Multiple Sequence Alignment

1
Multiple Sequence Alignment
2
Definition

• Homology related by descent
• Homologous sequence positions

? ATTGCGC
ATTGCGC
ATTGCGC
?
AT-CCGC
ATTGCGC
? ATCCGC
C
3
Reasons for aligning sets of sequences

• Organise data to reflect sequence homology
• Estimate evolutionary distance
• Infer phylogenetic trees from homologous sites
• Highlight conserved sites/regions
• Highlight variable sites/regions
• Uncover changes in gene structure
• Look for evidence of selection
• Summarise information

4
Alignments help to
Organise
Visualise
Analyze
Sequence Data
5

• The process of aligning sequences is a game
  involving playing off gaps and mismatches

6
Ways of aligning multiple sequences

• By hand
• Automated
• Combination

7
Definition

• Optimality criteria some kind rule or scoring
  scheme to help you to decide what you consider to
  be the best alignment

8
Pairwise vs Multiple Sequences

• Pairs of sequences typically aligned using
  exhaustive algorithms (dynamic programming)
• complexity of exhaustive methods is O(2n mn) n
  number of sequences m sequence length
• Multiple sequence alignment uses heuristic methods

9
The Correct Alignment
? ATTGCGC
ATTGCGC
ATTGCGC
? ATCCGC
C
10
The Correct Alignment
11

• Sequence alignment is easy with sufficiently
  closely related sequences
• Below a certain level of identity sequence
  alignment may become meaningless
• twilight zone for aa sequences 30
• In the twilight zone it is good to make use of
  additional information if possible (e.g.
  structure)

12
Consensus Sequences

• Simplest FormA single sequence which represents
  the most common amino acid/base in that position
• Y D D G A V - E A L
• Y D G G - - - E A L
• F E G G I L V E A L
• F D - G I L V Q A V
• Y E G G A V V Q A L
• Y D G G A/I V/L V E A L

13
Multiple Alignment Formats

• e.g. Clustal, Phylip, MSF, MEGA etc. etc.

14
Clustal Format

• CLUSTAL X (1.81) multiple sequence alignment
• CAS1_BOVIN MKLLILTCLVAVALARPKHPIKHQGLPQ------
  --EVLNEN-
• CAS1_SHEEP MKLLILTCLVAVALARPKHPIKHQGLSP------
  --EVLNEN-
• CAS1_PIG MKLLIFICLAAVALARPKPPLRHQEHLQNEPDSR
  E--------
• CAS1_HUMAN MRLLILTCLVAVALARPKLPLRYPERLQNPSESS
  E--------
• CAS1_RABBIT MKLLILTCLVATALARHKFHLGHLKLTQEQPESS
  EQEILKERK
• CAS1_MOUSE MKLLILTCLVAAAFAMPRLHSRNAVSSQTQ----
  --QQHSSSE
• CAS1_RAT MKLLILTCLVAAALALPRAHRRNAVSSQTQ----
  ---------
• .. .

15
Phylip Format (Interleaved)

• 7 100
• SOMA_BOVIN MMAAGPRTSL LLAFALLCLP WTQVVGAFPA
  MSLSGLFANA VLRAQHLHQL
• SOMA_SHEEP MMAAGPRTSL LLAFTLLCLP WTQVVGAFPA
  MSLSGLFANA VLRAQHLHQL
• SOMA_RAT_P -MAADSQTPW LLTFSLLCLL WPQEAGAFPA
  MPLSSLFANA VLRAQHLHQL
• SOMA_MOUSE -MATDSRTSW LLTVSLLCLL WPQEASAFPA
  MPLSSLFSNA VLRAQHLHQL
• SOMA_RABIT -MAAGSWTAG LLAFALLCLP WPQEASAFPA
  MPLSSLFANA VLRAQHLHQL
• SOMA_PIG_P -MAAGPRTSA LLAFALLCLP WTREVGAFPA
  MPLSSLFANA VLRAQHLHQL
• SOMA_HUMAN -MATGSRTSL LLAFGLLCLP WLQEGSAFPT
  IPLSRLFDNA MLRAHRLHQL
• AADTFKEFER TYIPEGQRYS -IQNTQVAFC
  FSETIPAPTG KNEAQQKSDL
• AADTFKEFER TYIPEGQRYS -IQNTQVAFC
  FSETIPAPTG KNEAQQKSDL
• AADTYKEFER AYIPEGQRYS -IQNAQAAFC
  FSETIPAPTG KEEAQQRTDM
• AADTYKEFER AYIPEGQRYS -IQNAQAAFC
  FSETIPAPTG KEEAQQRTDM
• AADTYKEFER AYIPEGQRYS -IQNAQAAFC
  FSETIPAPTG KDEAQQRSDM
• AADTYKEFER AYIPEGQRYS -IQNAQAAFC
  FSETIPAPTG KDEAQQRSDV
• AFDTYQEFEE AYIPKEQKYS FLQNPQTSLC
  FSESIPTPSN REETQQKSNL

