Multiple Sequence Alignment

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Multiple Sequence Alignment
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Terminology

• Motif the biological object one attempts to
  model - a functional or structural domain, active
  site, phosphorylation site etc.
• Pattern a qualitative motif description based on
  a regular expression-like syntax
• Profile a quantitative motif description -
  assigns a degree of similarity to a potential
  match

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Global Alignment

• Global algorithms are often not effective for
  highly diverged sequences and do not reflect the
  biological reality that two sequences may only
  share limited regions of conserved sequence.
• Sometimes two sequences may be derived from
  ancient recombination events where only a single
  functional domain is shared.

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What is Multiple Sequence Alignment (MSA)?

• Multiple sequence alignment (MSA) can be seen as
  a generalization of Pairwise Sequence Alignment -
  instead of aligning two sequences, n sequences
  are aligned simultaneously, where n is gt 2
• Definition A multiple sequence alignment is an
  alignment of n gt 2 sequences obtained by
  inserting gaps (-) into sequences such that the
  resulting sequences have all length L and can be
  arranged in a matrix of N rows and L columns
  where each column represents a homologous
  position (each column corresponds to a specific
  residue in the 'prototypical' protein)

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Multiple Sequence Alignment

• MSA applies both to nucleotide and amino acid
  sequences
• To construct a multiple alignment, one may have
  to introduce gaps in sequences at positions where
  there were no gaps in the corresponding pairwise
  alignment.
• This means that multiple alignments typically
  contain more gaps than any given pair of aligned
  sequences

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How to optimize alignment algorithms?

• Use structural information
• reading frame
• protein structure
• Sequence elements are not truly independent but
  related by phylogenic descent
• Sequences often contain highly conserved regions

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Optimize alignment algorithms
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Pairwise Alignment

• The alignment of two sequences (DNA or protein)
  is a relatively straightforward computational
  problem.

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The big-O notation

• One of the most important properties of an
  algorithm is how its execution time increases as
  the problem is made larger. By a larger problem,
  we mean more sequences to align, or longer
  sequences to align.
• This is the so-called algorithmic (or
  computational) complexity of the algorithm
• There is a notation to describe the algorithmic
  complexity, called the big-O notation.
• If we have a problem size (number of input data
  points) n, then an algorithm takes O(n) time if
  the time increases linearly with n.
• If the algorithm needs time proportional to the
  square of n, then it is O(n2)

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The big-O notation

• It is important to realize that an algorithm that
  is quick on small problems may be totally useless
  on large problems if it has a bad O() behavior.
• As a rule of thumb one can use the following
  characterizations, where n is the size of the
  problem, and c is a constant
• O(c) utopian
• O(log n) excellent
• O(n) very good
• O(n2) not so good
• O(n3) pretty bad
• O(cn) disaster

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The big-O notation

• To compute a N-wise alignment, the algorithmic
  complexity is something like O(c2n), where c is a
  constant, and n is the number of sequences.
• This is a big-O disaster!

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The best solution is Dynamic Programming.
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Multiple Sequence Alignment

• In pairwise alignments, you have a
  two-dimensional matrix with the sequenceson each
  axis.
• The number of operations required to locate the
  best path through the matrix is approximately
  proportional to the product of the lengths of the
  two sequences
• A possible general method would be to extend the
  pairwise alignment method into a simultaneous
  N-wise alignment, using a complete
  dynamical-programming algorithm in N dimensions.
• Algorithmically, this is not difficult to do

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Dynamic Programming

• Dynamic Programming is a very general programming
  technique.
• It is applicable when a large search space can be
  structured into a succession of stages, such
  that
• the initial stage contains trivial solutions to
  sub-problems
• each partial solution in a later stage can be
  calculated by recurring a fixed number of partial
  solutions in an earlier stage
• the final stage contains the overall solution

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Multiple Alignments

• In theory, making an optimal alignment between
  two sequences is computationally straightforward
  (Smith-Waterman algorithm), but aligning a large
  number of sequences using the same method is
  almost impossible.
• The problem increases exponentially with the
  number of sequences involved
• (the product of the sequence lengths)

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Optimal Alignment

• For a given group of sequences, there is no
  single "correct" alignment, only an alignment
  that is "optimal" according to some set of
  calculations.
• Determining what alignment is best for a given
  set of sequences is really up to the judgement of
  the investigator.

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Why we do multiple alignments?

• In order to characterize protein families,
  identify shared regions of homology in a multiple
  sequence alignment (this happens generally when
  a sequence search revealed homologies to several
  sequences).
• Determination of the consensus sequence of
  several aligned sequences.
• Consensus sequences can help to develop a
  sequence finger print which allows the
  identification of members of distantly related
  protein family (motifs)
• MSA can help us to reveal biological facts about
  proteins, like analysis of the secondary/tertiary
  structure)

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Why we do multiple alignments?

