DNA Properties and
Genetic Sequence Alignment
CSE, Marmara University
mimoza.marmara.edu.tr/~m.sakalli/cse546
Oct/19/09
Computational
Molecular
Biology
Bioinformatics
Genomics
Proteomics
Functional
genomics
Structural
bioinformatics
Sequence Alignment and Why
Global Alignment
Local Alignment
Suppose a cloned gene
If it is already in databases. Database Accession, Annotation
(summary of structure), expression profile? Mutants?
Its protein characteristics? -Sub-localization -Soluble? -3D fold
Is there conserved regions?-Alignments?-Domains?
Is there similar sequences?-% identity?-Family member?
Evolutionary relationship?-Phylogenetic tree
Scoring Matrices
Alignment with Affine Gap Penalties
Applying algorithms to analyze genomics data
Applying Manhattan Tourist Problem to sequence comparison
Local vs. Global Alignment
Global Alignment
--T—-CC-C-AGT—-TATGT-CAGGGGACACG—A-GCATGCAGA-GAC
| || | || | | | |||
|| | | | | ||||
|
AATTGCCGCC-GTCGT-T-TTCAG----CA-GTTATG—T-CAGAT--C
Local Alignment—better alignment to find conserved
tccCAGTTATGTCAGgggacacgagcatgcagagac
segment
||||||||||||
aattgccgccgtcgttttcagCAGTTATGTCAGatc
Local alignment
Global alignment
a “mini” Global
Alignment to get
Local
Some genes only have small conserved regions between species of organisms
Example: Homeobox genes have a short region called the homeodomain that is highly
conserved between species. A global alignment would not find the homeodomain
because it would try to align the ENTIRE sequence.
Find long ORFs, the longest ORFs first and putting together a set with
minimal overlaps, identify the furthest upstream start codon.
A new found ORF might not contain a real gene. Compare it with a gene
known from other species.
Different DNA sequences will give identical proteins (remember from the
codon table). So there is large amount of redundancy in DNA sequence.
Conservation of a sequence between species strongly suggests that the
sequence has a function that is being conserved by natural selection.
A protein being functional is more likely being conserved in evolutionary
process than DNA. The organism’s survival depends on the protein
being functional.
The protein 3-dimensional structure is even more conserved, because it is
more closely related to enzyme activity than the amino acid sequence is.
Research subjects: To build 3-D structure from a DNA
sequence
Sequence Comparison
Comparing ORF sequence to
a database of known
protein sequences from
many species.
BLAST is the standard
(BLAST = Basic Local
Alignment Search Tool)
BLAST is based on the
concept that if you
compare the same (that
is, homologous) protein
from many different
species.
- Some amino acids readily
substitute for each other
and some others almost
are unique and never will
substitute.
A substitution matrix, giving a
score for each amino acid
position in the proteins
being compared
These weights are given based on biological evidence.
Alignments can be thought of as mutations in the sequence.
Some of these mutations have little effect on the organism’s
function, therefore some penalties, δ(vi , wj), will be less
harsh than others.
Terminology: query sequence: sequence entered. Sequences matching
are subject sequences.
Gene B. meg. It is 174 amino acids lon, written in “fasta” format:
Starts with > and immediately followed by an identifier
(ORF00135), and then some comments. And then follows the
sequence.
Scoring matrices:
Scoring matrices are created based on biological evidences.
Alignments can be thought of as two sequences that differ due to
mutations in the sequence.
Some of these mutations have little effect on the organism’s function,
therefore some penalties, δ(vi , wj), will be less harsh than others.
Different amino acids might have a positive score. Ie due to having
positively charged.
Score S is calculated by summing the scores assigned for matches,
mismatches and gaps (creation/extension scores).
The scores are given by the specified substitution matrix.
PAM (Percent Accepted Mutation): for evolutionary studies.
For example in PAM1, 1 accepted point mutation per 100 amino acids
is required.
BLOSUM (BLOcks amino acid SUbstitution Matrix): for finding
common motifs. For example in BLOSUM62, the alignment is
created using sequences sharing no more than 62% identity.
How the matrices were created:
Very similar sequences were aligned.
From these alignments, the frequency of substitution between each
pair of amino acids was calculated and then PAM1 was built.
After normalizing to log-odds format, the full series of PAM matrices
can be calculated by multiplying the PAM1 matrix by itself.
