BCB 444/544
Lecture 10
BLAST Details
Plus some Gene Jargon
#10_Sept12
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
1
Required Reading
(before lecture)
√Mon Sept 10 - for Lecture 9
BLAST variations; BLAST vs FASTA, SW
• Chp 4 - pp 51-62
√Wed Sept 12 - for Lecture 10 & Lab 4
Multiple Sequence Alignment (MSA)
• Chp 5 - pp 63-74
Fri Sept 14 - for Lecture 11
Position Specific Scoring Matrices & Profiles
• Chp 6 - pp 75-78 (but not HMMs)
• Good Additional Resource re: Sequence Alignment?
• Wikipedia: http://en.wikipedia.org/wiki/Sequence_alignment
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
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Assignments & Announcements - #1
Revised Grading Policy has been sent via email
Please review!
√Mon Sept 10 - Lab 3 Exercise due 5 PM:
to: terrible@iastate.edu
Thu Sept 13 - Graded Labs 2 & 3
will be returned at beginning of Lab 4
Fri Sept 14 - HW#2 due by 5 PM (106 MBB)
Study Guide for Exam 1 will be posted by 5 PM
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Review: Gene Jargon #1
(for HW2, 1c)
Exons = "protein-encoding" (or "kept" parts) of eukaryotic genes
vs
Introns = "intervening sequences"
= segments of eukaryotic genes that "interrupt" exons
• Introns are transcribed into pre-RNA
• but are later removed by RNA processing
• & do not appear in mature mRNA
• so are not translated into protein
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Assignments & Announcements - #2
Mon Sept 17 - Answers to HW#2
will be posted by 5 PM
Thu Sept 20 - Lab = Optional Review Session for Exam
Fri Sept 21 - Exam 1 - Will cover:
•
•
•
•
Lectures 2-12 (thru Mon Sept 17)
Labs 1-4
HW2
All assigned reading:
Chps 2-6 (but not HMMs)
Eddy: What is Dynamic Programming
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Chp 3- Sequence Alignment
SECTION II
SEQUENCE ALIGNMENT
Xiong: Chp 3
Pairwise Sequence Alignment
•
•
•
•
•
•
√Evolutionary Basis
√Sequence Homology versus Sequence Similarity
√Sequence Similarity versus Sequence Identity
√Methods - (Dot Plots, DP; Global vs Local Alignment)
√Scoring Matrices (PAM vs BLOSUM)
√Statistical Significance of Sequence Alignment
Adapted from Brown and Caragea, 2007, with some slides from:
Altman, Fernandez-Baca, Batzoglou, Craven, Hunter, Page.
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
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Local Alignment: Algorithm
This slide has
been changed!
1) Initialize top row & leftmost column of matrix with "0"
2) Fill in DP matrix:
In local alignment, no negative scores
Assign "0" to cells with negative scores
3) Optimal score? in highest scoring cell(s)
4) Optimal alignment(s)? Traceback from each cell
containing the optimal score, until a cell with "0" is
reached (not just from lower right corner)
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
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Local Alignment DP:
Initialization & Recursion
S 0,0  0
New Slide
S(i,0)  0 S(0, j)  0

S i 1, j 1   x , y
  i j
 
S i, j   max S i 1, j   

S i, j 1  

0
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
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A Few Words about Parameter Selection
in Sequence Alignment
Optimal alignment between a pair of sequences depends critically
on the selection of substitution matrix &
gap penalty function
S i 1, j 1  xi , y j 