• Crucial for genome sequencing
• Random fragments of a large molecule are
  sequenced and those that overlap are found by a
  multiple sequence alignment program.
• There should be one correct alignment that
  corresponds to the genomic sequence rather than
  a range of possibilities
• Sequence may be from one strand of DNA or the
  other, so complements of each sequence must also
  be compared
• Sequence fragments will usually overlap, but by
  an unknown amount and in some cases, one sequence
  may be included within another
• All of the overlapping pairs of sequence
  fragments must be assembled into large composite
  genome sequence

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An example of Multiple Alignment
VTISCTGSSSNIGAG-NHVKWYQQLPG VTISCTGTSSNIGS--ITVNWY
QQLPG LRLSCSSSGFIFSS--YAMYWVRQAPG LSLTCTVSGTSFDD--
YYSTWVRQPPG PEVTCVVVDVSHEDPQVKFNWYVDG-- ATLVCLISDF
YPGA--VTVAWKADS-- AALGCLVKDYFPEP--VTVSWNSG--- VSLT
CLVKGFYPSD--IAVEWWSNG--
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Three Types of Algorithms

• Progressive ClustalW
• Iterative Muscle
• Concistency Based T-Coffee and Probcons

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Progressive Multiple Alignment

• The most practical and widely used method in
  multiple sequence alignment is the hierarchical
  extensions of pairwise alignment methods.
• The principal is that multiple alignments is
  achieved by successive application of pairwise
  methods.

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Choosing sequences for alignment

• The more sequences to align the better.
• Dont include similar (gt80) sequences.
• Sub-groups should be pre-aligned separately, and
  one member of each subgroup should be included in
  the final multiple alignment.

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Progressive Pairwise Methods

• Most of the available multiple alignment programs
  use some sort of incremental or progressive
  method that makes pairwise alignments, then adds
  new sequences one at a time to these aligned
  groups.
• This is an approximate or heuristic method!

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Multiple Alignment Method

• Compare all sequences pairwise.
• Perform cluster analysis on the pairwise data to
  generate a hierarchy for alignment. This may be
  in the form of a binary tree or a simple ordering
• Build the multiple alignment by first aligning
  the most similar pair of sequences, then the next
  most similar pair and so on. Once an alignment of
  two sequences has been made, then this is fixed.
  Thus for a set of sequences A, B, C, D having
  aligned A with C and B with D the alignment of A,
  B, C, D is obtained by comparing the alignments
  of A and C with that of B and D using averaged
  scores at each aligned position.

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Gap Penalties

• In the MSA scoring scheme, a penalty is
  subtracted for each gap introduced into an
  alignment because the gap increases uncertainty
  into an alignment
• The gap penalty is used to help decide whether or
  not to accept a gap or insertion in an alignment
• Biologically, it should in general be easier for
  a sequence to accept a different residue in a
  position, rather than having parts of the
  sequence chopped away or inserted.
  Gaps/insertions should therefore be more rare
  than point mutations (substitutions)
• In general, the lower the gapping penalties, the
  more gaps and more identities are detected but
  this should be considered in relation to
  biological significance
• Most MSA programs allow for an adjustment of gap
  penalties

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The PILEUP Algorithm

• First, PILEUP calculates approximate pairwise
  similarity scores between all sequences to be
  aligned, and they are clustered into a dendrogram
  (tree structure).
• Then the most similar pairs of sequences are
  aligned.
• Averages (similar to consensus sequences) are
  calculated for the aligned pairs.
• New sequences and clusters of sequences are added
  one by one, according to the branching order in
  the dendrogram.

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Choosing sequences for MSA

• As far as possible, try to align sequences of
  similar length.
• Pileup can align sequences of up to 5000
  residues, with 2000 gaps (total 7000 characters).
• Pileup is a good program only for similar (close)
  sequences.

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PileUp considerations

• PileUp does global multiple alignment, and
  therefore is good for a group of similar
  sequences.
• PileUp will fail to find the best local region of
  similarity (such as a shared motif) among distant
  related sequences.
• PileUp always aligns all of the sequences you
  specified in the input file, even if they are not
  related.
• The alignment can be degraded if some of the
  sequences are only distantly related.