>ORF00135 |chromosome
538197-538721 revcomp
MKAKLIQYVYDAECRLFKSVNQH
FDRKHLNRFLRLLTHAGGATFTI
VIACLLLFLYPSSVAYACAFSLA
VSHIPVAIAKKLYPRKRPYIQLK
HTKVLENPLKDHSFPSGHTTAIF
SLVTPLMIVYPAFAAVLLPLAVM
VGISRIYLGLHYPTDVMVGLILG
IFSGAVALNIFLT
- Some matrices reflect similarity: good for database searching
- Some reflect distance: good for phylogenies
- Log-odds matrices, a normalization method for matrix values:
Sij = log (qij/(pi pj))=log (qij) – log (pi) – log (pj)
Sij is the probability that two residues, i and j, are aligned by evolutionary descent
and by chance.
qij are the frequencies that i and j are observed to align in sequences known to be
related.
pi and pj are their frequencies of occurrence in the set of sequences.
The most widely used local similarity algorithms are:
Smith-Waterman (http://www.ebi.ac.uk/MPsrch/)
Basic Local Alignment Search and Fast Alignment, which are based on k-tuple
algorithms…
Speedwise: BLAST > FASTA > Smith-Waterman (It is VERY SLOW and uses a lots
of computational power
Sensitivity/statistics:
FASTA is more sensitive to variations, misses less homologues
Smith-Waterman is even more sensitive.
BLAST calculates probabilities
FASTA more accurate for DNA-DNA search then BLAST
BLAST and FASTA variants
Comparison between,
FASTA: a DNA query to DNA db, or a protein query to protein db
FASTX: a translated DNA query to a protein database
TFASTA: a protein query to a translated DNA database
BLASTN: a DNA query to DNA database.
BLASTP: a protein query to protein database.
BLASTX: 6-frame translations of DNA query to protein database.
TBLASTN: a protein query to the 6-frame translations of a DNA database.
TBLASTX: 6-frame translations of DNA query to the 6-frame translations of a DNA database.
PSI-BLAST: Performs iterative database searches. The results from each round are incorporated into a 'position
specific' score matrix, which is used for further searching
BLAST results
Detailed BLAST results
E value: is the expectation value or probability to find by chance hits similar to
your sequence. The lower the E, the more significant the score.
Mostly genes are named with the function of their protein.at some
point, some related genes had their function determined through
lab work: by examining the effects of mutations in the gene, by
isolating and studying the protein produced by the gene, etc.
Enzymes (end in –ase), transport across the cell membrane, genetic
information processing (DNA->RNA->protein), structural proteins,
sporulation and germination, and more!
Many genes (maybe 1/4 of them in a typical genome) have no known
function, although they are found in several different species:
conserved hypothetical genes
Every new genome has some genes that are unique: no matching
BLAST hits in the database.
Are they real genes? Sometimes there is evidence in the form of
messenger RNA, but usually we don’t know call them
hypothetical genes
“putative” means that we think we know the gene’s function but we
aren’t sure. Putative should be followed by the function name.
(Basic Similarity) Homology Search,
The Knuth-Morris-Pratt Algorithm (exact match)
All similarity searching methods rely on the concepts of alignment and a
distance measurements between.
Associated with a similarity score calculated from a distance matrix for
example the number of DNA base that are different between.
Exact String Matching
Naive brute force algorithm searching for pattern p in text t, lengths m=|p|
and n=|t|, then the worst case complexity Θ(nm) . Sliding the pattern across
from left to right, one step at a time, but it is possible to shift more than
one, given a certain length of the pattern sought is detected in the text.
Just imagine it..
The Knuth-Morris-Pratt Algorithm - Complexity
The algorithm of KMP algorithm takes into account the information gained
during previous symbol comparisons, with prefix and suffix definitions.
Never re-compares a text symbol that has matched a pattern symbol.
The complexity of the searching phase is in O(n).
Its preprocessing stage (the pattern search) has a complexity of O(m). m<=n.
Therefore overall complexity is in O(n).
The Knuth-Morris-Pratt Algorithm - Definitions
Definition: Let A be an alphabet, and x be a string of length k, x=x_0, x_1, …, x_(k) over A.
A prefix of x, is a substring u, u=u_0, u_1, …,u_b, where b<k, 0<={b,k},
A suffix of x, is a substring u, u=u_{k-b}, u_{k-b+1}, …, u_{k}, where 0<b<=k.
Called proper prefix or suffix of u, if b<k.
A border of x is a substring which appears as a proper suffix and a proper prefix of x and its
length is b.
Example x=abacab. Proper Prefixes: є, a, ab, aba, abac, abaca. Proper Suffixes: є, b, ab,
cab, acab, bacab. Borders: є, ab, with lengths of 0 and 2.
The empty string є is always a border of x, but itself has no border.
