Sequence Alignment Techniques

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• Sequence Alignment Techniques

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In this presentation

• Part 1 Searching for Sequence Similarity
• Part 2 Multiple Sequence Alignment

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Part1

• Searching for Sequence Similarity

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Sequence similarity searches

• Sequence similarity searches of database enable
  us to extract sequences that are similar to a
  query sequence
• Information about these extracted sequences can
  be used to predict the structure or function of
  the query sequence
• Prediction using similarity is a powerful and
  ubiquitous idea in bioinformatics. The
  underlying reason for this is molecular evolution

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Sequence alignment

• Any pair of DNA sequence will show some degree of
  similarity
• Sequence alignment is the first step in
  quantifying this in order to distinguish between
  chance similarity and real biological
  relationships
• Alignments show the differences between sequences
  and changes (mutations), insertions or deletions
  (indels or gaps) and can be interpreted in
  evolutionary terms

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Alignment algorithms

• Dynamic programming algorithms can calculate the
  best alignment of two sequences
• Well-known variants are
• the Smith-Waterman algorithm (local alignments)
• the Needleman-Wunsch algorithm (global
  alignments)
• Local alignments are useful when sequences are
  not related over their full lengths, e.g.,
  proteins sharing only certain domains or DNA
  sequences related only in exons

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Alignment scores and gap penalties

• A simple alignment score measures the number or
  proportion of identically matching residues
• Gap penalties are subtracted from such scores to
  ensure that alignment algorithms produce
  biologically sensible alignments without many
  gaps
• Gap penalties may be constant (independent of the
  length of the gap), proportional (proportional to
  the length of the gap) or affine (containing gap
  opening and gap extension contributions)
• Gap penalties can be varied according to the
  desired application

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Similarity and homology

• Similarity may exist between any sequences
• Sequences are homologous only if they have
  evolved from a common ancestor
• Homologous sequences often have similar
  biological functions (orthologs), but the
  mechanism of gene duplication allows homologous
  sequences to evolve different functions (paralogs)

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Similarity search in databases

• Sequences similar to a query can be found in a
  database by aligning it to each database sequence
  in turn and returning the highest scoring (most
  similar) sequences
• This can be achieved by dynamic programming
  algorithms but in practice faster approximate
  methods are often used

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Statistical scores

• The p value of a similarity score is the
  probability of obtaining a score at least as high
  in a chance similarity between two unrelated
  sequences of similar composition
• Low p values indicate significance matches that
  are likely to have real biological significance
• The related E value is the expected frequency of
  chance occurrences scoring at least as high as
  the identified similarity
• A low p value for a similarity between two
  sequences can translate into a high E value for a
  search of a large database

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Sensitivity and specificity

• These measures quantify the success of a database
  search strategy
• Sensitivity measures the proportion of real
  biological sequence relationships in the database
  that were detected as hits in the search
• Specificity is the proportion of the hits
  corresponding to real biological relationships
• Changing E and p value thresholds results in a
  trade-off between these complementary measures of
  success

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Maximizing amino acid identities

• Protein sequences can be aligned to maximize
  amino acid identities, but this will not reveal
  distant evolutionary relationships

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Evolution

• Protein-coding sequences evolve slowly compared
  with most other parts of the genome, because of
  the need to maintain protein structure and
  function
• An exception to this is the fast evolution that
  might occur in the redundant copy of a recently
  duplicated gene

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Allowed changes

• Changes in protein sequences during evolution
  tend to involve substitutions between amino acids
  with similar properties because these tend to
  maintain the structural stability of the protein

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Substitution score matrices

• These matrices give scores for all possible amino
  acid substitutions during evolution
• Higher scores indicate more likely substitutions
• Example matrices are BLOSUM62 and PAM250
• PAM stands for Accepted Point Mutations, and in
  this case, the evolutionary distance of the
  matrix is 250 amino acid changes per 100 residues
• Dynamic programming algorithms for sequence
  alignment can operate using scores from these
  matrices

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Significance of score matrices

• Substitution score matrices allow detection of
  distant evolutionary relationships between
  protein sequences
• It is possible to detect much more distant
  relationships by comparing protein sequences than
  by comparing nucleic acid sequences

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Part of the sequence of human Huntingtons
disease protein (Huntingtin) showing low
complexity regions (underlined) associated with
compositional bias towards glutamine (Q) and
proline (P)

• MATLEKLMKA FESLKSFQQQ QQQQQQQQQQ QQQQQQQQQQ
  PPPPPPPPPP PQLPQPPPQA QPLLPQPQPP PPPPPPPPGP
  AVAEEPLHRP KKELSATKKD RVNHCLTICE NIVAQSVRNS
  PEFQKLLGIA MELFLLCSDD AESDVRMVAD ECLNKVIKAL
  MSDNLPRLQL ELYKEIKKNG APRSLRAALW RFAELAHLVR
  PQKCRPYLVN LLPCLTRTSK RPEESVQETL AAAVPKIMAS