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PILEUP Considerations

• Since the alignment is calculated on a
  progressive basis, the order of the initial
  sequences can affect the final alignment.
• PILEUP parameters 2 gap penalties (gap insert
  and gap extend) and an amino acid comparison
  matrix.
• PILEUP will refuse to align sequences that
  require too many gaps or mismatches.
• PILEUP will take quite a while to align more than
  about 10 sequences

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CLUSTAL

• CLUSTAL is a stand-alone (i.e. not integrated
  into GCG) multiple alignment program that is
  superior in some respects to PILEUP
• Works by progressive alignment it aligns a pair
  of sequences then aligns the next one onto the
  first pair
• Most closely related sequences are aligned first,
  and then additional sequences and groups of
  sequences are added, guided by the initial
  alignments
• Uses alignment scores to produce a phylogenetic
  tree

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CLUSTAL

• Aligns the sequences sequentially, guided by the
  phylogenetic relationships indicated by the tree
• Gap penalties can be adjusted based on specific
  amino acid residues, regions of hydrophobicity,
  proximity to other gaps, or secondary structure
• Is available with a great web interface
  http//www.ebi.ac.uk/clustalw/
• Also available in Biology Workbench

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Multiple Alignment tools on the Web

• There are a variety of multiple alignment tools
  available for free on the web.
• CLUSTAL is available from a number of sites (with
  a variety of restrictions)
• Other algorithms are available too

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Muscle Algorithm Using The Iteration
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Consistency Based Algorithms T-Coffee

• Gotoh (1990)
• Iterative strategy using concistency
• Martin Vingron (1991)
• Dot Matrices Multiplications
• Accurate but too stringeant
• Dialign (1996, Morgenstern)
• Concistency
• Agglomerative Assembly
• T-Coffee (2000, Notredame)
• Concistency
• Progressive algorithm

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T-Coffee and Consistency
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T-Coffee and Consistency
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T-Coffee and Consistency
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T-Coffee and Consistency
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T-Coffee and Consistency
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APPROXIMATEFAST
ACCURATE SLOW
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Some URLs

• EMBL-EBI
• http//www.ebi.ac.uk/clustalw/
• BCM Search Launcher Multiple Alignment
• http//dot.imgen.bcm.tmc.edu9331/multi-align/mult
  i-align.html
• Multiple Sequence Alignment for Proteins (Wash.
  U. St. Louis)
• http//www.ibc.wustl.edu/service/msa/

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Editing and displaying alignments

• Sequence editors are used for
• manual alignment/editing of sequences
• visualization of data
• data management
• import/export of data
• graphical enhancement of data for presentations

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Editing Multiple Alignments

• There are a variety of tools that can be used to
  modify a multiple alignment.
• These programs can be very useful in formatting
  and annotating an alignment for publication.
• An editor can also be used to make modifications
  by hand to improve biologically significant
  regions in a multiple alignment created by one of
  the automated alignment programs.

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Displaying a multiple alignment in GCG

• There are several programs to display the
  multiple alignment prettily.
• The Pretty program prints sequences with their
  columns aligned and can display a consensus for
  the alignment, allowing you to look at
  relationships among the sequences.
• The PrettyBox program displays the alignment
  graphically with the conserved regions of the
  alignment as shaded boxes. The output is in
  Postscript format.

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Example of PrettyBox Output
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GCG alignment editors

• Alignments produced with PILEUP (or CLUSTAL) can
  be adjusted with LINEUP.
• Nicely shaded printouts can be produced with
  PRETTYBOX
• GCG's SeqLab X-Windows interface has a superb
  multiple sequence editor - the best editor of any
  kind.

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Other editors

• The MACAW and SeqVu program for Macintosh and
  GeneDoc and DCSE for PCs are free and provide
  excellent editor functionality.
• Many comprehensive molecular biology programs
  include multiple alignment functions
• MacVector, OMIGA, Vector NTI, and
  GeneTool/PepTool all include a built-in version
  of CLUSTAL

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SeqVu
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CINEMA

• CINEMA (Colour INteractive Editor for Multiple
  Alignments)
• It is an editor created completely in JAVA (old
  browsers beware)
• It includes a fully functional version of
  CLUSTAL, BLAST, and a DotPlot module

http//www.bioinf.man.ac.uk/dbbrowser/CINEMA2.1/
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Informative Colors

• By default, the alignment is coloured crudely
  according to residue type (proline and glycine
  have special structural properties, particularly
  in membrane proteins, so are grouped separately
  similarly for cysteine, which is often involved
  in disulphide bond formation)
• Polar positive H, K, R Blue
• Polar negative D, E Red
• Polar neutral S, T, N, Q Green
• Non-polar aliphatic A, V, L, I, M White
• Non-polar aromatic F, Y, W Purple
• P, G Brown
• C Yellow
• Special characters B, Z, X, - Grey

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