16
Phylip Format (Sequential)

• 3 100
• Rat
• ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGTTAATGGCCG
• TGGTGGCTGGAGTGGCCAGTGCCCTGGCTCACAAGTACCACTAA
• Mouse
• ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGTCTCTTGCCT
• TGGGGAAAGGTGAACTCCGATGAAGTTGGTGGTGAGGCCCTGGG
• Rabbit
• ATGGTGCATCTGTCCAGT---GAGGAGAAGTCTGCGGTCACTGC
• TGGGGCAAGGTGAATGTGGAAGAAGTTGGTGGTGAGGCCCTGGG

17
Mega Format

• mega
• TITLE No title
• Rat ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGT
• Mouse ATGGTGCACCTGACTGATGCTGAGAAGGCTGCTGT
• Rabbit ATGGTGCATCTGTCCAGT---GAGGAGAAGTCTGC
• Human ATGGTGCACCTGACTCCT---GAGGAGAAGTCTGC
• Oppossum ATGGTGCACTTGACTTTT---GAGGAGAAGAACTG
• Chicken ATGGTGCACTGGACTGCT---GAGGAGAAGCAGCT
• Frog ---ATGGGTTTGACAGCACATGATCGT---CAGCT

18
Progressive Multiple Alignment

• Heuristic
• Perform pairwise alignments
• Align sequences to alignments or alignments to
  existing alignments (profile alignments
• Do the alignments in some sensible order

19
Iterative methods

• Several progressive alignment methods can be
  iterated
• e.g. Barton-Sternberg, ClustalX

20
ClustalX Algorithm

• Perform pairwise alignments and calculate
  distances for all pairs of sequences
• Construct guide tree (dendrogram) joining the
  most similar sequences using Neighbour Joining
• Align sequences, starting at the leaves of the
  guide tree. This involves the pair-wise
  comparisons as well as comparison of single
  sequence with a group of seqs (Profile)

21

• ClustalX is not optimal
• There are known areas in which ClustalX performs
  badly e.g.
• errors introduced early cannot be corrected by
  subsequent information
• alignments of sequences of differing lengths
  cause strange guide trees and unpredictable
  effects
• edges ClustalX does not penalise gaps at edges
• There are alternatives to ClustalX available

22
Using ClustalX

• Start with sequences in FASTA format (or an
  existing alignment in Clustal format
• Do Alignment on the alignment menu

23
(No Transcript)
24
ClustalX Parameters

• Scoring Matrix
• Gap opening penalty
• Gap extension penalty
• Protein gap parameters
• Additional algorithm parameters
• Secondary structure penalties

25
Score Matrices

• Pairwise matrices and multiple alignment matrix
  series
• PAM (Dayhoff), BLOSUM (Hennikof), GONNET
  (default), user defined
• Transition (Alt-gtG)/Transversion (Clt-T) ratio
  low for distantly related sequences

26
Gap Penalties

• Linear gap penalties Affine gap penalties
• p (o l.e)
• Gap opening
• Gap extension
• Protein specific penalties (on by default)
• Increase the probability of gaps associated with
  certain residues
• Increase the chances of gaps in loop regions (gt 5
  hydrophilic residues)

27
Algorithm parameters

• Slow-accurate pair-wise alignment
• Do alignment from guide tree
• Reset gaps before aligning (iteration)
• Delay Divergent sequences ()