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A dot plot of human pleckstrin sequence against
itself produced with Erik Sonnhammers dotter
program. The sequence is plotted from N- to C-
terminus along horizontal and vertical axes
between residues 1 and approximately 350.
PLEK_HUMAN (horizontal) vs. PLEK_HUMAN (vertical)
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The PAM250 matrix and alignment of sequences.
Total alignment scores for two matrices should
not be compared, but note that the PAM matrix is
able to detect a much better alignment in second
halves of these sequences rather than identity
matrix. With the introduction of a single gap,
sensible alignments of hydrophobic amino acids,
and alignment of K with R (both basic), D with E
(both acidic) and F with Y (both aromatic) can be
seen
C 12 S 0 2 T 2 1 3 P 1 1 0 6 A 2 1 1
1 2 G 3 1 0 1 1 5 N 4 1 0 1 0 0 2 D
5 0 0 1 0 1 2 4 E 5 0 0 1 0 0 1 3
4 Q 5 1 1 0 0 1 1 2 2 4 B 3 1 1 0
1 2 2 1 4 3 6 R 4 0 1 0 2 3 0 1 1
1 2 5 K 5 0 0 1 1 2 1 0 0 1 0 3
5 M 5 2 1 2 1 3 2 3 2 1 2 0 0 6 I
3 1 0 2 1 3 2 2 2 -2 -2 -2 2 2 5 L 6
3 2 3 2 4 3 4 3 -2 -2 3 3 4 2 6 V 2
3 0 1 0 1 2 2 4 -2 -2 2 2 2 4 2 4 F
4 3 3 5 4 5 4 6 5 -5 2 4 5 0 1 2
1 9 Y 0 3 3 5 3 5 2 4 4 4 0 4 4 2
1 1 2 7 10 W 8 2 5 6 6 7 4 7 7 5 3
2 3 4 5 5 6 0 0 17 C S T P A G N
D E Q H R K M I L V F Y W
Sequence 1 MIIVKP VVLKGDFG Sequence 2
MILLKP AIIIRAEY- Position score 656256 044231370
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Figure 3. Display of the DNA unit. DNA can be
described at several levels of detail. At the
most detailed level, DNA can be characterized by
the 5' and 3' termini at both external and
internal positions at the most abstract level,
the substrate DNA can be one of 16 common
structures. The goal is to provide methods for
specifying the properties of DNA in as many ways
as is natural for a scientist.
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Figure 7. An initial experimental environment.
The temperature is 37 degrees Celsius and the pH
value is 7.4. No DNA polymerase I activity is
possible
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Part2

• Multiple Sequence Alignment

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Non specific sequence similarity

• Certain types of sequence similarity are less
  likely to be indicative of an evolutionary
  relationship than others are
• Examples of this are similarity between regions
  of low compositional complexity, short period
  repeats and protein sequences coding for generic
  structures like coiled coils

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Similarity search filters

• Regions of the non specific sequence types can
  degrade the results of similarity searches and
  are often filtered out of query sequences prior
  to searching
• The programs SEG and DUST can be used to detect
  and filter low complexity sequences, XNU can
  filter short period repeats and COILS can detect
  the presence of potential coiled coil structures

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Database types for searches

• Database and query sequences can be protein or
  nucleic acid sequences and different query
  strategies are required for different types and
  combinations
• In general, searches are more sensitive using
  strategies where protein-coding nucleic acid
  database and/or query sequences are first
  translated to protein sequences

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Iterative database searches

• PSI-BLAST is an iterative search method that
  improves on the detection rate of BLAST and FASTA
• Each iteration discovers intermediate sequences
  that are used in a sequence profile to discover
  more distant relatives of the query sequence in
  subsequent iterations
• Potential problems with PSI-BLAST are associated
  with the potential for unrelated sequences to
  pollute the iterative search, and difficulties
  associated with the domain structure of proteins
• PSI-BLAST often detects up to twice as many
  evolutionary relationships as BLAST

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Multiple sequence alignment

• Multiple alignment illustrates relationships
  between two or more sequences
• When the sequences involved are diverse, the
  conserved residues are often key residues
  associated with maintenance of structural
  stability or biological function
• Multiple alignments can reveal many clues about
  protein structure and functions

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Multiple alignment
Part of a (artificial) multiple alignment of a
family consisting of 7 sequences, which subdivide
into 3 subfamilies. The bars on the left indicate
subfamilies the dotted boxes highlight
conservation patterns.
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Progressive sequence alignment

• Most commonly used software uses the method of
  progressive alignment
• This is a fast method, but frozen-in errors mean
  that it does not always work perfectly
• Biological knowledge can provide information
  about likely alignments, and where automatically
  produced alignments turn out to be imperfect,
  software for manual alignment editing is required

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Protein families

• Assigning sequences to protein families is a very
  valuable way of predicting protein family
  (consensus sequences, conserved residues, residue
  patterns, sequence profiles, etc.)
• Many ways have been developed to represent
  protein family information and these have been
  stored in secondary protein family databases

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Consensus sequences

• These condenses the information from a multiple
  alignment into single sequence
• Their main shortcoming is the inability to
  represent any probabilistic information apart
  from the most common residue at a particular
  position
• Derivation of consensus sequence illustrates that
  any protein family representation is subject to
  bias if the set of sequences from which it was
  derived is biased

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PRINTS and BLOCKS

• These represent protein families of multiply
  aligned ungapped segments (motifs) derived from
  the most highly conserved regions of sequences
• By representing more of the sequence, they have
  the potential to be more sensitive than short
  PROSITE patterns
• The ability to match in only a subset of the
  motifs associated with a particular family means
  that they have the ability to detect splice
  variants and sequence fragments and to represent
  subfamilies
• WWW-based search engines for the databases are
  available

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Protein domain families

• Many proteins are built up from domains in a
  modular architecture
• The study of protein families is best pursued as
  a study of protein domain families
• Prodom is a database of protein domain sequences
  created by automatic means from the protein
  sequence databases

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Resources for domain families

• Pfam and SMART can be used for protein domain
  family analysis
• The integrated resource Interpro unites PROSITE,
  PRINTS, Pfam, Prodom and SMART

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Visualization of similarities

• Dot plots are a very good way to visualize
  sequence similarity and find repeats

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