Lecture 4
Sequence alignment and searching
Sequence alignment
What is the purpose of sequence alignment?
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Identification of homology and homologous sites in
related sequences
Inference of evolutionary history that lead to the
differences in observed sequences
Types of comparisons and alignment methods
According to
sequence
According to Coverage:
number od
sequences:
LOCAL
GLOBAL
TWO SEQUENCES
(Pairwise alignment)
Database seqrch against
query sequences
 BLAST algorithm
Comparoson of two
sequences;
First step in multiple
alignment
THREE OR MORE
SEQUENCES
(Multiple alignment)
Defining consensus
sequences, protein structural
motifs and domains,
regulatory elements in DNA
etc.
Determination of conserved
residues and domains;
Introductory step in mplecular
phylogenetic analysis
Introduction to sequence alignment
Given two text strings:
First string = a b c d e
Second string = a c d e f
a reasonable alignment would
be
a b c d e a - c d e f
For the sequences gctgaacg
and ctataatc:
An uninformative alignment:
-------gctgaacg
ctataatc-------
An alignment without gaps
gctgaacg
ctataatc
An alignment with gaps
We must choose criteria so
that algorithm can choose
the best alignment.
gctga-a--cg
--ct-ataatc
And another
gctg-aa-cg
-ctataatc-
Source: Lesk, Introduction to Bioinformatics
The dotplot (1)
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A simple picture that gives an overview of the similarities
between two sequences
Dotplot showing identities between short name (DOROTHYHODGKIN) and
full name (DOROTHYCROWFOOTHODGKIN)of a famous protein
crystallographer:
Letters corresponding to isolated
matches are shown in non-bold
type. The longest matching
regions, shown in boldface, are
the first and last names DOROTHY
and HODGKIN. Shorter matching
regions, such as the OTH of
dorOTHy and
crowfoOTHodgkin, or the RO of
doROthy and cROwfoot, are
noise.
Source: Lesk, Introduction to Bioinformatics
The dotplot (2)
Dotplot showing identities
between a repetitive sequence
(ABRACADABRACADABRA) and
itself. The repeats appear on
several subsidiary diagonals
parallel to the main diagonal.
Source: Lesk, Introduction to Bioinformatics
The dotplot (3)
Dotplot showing identities
between the palindromic
sequence MAX I STAY
AWAY AT SIX AM and
itself. The palindrome
reveals itself as a stretch
of matches perpendicular
to the main diagonal.
Source: Lesk, Introduction to Bioinformatics
The dotplot (4)
Source: Lesk, Introduction to Bioinformatics
Dotplots and sequence alignments
Any path through the
dotplot from upper left to
lower right passes through a
succession of cells, each of
which picks out a pair of
positions, one from the row
and one from the column,
that correspond in the
alignment; or that indicates
a gap in one of the
sequences. The path need
not pass through filled-in
points only. However, the
more filled-in points on the
diagonal segments of the
path, the more matching
residues in the alignment.
Corrseponding alignment:
DOROTHY--------HODGKIN
DOROTHYCROWFOOTHODGKIN
Source: Lesk, Introduction to Bioinformatics
Measures of sequence similarity
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Two measures of distance between two character strings:
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agtc
cgta
The Hamming distance, defined between two strings of equal
length, is the number of positions with mismatching characters.
The Levenshtein, or edit distance, between two strings of not
necessarily equal length, is the minimal number of 'edit operations'
required to change one string into the other, where an edit
operation is a deletion, insertion or alteration of a single character in
either sequence. A given sequence of edit operations induces a
unique alignment, but not vice versa.
Hamming distance = 2
ag-tcc
cgctca Levenshtein distance = 3
Source: Lesk, Introduction to Bioinformatics
Scoring schemes
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In molecular biology, certain changes are more likely to occur
than others
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Amino acid substitutions tend to be conservative
In nucleotide sequences, transitions are more frequent than
transversions
-> We want to give different weights to different edit
operations
Hamming and Lenenshtein distance are measues of
dissimilarity. In MB it is more common to measure similarity
(higher similarity = higher score)
Example: a DNA substitution matrix:
Source: Lesk, Introduction to Bioinformatics
a
g
c
t
a
20
10
5
5
g
10
20
5
5
c
5
5
20
10
t
5
5
10
20
Observed number of substitutions
Start frequencies for
a genetic matrix
(upper triangular matrix)
Expected number of random mutation events
Start frequencies for
PAM matrices
(lower triangular matrix)
Derivation of substitution matrices
PAM Matrices
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First substitution matrices widely used
Based on the point-accepted-mutation (PAM)
model of evolution (Dayhoff, 1978)
PAMs are relative measures of evolutionary
distance
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1 PAM = 1 accepted mutation per 100 AAs
Does not mean that after 100 PAMs every AA will
be different? Why or why not?
