Download Lec-LocalAlignmentAndBLAST2010

Local Alignment and BLAST
Three key questions
• Query?
• Purpose?
• Database?
BLAST – the way it used to look
>gi|77630012|ref|ZP_00792598.1|
COG0442: Prolyl-tRNA synthetase [Yersinia pseudotuberculosis
IP 31758]
Length=572
Score = 1013 bits (2619),
Expect = 0.0, Method: Composition-based stats.
Identities = 498/572 (87%), Positives = 537/572 (93%), Gaps = 0/572 (0%)
Query
1
MRTSQYMLSTLKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGLRVLRKVENIVREE
60
MRTSQY+LST KETPADAEVISHQLMLRAGMIRKLASGLYTWLPTG+RVL+KVENIVREE
Sbjct
1
MRTSQYLLSTQKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGVRVLKKVENIVREE
60
Query
61
MNNAGAIEVSMPVVQPADLWVESGRWDQYGPELLRFVDRGERPFVLGPTHEEVITDLIRN
120
MNNAGAIEVSMPVVQPADLW ESGRW+QYGPELLRFVDRGERPFVLGPTHEEVITDLIR
Sbjct
61
MNNAGAIEVSMPVVQPADLWQESGRWEQYGPELLRFVDRGERPFVLGPTHEEVITDLIRG
120
Query
121
EVSSYKQLPLNFFQIQTKFRDEVRPRFGVMRSREFLMKDAYSFHTSQESLQATYDTMYAA
180
E++SYKQLPLNFFQIQTKFRDEVRPRFGVMR+REFLMKDAYSFHT+QESLQ TYD MY A
Sbjct
121
EINSYKQLPLNFFQIQTKFRDEVRPRFGVMRAREFLMKDAYSFHTTQESLQETYDAMYTA
180
MNMHKSFRVKEVAEDIYQQLRAKGIEVLLDDRKERPGVMFADMELIGVPHTIVIGDRNLD
540
………………………….
Query
481
MNMHKSFRVKE+AE++Y
LR+ GI+V+LDDRKERPGVMFADMELIGVPH IVIGDRNLD
Sbjct
481
MNMHKSFRVKELAEELYTTLRSHGIDVILDDRKERPGVMFADMELIGVPHNIVIGDRNLD
Query
541
SEEIEYKNRRVGEKQMIKTSEIIDFLLANIIR
572
SEE+EYKNRRVGEKQMIKTSEI++FLL+ I R
Sbjct
541
SEEVEYKNRRVGEKQMIKTSEIVEFLLSQIKR
572
540
Global Alignment vs. Local Alignment
• Global Methods find the best alignment of
both sequences in their entirety
• Local Methods find the best alignable
subsections of both sequences
Sequence Similarity Searches using BLAST
BLAST: Basic Local Alignment Search Tool
Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. J Mol
Biol. 1990 Oct 5;215(3):403-10.
Statistical basis:
Karlin, S., and Altschul, S. F. (1990) ``Method for assessing the
statistical significance of molecular sequence features by using
general scoring schemes,'' Proceedings of the National Academy
of Science, USA 87, 2264-2268.
Comparing a Genome to Other Genes and
Genomes
BLAST = Basic Local Alignment Search Tool
BLASTN
DNA sequence vs. DNA sequence db
BLASTP
protein sequence vs. protein sequence db
BLASTX
DNA sequence translated in 6 reading frames
vs. protein sequence db
tBLASTX
DNA sequence translated in 6 reading frames
vs. DNA sequence db translated in 6 frames
PSI-BLAST
Iterative Search
Comparing a Genome to Other Genes and
Genomes
BLAST = Basic Local Alignment Search Tool
1.
Find a potential match in the database by finding a little seed (or
seeds) of a match
2.
Extend that seed and score the resulting alignment based on cooccurance of amino acids (nucleotides) in “known” alignments
3.
Determine whether the possible alignment looks better than you
might expect by chance alone.
4. Decide whether the match tells you anything about biology.
1.