S i, j   max S i 1, j   
S i, j 1  

 
In using BLAST or similar software, it is important to understand and,
sometimes, to adjust these parameters (default is NOT always best!)
How do we pick parameters that give the most biologically
meaningful alignments and alignment scores?
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
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Calculating an Alignment Score using
a Substitution Matrix &
an Affine Gap Penalty
• Alignment score is sum of all match/mismatch
scores (from substitution matrix) with an affine
penalty subtracted for each gap
Match
score
a b c - - d
a c c e f d
9 2 7
6 => 24
Values from
substitution matrix
Gap opening
+ extension
-
Alignment
(10 + 2) = 12
Score
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Chp 4- Database Similarity Searching
SECTION II
SEQUENCE ALIGNMENT
Xiong: Chp 4
Database Similarity Searching
•
•
•
•
•
•
Unique Requirements of Database Searching
Heuristic Database Searching
Basic Local Alignment Search Tool (BLAST)
FASTA
Comparison of FASTA and BLAST
Database Searching with Smith-Waterman Method
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Database searching
Sequence
database
Query Sequence
Target
sequences
ranked by score
Sequence
comparison
algorithm
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Why search a database?
• Given a newly discovered gene,
• Does it occur in other species?
• Is its function known in another species?
• Given a newly sequenced genome, which regions align
with genomes of other organisms?
•
•
Identification of potential genes
Identification of other functional parts of chromosomes
• Find members of a multigene family
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Recall: There are 3 Basic Types of
Alignment Algorithms?
SECTION II
SEQUENCE ALIGNMENT
Xiong: Chp 3
1) Dot Matrix
2) Dynamic Programming
Xiong: Chp 4
3) Word or k-tuple methods
(BLAST & FASTA)
Wikipedia:
Word methods, also known as k-tuple methods, are heuristic methods
that are not guaranteed to find an optimal alignment solution, but are
significantly more efficient than dynamic programming.
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Exhaustive vs Heuristic Methods
Exhaustive - tests every possible solution
• guaranteed to give best answer
(identifies optimal solution)
• can be very time/space intensive!
• e.g., Dynamic Programming
(as in Smith-Waterman algorithm)
Heuristic - does NOT test every possibility
• no guarantee that answer is best
(but, often can identify optimal solution)
• sacrifices accuracy (potentially) for speed
• uses "rules of thumb" or "shortcuts"
• e.g., BLAST & FASTA
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Why do we Need Fast Search Algorithms?
• Your query is 200 amino acids long (N)
• You are searching a non-redundant database, which
currently contains >106 proteins (K)
• If proteins in database have avg length 200 aa (M), then:
 Must fill in 200  200  106 = 4  1010 DP entries!!
• 4  1010 operations just to fill in the DP matrix!
• DP for pairwise alignment is O(NM)
• Searching in a database is O(NMK)
 Need faster algorithms for searching in large
databases!
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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FASTA vs BLAST
• Both FASTA, BLAST are based on heuristics
• Tradeoff:
Sensitivity vs Speed
• DP is slower, but more sensitive
• FASTA
• user defines value for k = word length
• Slower, but more sensitive than BLAST at lower values of k,
(preferred for searches involving a very short query sequence)
• BLAST family
• Family of different algorithms optimized for particular types of
queries, such as searching for distantly related sequence matches
• BLAST was developed to provide a faster alternative to FASTA
without sacrificing much accuracy
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Lab3: focus on BLAST
Basic Local Alignment
Search Tool
STEPS:
1.
2.
3.
4.
5.
Create list of very possible "word" (e.g., 3-11 letters)
from query sequence
Search database to identify sequences that contain
matching words
Score match of word with sequence, using a substitution matrix
Extend match (seed) in both directions, while calculating alignment
score at each step
Continue extension until score drops below a threshold (due to
mismatches)
High Scoring Segment Pair (HSP) - contiguous aligned
segment pair (no gaps)
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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What are the Results of a BLAST Search?
Original version of BLAST?
List of HSPs called Maximum Scoring Pairs
More recent, improved version of BLAST?
Allows gaps: Gapped Alignment
How? Allows score to drop below threshold,
(but only temporarily)
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Why is Gapped Alignment Harder?
•
•
