MSA
in addressing certain biological problems

• Jeff Mower
• Kiran Annaiah

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Sequence Comparison

• Aligning two sequences is the cornerstone of
  Bioinformatics.
• Sequence alignment is the basic step upon which
  everything else built.
• Sequence alignments are employed in
• predicting de novo secondary structure of
  proteins,
• The initial functional assignment
• knowledge-based tertiary structure predictions,
• Interpretation of data from genome sequencing
  projects
• Inference of phylogenetic trees and resolution of
  ancestral relationships between species.

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Sequence Comparisons

• Homology Searches
• Look for homologous sequences in databases using
  FASTA or BLAST program
• Pattern Searches
• Used for searching short sequence patterns
• Multiple Sequence Alignment
• For aligning and comparing 3 or more related
  sequences
• Profile Analysis
• A profile is created from a multiple sequence
  alignment

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MSA vs Pairwise

• PSA
• based on seq. similarity we can identify
  unknown biological relationship
• MSA
• Similar to PSA
• But also possible to identify conserved
  sub-patterns based on known biological
  relationship
• High seq similarity functional structural
  similarity (PSA)
• Sequences with functional and structural
  similarity can differ in sequences
• PSA cannot detect this case
• Example Haemoglobin

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MSA vs Pairwise

• Structurally and functionally conserved molecules
  can differ in sequence PSA cannot reveal
  conserved patterns
• Comparing of 2 sequences with high similarity
  patterns detection lost due to high similarity
• MSA useful in revealing critical patterns from
  multiple related sequences

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Multiple Sequence Alignment

• Homology
• Homologous sequences, derive from common ancestor
• Inferred by sequence similarity
• MSA useful to demonstrate homology
• Weak similarity non-significant in pairwise
  comparison
• could be highly significant if same residues are
  conserved
• in other distantly related sequences

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Multiple Sequence Alignment

• Global or Local Alignments
• Substitution Matrices and weighting gaps
• Best alignment is one that represents an
  evolutionary scenario
• Mutational events in evolution considered in MSA
• Substitutions
• Insertions
• Deletions
• BLOSUM and PAM based on evolutionary distances
• Affine gap penalty model
• p a bL
• p a b blog(L)
• Scoring MSA
• Sum of Pairs score (SP) for columns

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http//www.people.virginia.edu/wrp/papers/ismb200
0.pdf
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Which MSA method?

• Global Methods
• Optimal Algorithms (MSA, MWT, MUSEQAL)
• Progressive (MULTALIN, PILEUP, CLUSTAL, MULTAL,
  AMULT, DFALIGN, T-Coffee, MAP, PRRP, AMPS)
• Local methods
• PIMA, DIALIGN, PRALINE, POA, MACAW, BlockMaker,
  Iteralign
• Combined (GENALIGN, ASSEMBLE, DCA)
• Statistical (HMMT, SAGA, SAM, Match Box)
• Parsimony (MALIGN, TreeAlign)

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Progressive algorithm

• Alignment is only an approximate solution
  (heuristic based)
• Simplest and effective ways of MSA
• Less time and less memory
• Sequences are added one by one to the multiple
  alignment based on a pre-computed dendogram
• Sequence addition is by PSA algorithm
• Disadvantage
• Once a sequence is aligned, cant be changed even
  if it conflicts with later sequence additions
• Examples
• PileUp, ClustalW, MultAlign, T-Coffee, etc

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Exact Algorithms

• Useful in cases where sequences are extremely
  divergent
• Simultaneously aligns all sequences
• Disadvantage
• Need to generalize Needleman-Wunsch algorithm
• Only for a maximum of 3 sequences
• Causes exponential increase in time and memory as
  number of sequences increase
• Examples
• MSA (up to 10 closely related sequences)

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Iterative algorithms

• Iterate over a existing sub-optimal solution,
  modifying it at each step, until a convergence
  point is met.
• Examples
• SAGA, AMPS, Praline, IterAlign

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Consistency-based Algorithms

• Given a set of sequences, an optimal MSA is one
  that agrees most with all possible optimal
  pairwise alignments
• Do not depend on specific subs. Matrix
• Score associated with alignment of 2 residues
  depends on their indexes (position within protein
  sequence)
• Most consistent are often the ones close to truth
• Examples
• T-Coffee, DiAlign

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Multiple Alignment by profile HMM training

• Given n sequences , consider the following
  cases
• If the profile HMM is known, the following
  procedure can be applied
• Align each sequence S(i) to the profile
  separately.
• Accumulate the obtained alignments to a multiple
  alignment.
• If the profile HMM is not known, one can use
  the following technique in order to obtain an HMM
  profile from the given sequences
• Choose a length L for the profile HMM and
  initialize the transition and emission
  probabilities.
• Train the model using the Baum-Welch algorithm,
  on all the training sequences.
• Obtain the multiple alignment from the resulting
  profile HMM, as in the previous case.
• http//www.math.tau.ac.il/rshamir/algmb/00/scribe
  00/html/lec06/node11.html

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Schematic of T-Coffee
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Testing the methods

• BAliBASE benchmark
• Correct Alignments
• Core Blocks of Conserved Motifs
• Typical Hard Problem Sets

