Download Query sequence

Sequence database searching –
Homology searching
Dynamic Programming (DP) too slow for
repeated database searches.
Therefore fast heuristic methods:
• FASTA
Fast
heuristics
• BLAST and PSI-BLAST
• QUEST
• HMMER
• SAM-T98
Hidden Markov modelling
FASTA
• Compares a given query sequence with a library of
sequences and calculates for each pair the highest
scoring local alignment
• Speed is obtained by delaying application of the
dynamic programming technique to the moment
where the most similar segments are already
identified by faster and less sensitive techniques
• FASTA routine operates in four steps:
FASTA
Operates in four steps:
1. Rapid searches for identical words of a user specified length
occurring in query and database sequence(s) (Wilbur and
Lipman, 1983, 1984). For each target sequence the 10 regions
with the highest density of ungapped common words are
determined.
2. These 10 regions are rescored using Dayhoff PAM-250 residue
exchange matrix (Dayhoff et al., 1983) and the best scoring
region of the 10 is reported under init1 in the FASTA output.
3. Regions scoring higher than a threshold value and being
sufficiently near each other in the sequence are joined, now
allowing gaps. The highest score of these new fragments can be
found under initn in the FASTA output.
4. full dynamic programming alignment (Chao et al., 1992) over the
final region which is widened by 32 residues at either side, of
which the score is written under opt in the FASTA output.
FASTA output example
DE METAL RESISTANCE PROTEIN YCF1 (YEAST CADMIUM FACTOR 1). . . .
SCORES Init1: 161 Initn: 161 Opt: 162 z-score: 229.5 E(): 3.4e-06
Smith-Waterman score: 162; 35.1% identity in 57 aa overlap
test.seq
YCFI_YEAST
10
20
30
MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLE
:| :|::| |:::||:|||::|: |
CASILLLEALPKKPLMPHQHIHQTLTRRKPNPYDSANIFSRITFSWMSGLMKTGYEKYLV
180
test.seq
YCFI_YEAST
190
200
210
220
230
40
50
60
LSDIYQIPSVDSADNLSEKLEREWDRE
:|:|::|
|:::||:|||::|: |
EADLYKLPRNFSSEELSQKLEKNWENELKQKSNPSLSWAICRTFGSKMLLAAFFKAIHDV
240
250
260
270
280
290
FASTA
(1) Rapid identical word searches:
• Searching for k-tuples of a certain size within a
specified bandwidth along search matrix diagonals.
• For not-too-distant sequences (> 35% residue
identity), little sensitivity is lost while speed is greatly
increased.
• Technique employed is known as hash coding or
hashing: a lookup table is constructed for all words in
the query sequence, which is then used to compare all
encountered words in each database sequence.
FASTA
• The k-tuple length is user-defined and is usually 1 or
2 for protein sequences (i.e. either the positions of
each of the individual 20 amino acids or the positions
of each of the 400 possible dipeptides are located).
• For nucleic acid sequences, the k-tuple is 5-20, and
should be longer because short k-tuples are much
more common due to the 4 letter alphabet of nucleic
acids. The larger the k-tuple chosen, the more rapid
but less thorough, a database search.
BLAST
• blastp compares an amino acid query sequence
against a protein sequence database
• blastn compares a nucleotide query sequence
against a nucleotide sequence database
• blastx compares the six-frame conceptual protein
translation products of a nucleotide query
sequence against a protein sequence database
• tblastn compares a protein query sequence against
a nucleotide sequence database translated in six
reading frames
• tblastx compares the six-frame translations of a
nucleotide query sequence against the six-frame
translations of a nucleotide sequence database.
BLAST
• Generates all tripeptides from a query sequence
and for each of those the derivation of a table of
similar tripeptides: number is only fraction of total
number possible.
• Quickly scans a database of protein sequences for
ungapped regions showing high similarity, which
are called high-scoring segment pairs (HSP),
using the tables of similar peptides. The initial
search is done for a word of length W that scores
at least the threshold value T when compared to
the query using a substitution matrix.
• Word hits are then extended in either direction in
an attempt to generate an alignment with a score
exceeding the threshold of S, and as far as the
cumulative alignment score can be increased.
BLAST
Extension of the word hits in each direction are halted
• when the cumulative alignment score falls off by the
quantity X from its maximum achieved value
• the cumulative score goes to zero or below due to the
accumulation of one or more negative-scoring residue
alignments
• upon reaching the end of either sequence
• The T parameter is the most important for the speed and
sensitivity of the search resulting in the high-scoring
segment pairs
• A Maximal-scoring Segment Pair (MSP) is defined as
the highest scoring of all possible segment pairs
produced from two sequences.
PSI-BLAST
• Query sequences are first scanned for the presence of
so-called low-complexity regions (Wooton and
Federhen, 1996), i.e. regions with a biased composition
likely to lead to spurious hits; are excluded from
alignment.
• The program then initially operates on a single query
sequence by performing a gapped BLAST search
• Then, the program takes significant local alignments
found, constructs a multiple alignment and abstracts a
position specific scoring matrix (PSSM) from this
alignment.
• Rescan the database in a subsequent round to find more
homologous sequences Iteration continues until user
decides to stop or search has converged
PSI-BLAST iteration
Q
xxxxxxxxxxxxxxxxx
Query sequence
Gapped BLAST search
Q
xxxxxxxxxxxxxxxxx
Query sequence
Database hits
A
C
D
.
.
Y
PSSM
Pi
Px
Gapped BLAST search
A
C
D
.
.
Y
Pi
Px
PSSM
Database hits
PSI-BLAST output example
Multiple alignment profiles
Gribskov et al. 1987
A way to represent multiple alignment consensus
i
A
C
D