Without gaps, there are N+M-1 possible alignments between
sequences of length N and M
Once we start allowing gaps, there are many more possible
arrangements to consider:
abcbcd
||| |
abc--d
•
abcbcd
| |||
a--bcd
abcbcd
|| ||
ab--cd
Becomes a very large number when we also allow mismatches,
because we need to look at every possible pairing between elements:
Roughly NM possible alignments!
e.g.: for N=M=100, there are 100100=10200 possible alignments
& 100 aa is a small protein!
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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BLAST - a few details
Developed by Stephen Altschul at NCBI in 1990
•
Word length?
•
•
Substitution matrix?
•
•
•
•
•
Typically:
3 aa for protein sequence
11 nt for DNA sequence
Default is BLOSUM62
Can change under Algorithm Parameters
Can choose other BLOSUM or PAM matrices
Change other parameters here, too
Stop-Extension Threshold?
•
Typically:
22 for proteins
20 for DNA
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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BLAST - Statistical Significance?
1. E-value: E = m x n x P
m = total number of residues in database
n = number of residues in query sequence
P = probability that an HSP is result of random chance
lower E-value, less likely to result from
random chance, thus higher significance
2. Bit Score: S'
normalized score, to account for differences in size of
database (m) & sequence length(n) - more later
3. Low Complexity Masking
remove repeats that confound scoring - more sooner
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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BLAST algorithms can generate both
"global" and "local" alignments
Global
alignment
Local
alignment
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
9/12/07
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BLAST - a Family of Programs:
Different BLAST "flavors"
•
•
•
•
•
BLASTP - protein sequence query against protein DB
BLASTN - DNA/RNA seq query against DNA DB (GenBank)
BLASTX - 6-frame translated DNA seq query against protein DB
TBLASTN - protein query against 6-frame DNA translation
TBLASTX - 6-frame DNA query to 6-frame DNA translation
•
•
•
PSI-BLAST - protein "profile" query against protein DB
PHI-BLAST - protein pattern against protein DB
Newest: MEGA-BLAST - optimized for highly similar sequences
Which tool should you use?
http://www.ncbi.nlm.nih.gov/blast/producttable.shtml
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Review: Gene Jargon #2.1
6-Frame translated DNA Sequence?
Remember GeneBoy exercise?
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Review: Gene Jargon #2.2
6-Frame translated DNA Sequence?
Try NCBI tools:
http://www.ncbi.nlm.nih.gov/gorf/orfig.cgi
http://www.ncbi.nlm.nih.gov/
Or - for some Biology review re: DNA/RNA & ORFs,
see next 3 slides borrowed from EMBL-EBI:
http://www.ebi.ac.uk/
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Review: Gene Jargon #2.3
http://www.ebi.ac.uk/
DNA Strands
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Review: Gene Jargon #2.4
http://www.ebi.ac.uk/
RNA Strands - copied from DNA
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Review: Gene Jargon #2.5
http://www.ebi.ac.uk/
Reading Frames
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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BLAST - How does it work?
Main idea - based on dot plots!
GATCA AC TGA CGTA
G
T
T
C
A
G
C
T
G
C
G
T
A
C
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Dot Plots - apply in BLAST:
GATCA AC TGA CGTA
G
T
T
C
A
G
C
T
G
C
G
T
A
C
Perform fast, approximate
local alignments to find
sequences in database that
are related to query sequence
Here, use 4-base "window"
75% identity (allow mismatches)
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Detailed Steps in BLAST algorithm
1.
Remove low-complexity regions (LCRs)
2. Make a list (dictionary): all words of length 3aa or 11 nt
3. Augment list to include similar words
4. Store list in a search tree (data structure)
5. Scan database for occurrences of words in search tree
6. Connect nearby occurrences
7. Extend matches (words) in both directions
8. Prune list of matches using a score threshold
9. Evaluate significance of each remaining match
10. Perform Smith-Waterman to get alignment
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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1: Filter low-complexity regions
(LCRs)
This slide has
been changed!
K = computational complexity;
• Low complexity regions,
varies from 0 (very low complexity)
transmembrane regions and
to 1 (high complexity)
coiled-coil regions often display
Alphabet size
significant similarity without
(4 or 20)
Window length
homology.
(usually 12)
• Low complexity sequences can
yield false positives.
• Screen them out of your query