0123456789...
abcabcabd
abcabd
abcabd
Matching prefix p=abcab, length 5, mismatch occurs at position 5 between c and b, the
widest (border length) - of the prefix of p matching to suffix of p -, b=2, then the shift
distance is |p|-|b|=5-2.
Theorem: Let r, s be borders of a string x, where |r| < |s|. Then r is a border of s.
Proof.
The Knuth-Morris-Pratt Algorithm – Preprocessing generating a look-up
table
Definition: Let x be a string and a A a symbol. A border r of x can be extended
by a, if ra (r appended a) is a border of xa.
j:
0123456
p[j]:
ababaa
b[j]:
-1001231
void kmpPreprocess()
{
int i=0, j=-1;
b[i] = j;
while (i<m)
{
while (j>=0 && p[i]!=p[j]) j=b[j];
i++; j++;
b[i] = j;
}
}
//O(m) length of the pattern
//initial j=-1, i=0; j<i..
//if mismatch, reduce j, max reduction until j<0
//if increase with..
The Knuth-Morris-Pratt Algorithm – Searching
void kmpSearch()
{
int i=0, j=0;
while (i<n)
//Text length
{
while (j>=0 && t[i]!=p[j]) j=b[j];
i++; j++;
if (j==m)
//m pattern length
{
report(i-j);
j=b[j];
}
}
}
Example:
0123
4567
abab
baba
abab
ac
ab
abac
abab
ab
//if mismatch, max reduction to –j;
//max j can be m, hits to a match
89...
a
ac
ab
ab
//border length b[j] lookup
ac
abac
Max number of comparisons 2n.
j:
0123456
p[j]:
ababaa
b[j]:
-1001231
Boyer-Moore algorithm
Starts comparison from the rightmost, and if the rightmost does not match and if not occurs in
the pattern at all, then the pattern can be shifted by m.
The Boyer-Moore algorithm uses two different heuristics for determining the maximum possible
shift distance in case of a mismatch: the "bad character" and the "good suffix" heuristics.
Both heuristics can lead to a shift distance of m.
0 1 2 3 4 5 6 7 8 9 ...
0 1 2 3 4 5 6 7 8 9 ...
abbadabacba
aabababacba
babac
abbab
babac
a b b a b //borders matching
Occurrence Function. occ(p, a) = max{ j | pj = a}, occ(text, x) = 2, occ(text, t) = max{0, 3}=3.
The pattern is shifted by the longest of the two distances given by the bad chrtr and the good
suffix heuristics.
Left: Bad suffix character. Example of bad pattern heuristics (a special case of good suffix
heuristics). Needs preprocessing, borders..
Requires only O(n/m) comparisons, if always the first symbol mismatch occurs.
The preprocessing for the good suffix heuristics is rather difficult to understand and to
implement.
Therefore, modified versions of the BM algorithm in which the good suffix heuristics are
avoided. The argument is that the bad character heuristics would avoid comparisons, while
good suffix heuristics would not avoid.
Bad character heuristics, - the Horspool algorithm or the Sunday algorithm suit better.
Bad-character mismatch of BM, versus Horspool Algorithm, and
Sunday’s Algorithm
0 1 2 3 4 5 6 7 8 9 ...
0 1 2 3 4 5 6 7 8 9 ...
0 1 2 3 4 5 6 7 8 9 ...
abcabdbacba
abcabdbacba
abcabdbacba
bcbab
bcbab
bcbab
bcbab
bcbab
bcbab
the rightmost position of a in p0 ... pm-2, or -1, occ(text, x) = 2, occ(text, t) = 0,
occ(next, t) = -1.
Occurrence at the leftmost (last bit) is not taken into count, best case
performance O(n/m)
On the Sunday’s shifts left of the d, since d does not occur in the pattern, and
the comparison can be depend on the symbol probabilities, if known, then
the least probable symbol in the pattern is compared first, hoping that it
does not match, for the pattern be shifted.
Skip Search Algorithm searches least likely pattern!!!..
Minimize the false positive matches.
Information theoretic approach: Repetitive subsequences (ie poly AT) has low
information content, random..
Minimum Message Length, (MML).. uses HMM (or PFSM)..
Next week: Aligning Two Strings
www.bioalgorithms.info\Winfried Just
Represents each row and each column with a number and a symbol of
the sequence present up to a given position. For example the
sequences are represented as:
Alignment as a Path in the Edit
Graph
0 1
A
A
0 1
2
T
T
2
2
_
C
3
3
G
G
4
4
T
T
5
5
T
_
5
6
A
A
6
7
T
_
6
7
_
C
7
(0,0) , (1,1) , (2,2), (2,3),
(3,4), (4,5), (5,5), (6,6),
(7,6), (7,7)