Find a potential match in the database by finding a little seed (or
seeds) of a match
db
query
Your query is small relative to the universe of known sequences
2. Extend the seed and score the resulting alignment based on cooccurance of amino acids (nucleotides) in “known” alignments
N
Y
A
L
L
P
W
M
T
A
Y
E
N
V
Y
L
A
V
D
0
V F Q N E L L P WR N V Q D N V A F G
2
4
6
8
10
12
E.coli ABC transport
14
16
18
20
Alignment Methods – Dynamic Programming
• Needleman-Wunsch (global) and SmithWaterman (local) use dynamic programming
• Guaranteed to find an optimal alignment
given a particular scoring function
• Computationally intensive
Dynamic Programming
One possible simple scoring scheme:
•Si,j = 1 if the residue at position i of sequence #1 is
the same as the residue at position j of sequence #2
(match score); otherwise
•Si,j = 0 (mismatch score)
•w = 0 (gap penalty)
Dynamic Programming
Three steps: 1) Initialize
2) Fill Matrix
Mi,j = MAXIMUM
[Mi-1, j-1 + Si,j (match/mismatch in the diagonal),
Mi,j-1 + w (gap in sequence #1),
Mi-1,j + w (gap in sequence #2)]
Dynamic Programming
3) Traceback
G A A T T C A G T T A
G G A - T C - G - - A
Score = 1+0+1+0+1+1+0+1+0+0+1 = 6
How does BLASTP score an alignment?
Substitution Matrix based on cooccurrence in related proteins
BLOSUM = BLOcks Substitution Matrix
Identify gap-free protein alignments
in the BLOCKS database.
BLOSUM# corresponds to %
identity for inclusion
Count co-occurrence of Amino
Acids in alignment
Calculate log-odds ratio:
Log (observed frequency/expected
frequency)
How does BLASTP score an alignment?
Substitution Matrix based on cooccurance in related proteins
62 means that contributions from
proteins more than 62% identical
are weighted to sum to one.
Other matrices are available for
comparisons of more or less
divergent proteins.
How does BLASTP score an alignment?
Walk through the alignment and add
up the score
Query: AFGECDA
AF C+A
Sbjct: AFAFCEA
4+6+0+(-3)+9+2+4 = 22
Normalize  bit score
Statistics of BLAST when no gaps
are allowed
• The number of matches (E) expected to occur with a score as
good as S just by random chance, when you search a sequence
the size of your query against a database as large as the one
you chose (m and n), tends to follow an Extreme Values
Distribution (K and lambda).
• Simulation is used to estimate K and lambda for gapped BLAST
How good is your BLAST hit?
• The number of matches (E) expected to occur with a score as
good as S just by random chance
>gi|77630012|ref|ZP_00792598.1|
pseudotuberculosis
COG0442: Prolyl-tRNA synthetase [Yersinia
IP 31758]
Length=572
Score = 1013 bits (2619),
Expect = 0.0, Method: Composition-based stats.
Identities = 498/572 (87%), Positives = 537/572 (93%), Gaps = 0/572 (0%)
Query
1
MRTSQYMLSTLKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGLRVLRKVENIVREE
60
MRTSQY+LST KETPADAEVISHQLMLRAGMIRKLASGLYTWLPTG+RVL+KVENIVREE
Sbjct
1
MRTSQYLLSTQKETPADAEVISHQLMLRAGMIRKLASGLYTWLPTGVRVLKKVENIVREE
60
Search one
protein against
a given
database and
most of the E
values are zero
Search one
protein against
a given
database and
most of the E
values are zero
Search the
protein encoded
by the gene
next to it in the
genome against
the same
database and
all the E values
are much
higher.
Search the same
protein against
two different
databases and
the E value is
different for the
same hit.
So,what’s a good match?
E-values…
0.0 is a perfect score
Really good matches have really small E-values, like e-107
Matches can still be real with moderate E-values like e-05
Sometimes matches with higher E-values are still real.
You should also have some expectation of what level of match is typical for
the type of comparison.
For example: if you are querying with the E. coli O157:H7 proteins against
a database of E. coli K-12 proteins, and most orthologous proteins have
matches on the order of e-107, then a match that scores e-05 is probably not
an orthologous pair.
Other characteristics of a good match:
Amount of the sequences in the alignment
A match that includes >90% of both sequences is great
Divergent matches include blocks of higher identity
Conserved motifs can be indicative of conserved function
All equally good matches are to proteins with the same function
***The function of at least one of the best hits was experimentally
determined.***