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BAliBASE reference sets, showing the number of
alignments in each set
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BAliBASE - Reference set 1
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Scores based on core blocks(V1)
Scores based on full-length alignment(V1)
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Median score for Reference set 2
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Median score for aligning subgroups of sequences
in Reference set 3
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Median score for N/C terminal extensions
Reference set 4
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Median score for internal insertions Reference
set 5
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Results

• Core blocks aligned well over long sequences
  all programs
• Due to different patterns of conservation
• Alignment unreliable in the twilight zone
• Iterative method did well under distinct
  alignment conditions, but not in the presence of
  an orphan sequence
• Global algorithms accurate and reliable for
• equidistant sequences
• divergent families of sequences and
• alignment of orphan sequence with a family
• Local algorithm DiAlign
• Best for N/C terminal extensions
• Internal insertions

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ClustalW vs DiAlign vs T-Coffee vs POA

• ClustalW global progressive method
• Guide tree created
• Successive pairwise alignment
• Poa progressive using partially ordered graphs
• No tree to guide alignment of sequences
• 2 most similar seqs are aligned and others are
  added to this one profile in a stepwise fashion
• DiAlign local algorithm
• Aligns whole segments
• PSA performed and ungapped alignments used
• T-Coffee progressive global local
• Performs PSA twice, once global (ClustalW) and
  local (LAlign)
• Results combined into primary library, then
  extension step
• Progressive alignment using info from library

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Results of BAliBASE testing
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CPU time consumed by each program to align sets
of increasingly long sequences
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Results

• Poor performance by all programs with increase in
  evolutionary distance
• Increase in seq length better alignment
• T-Coffee best for low moderate evolutionary
  distances
• DiAlign good for high evolutionary distances

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What Can We Do With MSAs?

• Motif / pattern identification
• Structural modeling
• Phylogenetic analysis
• Molecular evolutionary analyses
• Identification of conserved genomic regions
  across species

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Phylogenetic Analysis

• Visual representation of a MSA as a tree of
  relationships
• Many methods
• Distance builds tree by clustering sequences
  according to their similarity
• Parsimony finds tree that minimizes the number
  of changes required
• Maximum Likelihood finds tree that maximizes
  likelihood given parameters
• Bayesian Markov chain Monte Carlo simulation to
  calculate posterior probabilities of trees

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Determining Relationships
Phylogram
Cladogram
(Baldauf 2003)
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Rooted vs Unrooted Trees
Rooted
Unrooted
(Baldauf 2003)
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Many taxa (567), Few genes (3)
(Soltis et al. 1999)
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Few taxa (13), Many genes (61)
(Goremykin et al. 2003)
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Many taxa, Many genes
Coming Soon
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Detecting Gene Families
(Baldauf 2003)
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Human, Gorilla, Chimp Glycophorin gene family
(Wang et al. 2003)
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Bootstrapping
(Baldauf 2003)
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Molecular Evolution Background

• Types of changes in protein-coding DNA
• Silent (synonymous)
• Replacement (nonsynonymous)
• Based on degeneracy of the genetic code
• K frequency of change between two sequences
• Ka of replacement changes per replacement
  site
• Driven by natural selection
• Reflect level of protein conservation
• Ks of silent changes per silent site
• Neutral, not affected by selective forces
• Used to estimate the neutral mutation rate

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Estimation of Substitution Rates

• Absolute Rate
• R ½ K / T, where T time since last common
  ancestor
• Rates are directly comparable
• Relative Rate
• Two species of interest and one outgroup
• Compare K from species 1 and outgroup against K
  from species 2 and outgroup

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Relative Rate
Rat
K Rat-Kan K Hum-Kan

Human
K Rat-Kan - K Hum-Kan
-
Kangaroo
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Evaluation of Selective Pressures, The Ka / Ks
Ratio

• Ka / Ks gt 1 indicates positive selection
• Ka / Ks 1 indicates no selection
• Ka / Ks lt 1 indicates purifying selection

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280 homologs, Macaque-Human
(Wang et al. 2003)
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Conserved Genomic Regions

• Need complete genomes or homologous genomic
  regions
• Identify exons from distantly related species
• Identify regulatory elements from more closely
  related species

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(Thomas et al. 2003)
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References

• Biological Sequence Analysis R. Durbin, S.
  Eddy, A. Krogh G. Mitchison
• Bioinformatics Sequence, structure and databanks
  D. Higgins W. Taylor
• Recent progress in multiple sequence alignment a
  survey.
• http//www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd
  RetrievedbPubMedlist_uids11966409doptAbstra
  ct
• Quality assessment of multiple alignment
  programs.http//www.ncbi.nlm.nih.gov/entrez/query
  .fcgi?cmdRetrievedbPubMedlist_uids12354624do
  ptAbstract
• Multiple sequence alignment with the Clustal
  series of programs.http//www.ncbi.nlm.nih.gov/en
  trez/query.fcgi?cmdRetrievedbPubMedlist_uids1
  2824352doptAbstract
• MAVID multiple alignment serverhttp//www.ncbi.nl
  m.nih.gov/entrez/query.fcgi?cmdRetrievedbPubMed
  list_uids12824358doptAbstract
• Multiple alignment of sequences and structures.
  http//www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd
  RetrievedbPubMedlist_uids12510583doptAbstrac
  t
• Fast algorithms for large-scale genome alignment
  and comparison.http//www.ncbi.nlm.nih.gov/entrez
  /query.fcgi?cmdRetrievedbPubMedlist_uids12034
  836doptAbstract