W
Y
Gap
penalties
1.0
0.3
0.1
0



0.3
0.3
0.5
Position dependent gap penalties
Normalised sequence similarity
The p-value is defined as the probability of seeing at
least one unrelated score S greater than or equal to a
given score x in a database search over n sequences.
This probability follows the Poisson distribution
(Waterman and Vingron, 1994):
P(x, n) = 1 – e-nP(S x),
where n is the number of sequences in the database
Depending on x and n (fixed)
Normalised sequence similarity
Statistical significance
The E-value is defined as the expected number of nonhomologous sequences with score greater than or equal
to a score x in a database of n sequences:
E(x, n) = nP(S  x)
if E-value = 0.01, then the expected number of random
hits with score S  x is 0.01, which means that this Evalue is expected by chance only once in 100
independent searches over the database.
if the E-value of a hit is 5, then five fortuitous hits with S
 x are expected within a single database search, which
renders the hit not significant.
Normalised sequence similarity
Statistical significance
• Database searching is commonly performed
using an E-value in between 0.1 and 0.001.
• Low E-values decrease the number of false
positives in a database search, but increase
the number of false negatives, thereby
lowering the sensitivity of the search.
HMM-based homology searching
• Most widely used HMM-based profile searching
tools currently are SAM-T98 (Karplus et al.,
1998) and HMMER2 (Eddy, 1998)
• formal probabilistic basis and consistent theory
behind gap and insertion scores
• HMMs good for profile searches, bad for
alignment
• HMMs are slow
The HMM algorithms
Forward:
 t (i) = P(observed sequence, ending in state i at base t)
Backward:
ß t (i) = P(obs. after t | ending in state i at base t)
Viterbi:
 t (i) = max P(obs. , ending in state i at base t)
Questions:
1. What is the most likely die (predicted) sequence? Viterbi
2. What is the probability of the observed sequence? Forward
3. What is the probability that the 3rd state is B, given the
observed sequence? Backward
HMM-based homology searching
Transition probabilities and Emission probabilities
Gapped HMMs also have insertion and deletion
states
Profile HMM: m=match state, I-insert state, d=delete state; go from
left to right. I and m states output amino acids; d states are ‘silent”.
d1
d2
d3
d4
I0
I1
I2
I3
I4
m0
m1
m2
m3
m4
Start
m5
End
Homology-derived Secondary Structure of Proteins
(HSSP)
Sander & Schneider, 1991