To view and compare different substitution matrices, visit
http://eta.embl-heidelberg.de:8000/misc/mat/
PAM Matrices
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Derived from global alignments of closely related
sequences.
Matrices for greater evolutionary distances are
extrapolated from those for lesser ones.
The number with the matrix (PAM40, PAM100) refers
to the evolutionary distance; greater numbers are
greater distances.
Does not take into account different evolutionary rates
between conserved and non-conserved regions.
BLOSUM Matrices
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Henikoff, S. & Henikoff J.G. (1992)
Use blocks of protein sequence fragments from different families
(the BLOCKS database)
Amino acid pair frequencies calculated by summing over all
possible pairs in block
Different evolutionary distances are incorporated into this scheme
with a clustering procedure (identity over particular threshold =
same cluster)
Similar idea to PAM matrices
Probabilities estimated from blocks of sequence fragments
Blocks represent structurally conserved regions
BLOSUM Matrices
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Target frequencies are identified directly instead of
extrapolation.
Sequences more than x% identitical within the block where
substitutions are being counted, are grouped together and
treated as a single sequence
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BLOSUM 50 : >= 50% identity
BLOSUM 62 : >= 62 % identity
To view and compare different substitution matrices, visit
http://eta.embl-heidelberg.de:8000/misc/mat/
BLOSUM Matrices
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Derived from local, ungapped alignments of distantly
related sequences
All matrices are directly calculated; no extrapolations are
used – no explicit model
The number after the matrix (BLOSUM62) refers to the
minimum percent identity of the blocks used to construct
the matrix; greater numbers are lesser distances.
The BLOSUM series of matrices generally perform better
than PAM matrices for local similarity searches (Proteins
17:49).
How do you prefer it: done right or done fast?
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Exact methods - the result is guaranteed to be
(mathematically) optimal
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Pairwise alignment
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Multiple alignment
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Needleman-Wunsch (global)
Smith-Waterman (local)
Rarely used; quickly becomes computationally intractable with
increasing number of sequences and/or their length
Heuristic methods: make some assumptions that hold
most, but not all of the time
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Pairwise alignment – BLAST
Multiple alignment – all widely used multiple alignment
programs (ClustalW, TCofee…)
Exact pairwise methods: Dynamic programming
Equivalence matrix
Cumulative matrix
(can be computed from Cor N-terminus)
An example of a cumulative
matrix (computed from Cterminus) with traces of
possible alignments)
What does it mean?
Principle of reduction of number
of paths that need to be
examined:
If a path from X->Z passes through
Y, the best path from X->Y is
independent of the best path from
Y->Z
Source: Lesk, Introduction to Bioinformatics
BLAST – the workhorse of bioinformatcs
http://www.ncbi.nlm.nih.gov/BLAST
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BLAST = Basic local
alignment search tool
When you have a
nucleotide or protein
sequence that you
want to search
against sequence
databases
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to determine what
the sequence is
to find related
sequences
(homologs)
Bioinformatics
researchers use
standalone version of
BLAST, run from the
command line or
other programs
Different BLAST programs
BLAST algorithm principles
(Basic Local Alignment Search Tool)
Main idea:
1.