28
Additional displays

• Column Scores
• Low quality regions
• Exceptional residues

29
Multiple Alignment Strategies

• Align pairs of sequences using an optimal method
• Choose representative sequences to align
  carefully
• Choose sequences of comparable lengths
• Progressive alignment programs such as ClustalX
  for multiple alignment
• Progressive alignment programs may be combined
• Review alignment by eye and edit

30
Alignment of coding regions

• Nucleotide sequences much harder to align
  accurately than proteins
• Protein coding sequences can be aligned using the
  protein sequences
• e.g. BioEdit toggle translation to amino acid,
  call clustalw to align, edit alignment by hand,
  toggle back to nucleotide
• In-frame nucleotide alignments can be used, e.g.
  to determine non-synonymous and synonymous
  distances separately

31
Multiple Alignments and Phylogenetic Trees

• You can make a more accurate multiple sequence
  alignment if you know the tree already
• A good multiple sequence alignment is an
  important starting point for drawing a tree
• The process of constructing a multiple alignment
  (unlike pair-wise) needs to take account of
  phylogenetic relationships

32
Editing a multiple sequence alignment

• It is NOT fraud to edit a multiple sequence
  alignment
• Incorporate additional knowledge if possible
• Alignment edititors help to keep the data
  organised and help to prevent unwanted mistakes

33
Alignment Editors

• e.g. GDE, Bioedit, Seaview, Jalview etc.
• Alignment editors can function as an
  organisational tool (analyses tools on BioEdit)
• Construct sub-sequences (GDE, Seaview)
• Annotate sequences (Seaview)

34
Aligning weakly similar sequences
35
Sequence contains conserved regions

• e.g. DIALIGN (Morgenstern, Dress, Werner)
• re-aligns regions between conserved blocks
• http//bibiserv.techfak.uni-bielefeld.de/
• useful if sequences contains consistent conserved
  blocks
• Block Maker searches for conserved words that
  may be inconsistent http//blocks.fhcrc.org/

36
Profile Alignment

• Gribskov et al. 1987
• Position specific scores
• Allows alignment of alignments
• Gaps introduced as whole columns in the separate
  alignments
• Optimal alignment in time O(a2l2)
• a alphabet size, l sequence length
• Information about the degree of conservation of
  sequence positions is included

37
Good reasons to use profile alignments

• Adding a new sequence to an existing multiple
  alignment that you want to keep the same(align
  sequence to profile)
• Searching a database for new members of your
  protein family(pfsearch)
• Searching a database of profiles to find out
  which one your sequence belongs to(pfscan)
• Combining two multiple sequence
  alignments(profile to profile)

38
Profile Alignment Using ClustalX

• Profile Alignment Mode
• Align sequence to profile
• Align profile 1 to profile 2
• Secondary structure parameters

39
(No Transcript)
40
Profile searching using PSI-BLAST

• Position Specific Iterative
• Perform search construct profile perform
  search
• Convergence (hopefully)
• Increased sensitivity for distantly related
  sequences
• Available on-line (NCBI)

41
Databases of Aligned Sequences

• Hovergen http//pbil.univ-lyon1.fr/databases/hover
  gen.html (vertebrate alignments)
• Pfam http//www.sanger.ac.uk/Software/Pfam/
  (protein domain alignments and profile HMMs)
• BLOCKS http//blocks.fhcrc.org/
• Ribosomal Database Project http//rdp.cme.msu.edu/
  html/ alignments and trees derived from rRNA
  sequences
• Interpro combines information from other
  sources
• Many more

42
Probabilistic Models of Sequence Alignment

• Hidden Markov Models
• sequence of states and associated symbol
  probabilities
• Produces a probabilistic model of a sequence
  alignment
• Align a sequence to a Profile Hidden Markov Model
• Algorithms exist to find the most efficient
  pathway through the model

43

• Markov Chain A chain of things. The
  probability of the next thing depends only on the
  current thing
• Hidden Markov Model A sequence of states which
  form a Markov Chain. The states are not
  observable. The observable characters have
  emission probabilities which depend on the
  current state.