2.
query
Construct a dictionary of all the words in
the query
Initiate a local alignment for each word
match between query and DB
Running Time: O(MN)
However, orders of magnitude faster
than Smith-Waterman
DB
BLAST  Original Version
Dictionary:
All words of length k (~11)
Alignment initiated between
of alignment score  T
(typically T = k)
Alignment:
Ungapped extensions until score
below statistical threshold
Output:
All local alignments with score
> statistical threshold
……
query
words
……
scan
DB
query
BLAST  Original Version
A C G A A G T A A G G T C C A G T
k = 4,
T=4
The matching word GGTC
initiates an alignment
Extension to the left and right
with no gaps until alignment
falls < 50%
Output:
GTAAGGTCC
GTTAGGTCC
C C C T T C C T G G A T T G C G A
Example:
Gapped BLAST
A C G A A G T A A G G T C C A G T
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Pairs of words can initiate
alignment
Extensions with gaps in a
band around anchor
Output:
GTAAGGTCCAGT
GTTAGGTC-AGT
C T G A T C C T G G A T T G C G A
Added features:
Gapped BLAST
A C G A A G T A A G G T C C A G T
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Pairs of words can initiate
alignment
Nearby alignments are
merged
Extensions with gaps until
score < T below best score
so far
Output:
GTAAGGTCCAGT
GTTAGGTC-AGT
C T G A T C C T G G A T T G C G A
Added features:
BLAST: Example of sequence search
Query
sequence or
accession
number
Sequence
database
to search
BLAST: Example of sequence search
Please come
back later 
BLAST results: List of hits
E–value - number of unrelated databank sequences expected
to yield same or higher score by pure chance
BLAST results: High scoring pairs (HSPs)
Fundamental unit of the BLAST algorithm output
HSP (high scoring pair)
Aligned fragments of query and detected sequence
with similarity score exceeding a set cutoff value
E-value (Expectation)
Score = 61 (27.8 bits), Expect = 1.8e-65, Sum P(4) = 1.8e-65 Identities = 10/17
(58%), Positives = 16/17 (94%)
Query:
81 SGDLSMLVLLPDEVSDL 97
+GD+SM +LLPDE++D+
Sbjct: 259 AGDVSMFLLLPDEIADV 275
HSP
BLAST Confidence measures
Score and bit-score : depend on scoring method
E-value (Expect value) : number of unrelated database
sequences expected to yield same or higher score by pure
chance
P-value (Probability) : probability that database yields by pure
chance at least one alignment with same or higer score
The Expect value (E) is a parameter that describes the number of hits one can "expect" to see just
by chance when searching a database of a particular size. It decreases exponentially with the
Score (S) that is assigned to a match between two sequences. Essentially, the E value describes
the random background noise that exists for matches between sequences.
The Expect value is used as a convenient way to create a significance threshold for reporting
results. When the Expect value is increased from the default value of 10, a larger list with more
low-scoring hits can be reported. (E-value approching zero => significant alignment. Less
than 0,01 = almost always found to be homologous; 1e-10 for nucleotide searches of
1e-4 for protein searches= frequently related)
In BLAST 2.0, the Expect value is also used instead of the P value (probability) to report the
significance of matches. For example, an E value of 1 assigned to a hit can be interpreted as
meaning that in a database of the current size one might expect to see 1 match with a similar
score simply by chance.
Be careful when comparing E- or P-values
from different searches
Comparison is only meaningful for different query
sequences searched agains the same database with the
same BLAST parameters
Variants of BLAST
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MEGABLAST:
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Optimized to align very similar sequences
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PSI-BLAST:
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BLAST produces many hits
Those are aligned, and a pattern is extracted
Pattern is used for next search; above steps iterated
WU-BLAST: (Washington University BLAST)
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Works best when k = 4i  16
Linear gap penalty
Optimized, added features
BlastZ
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Combines BLAST/PatternHunter methodology
Good for local alignments of non-coding (regulatory) regions
IMPORTANT: When NOT to use BLAST
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A typical situation: you have a piece of human DNA sequence
and want to extend it or find what gene it belongs to.
In other words, you want an exact or near-exact match to a
sequence that is part of an assembled genome.
Genome browsers are equipped with very fast algorithms for
finding near-exact matches in genomic sequences:
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BLAT (with UCSC Genome Browser)
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Highly recommended: the BLAT paper (Kent WJ (2003)
Genome Res 12:656-64) - it is famous for its unorthodox
writing style 
SSAHA (with Ensembl)
BLAT: BLAST-like alignment tool
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Differences with respect to BLAST:
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Dictionary of the DB (BLAST: query)
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Initiate alignment on any # of matching hits (BLAST: 1 or 2 hits)
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More aggressive stitching-together of alignments
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Special code to align mature mRNA to DNA
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Result: very efficient for:
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Aligning many sequences to a DB
Aligning two genomes