sequences! When appropriate!


e.g., for GGGG:
L! = 4!=4x3x2x1= 24
nG=4 nT=nA=nC=0
 ni! = 4!x0!x0!x0! = 24
K=1/4 log4 (24/24) = 0
For CGTA: K=1/4 log4(24/1) = 0.57
1
L!
K  log N 

L
  ni ! 
 i

Frequency of ith
letter in the window
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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2: List all words in query
YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ
YGG
GGF
GFM
FMT
MTS
TSE
SEK
…
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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3: Augment word list
YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ
YGG
GGF
GFM
AAA
AAB
FMT
AAC
MTS
203 = 8000
…
TSE
possible matches
SEK
YYY
…
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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3: Augment word list
BLOSUM62
scores
G
G
F
A
A
A
0 + 0 + -2 = -2
Non-match
G
G
G
G
6 + 6 +
Match
F
Y
3 = 15
A user-specified threshold, T, determines which 3-letter
words are considered matches and non-matches
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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3: Augment word list
YGGFMTSEKSQTPLVTLFKNAIIKNAHKKGQ
YGG
GGF
GFM
GGI
GGL
FMT
GGM
MTS
GGF
GGW
TSE
GGY
SEK
…
…
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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3: Augment word list
Observation:
Selecting only words with score > T greatly reduces
number of possible matches
otherwise, 203 for 3-letter words from amino acid sequences!
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Example
Find all words that match EAM with a score greater
than or equal to 11
A
R
N
D
C
Q
E
G
H
I
L
K
M
F
P
S
T
W
Y
V
A
4
-1
-2
-2
0
-1
-1
0
-2
-1
-1
-1
-1
-2
-1
1
0
-3
-2
0
R
-1
5
0
-2
-3
1
0
-2
0
-3
-2
2
-1
-3
-2
-1
-1
-3
-2
-3
N
-2
0
6
1
-3
0
0
0
1
-3
-3
0
-2
-3
-2
1
0
-4
-2
-3
D
-2
-2
1
6
-3
0
2
-1
-1
-3
-4
-1
-3
-3
-1
0
-1
-4
-3
-3
C
0
-3
-3
-3
9
-3
-4
-3
-3
-1
-1
-3
-1
-2
-3
-1
-1
-2
-2
-1
Q
-1
1
0
0
-3
5
2
-2
0
-3
-2
1
0
-3
-1
0
-1
-2
-1
-2
E
-1
0
0
2
-4
2
5
-2
0
-3
-3
1
-2
-3
-1
0
-1
-3
-2
-2
G
0
-2
0
-1
-3
-2
-2
6
-2
-4
-4
-2
-3
-3
-2
0
-2
-2
-3
-3
H
-2
0
1
-1
-3
0
0
-2
8
-3
-3
-1
-2
-1
-2
-1
-2
-2
2
-3
I
-1
-3
-3
-3
-1
-3
-3
-4
-3
4
2
-3
1
0
-3
-2
-1
-3
-1
3
L
-1
-2
-3
-4
-1
-2
-3
-4
-3
2
4
-2
2
0
-3
-2
-1
-2
-1
1
K
-1
2
0
-1
-3
1
1
-2
-1
-3
-2
5
-1
-3
-1
0
-1
-3
-2
-2
M
-1
-1
-2
-3
-1
0
-2
-3
-2
1
2
-1
5
0
-2
-1
-1
-1
-1
1
F
-2
-3
-3
-3
-2
-3
-3
-3
-1
0
0
-3
0
6
-4
-2
-2
1
3
-1
P
-1
-2
-2
-1
-3
-1
-1
-2
-2
-3
-3
-1
-2
-4
7
-1
-1
-4
-3
-2
S
1
-1
1
0
-1
0
0
0
-1
-2
-2
0
-1
-2
-1
4
1
-3
-2
-2
T
0
-1
0
-1
-1
-1
-1
-2
-2
-1
-1
-1
-1
-2
-1
1
5
-2
-2
0
W
-3
-3
-4
-4
-2
-2
-3
-2
-2
-3
-2
-3
-1
1
-4
-3
-2
11
2
-3
Y
-2
-2
-2
-3
-2
-1
-2
-3
2
-1
-1
-2
-1
3
-3
-2
-2
2
7
-1
V
0
-3
-3
-3
-1
-2
-2
-3
-3
3
1
-2
1
-1
-2
-2
0
-3
-1
4
EAM
DAM
QAM
ESM
EAL
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
5
2
2
5
5
+
+
+
+
+
4
4
4
1
4
+
+
+
+
+
5
5
5
5
2
=
=
=
=
=
14
11
11
11
11
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4: Store words in search tree
Augmented list of
query words
“Does this query contain GGF?”
Search tree
“Yes, at position 2.”
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Search tree
GGF
GGL
GGM
GGW
GGY
G
G
F
L
M
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
W
Y
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Example
Put this word list into a search tree
DAM
QAM
EAM
KAM
ECM
EGM
ESM
ETM
EVM
EAI
EAL
EAV
D
A
A
M
M
A
I
Q
E
K
C
G
S
T
V
A
M
M
M
M
M
M
V
L
M
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5: Scan the database sequences
Query sequence
Database sequence








BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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Example
Scan this "database" for occurrences of your words
MKFLILLFNILCLDAMLAADNHGVGPQGASGVDPITFDINSNQTGPAFLTAVEAIGVKYLQVQHGSNVNIHRLVEGNVKAMENA
E
A
M
P
Q
L
S
V
D
A
M

BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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6: Connect nearby occurences
(diagonal matches in Gapped BLAST)
Query sequence
Database sequence
Two dots are connected
IFF if they are less
than A letters apart &
are on diagonal








BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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7: Extend matches in both directions
Scan
DB
BCB 444/544 F07 ISU Dobbs #10 - BLAST details + some Gene Jargon
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7: Extend matches,
calculating score at each step
L P
M P
P Q G L L
P E G L L
<word>
7 2 6
<----->
2 7 7 2 6 4 4
Query sequence
Database sequence
BLOSUM62 scores
word score = 15
HSP SCORE = 32
(High Scoring Pair)
• Each match is extended to left & right until a
negative BLOSUM62 score is encountered
• Extension step typically accounts for > 90% of
execution time
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8: Prune matches
• Discard all matches that score below defined
threshold
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9: Evaluate significance
This slide has
been changed!
• BLAST uses an analytical statistical significance
calculation
RECALL:
1.
E-value: E = m x n x P
m = total number of residues in database
n = number of residues in query sequence
P = probability that an HSP is result of random chance
lower E-value, less likely to result from random chance,
thus higher significance
2.
Bit Score: S' =
normalized score, to account for differences in size of database (m) & sequence
length(n); Note (below) that bit score is linearly related to raw alignment
score, so: higher S' means alignment has higher significance
S'= ( X S - ln K)/ln2 where:
 = Gumble distribution constant
S = raw alignment score
K = constant associated with scoring matrix
For more details - see text & BLAST tutorial
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10: Use Smith-Waterman algorithm
(DP) to generate alignment
• ONLY significant matches are re-analyzed using
Smith-Waterman DP algorithm.
• Alignments reported by BLAST are produced by
dynamic programming
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BLAST: What is a "Hit"?
• A hit is a w-length word in database that aligns with a
word from query sequence with score > T
• BLAST looks for hits instead of exact matches
• Allows word size to be kept larger for speed, without sacrificing
sensitivity
• Typically, w = 3-5 for amino acids,
w = 11-12 for DNA
• T is the most critical parameter:
• ↑T  ↓ “background” hits (faster)
• ↓T  ↑ ability to detect more distant relationships
(at cost of increased noise)
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Tips for BLAST Similarity Searches
• If you don’t know, use default parameters first
• Try several programs & several parameter settings
• If possible, search on protein sequence level
• Scoring matrices:
PAM1 / BLOSUM80:
if expect/want less divergent proteins
PAM120 / BLOSUM62: "average" proteins
PAM250 / BLOSUM45: if need to find more divergent proteins
• Proteins:
>25-30% identity (and >100aa)
15-25% identity
<15% identity
-> likely related
-> twilight zone
-> likely unrelated
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Practical Issues
Searching on DNA or protein level?
In general,
protein-encoding DNA should be translated!
• DNA yields more random matches:
• 25% for DNA vs. 5% for proteins
• DNA databases are larger and grow faster
• Selection (generally) acts on protein level
• Synonymous mutations are usually neutral
• DNA sequence similarity decays faster
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BLAST vs FASTA
• Seeding:
• BLAST integrates scoring matrix into first phase
• FASTA requires exact matches (uses hashing)
• BLAST increases search speed by finding fewer, but
better, words during initial screening phase
• FASTA uses shorter word sizes - so can be more
sensitive
• Results:
• BLAST can return multiple best scoring alignments
• FASTA returns only one final alignment
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BLAST & FASTA References
• FASTA -
developed first
• Pearson & Lipman (1988) Improved Tools for Biological
Sequence Comparison. PNAS 85:2444- 2448
• BLAST
• Altschul, Gish, Miller, Myers, Lipman, J. Mol. Biol. 215 (1990)
• Altschul, Madden, Schaffer, Zhang, Zhang, Miller, Lipman
(1997) Gapped BLAST and PSI-BLAST: a new generation of
protein database search programs. Nucleic Acids Res.
25:3389-402
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BLAST Notes - & DP Alternatives
• BLAST uses heuristics: it may miss some good matches
• But, it’s fast: 50 - 100X faster than Smith-Waterman (SW) DP
• Large impact:
• NCBI’s BLAST server handles more than 100,000 queries/day
• Most used bioinformatics program in the world!
 But - Xiong says: "It has been estimated that for some families of
protein sequences BLAST can miss 30% of truly significant
matches."
• Increased availability of parallel processing has made DP-based
approaches feasible:
• 2 DP-based web servers: both more sensitive than BLAST
• Scan Protein Sequence: http://www.ebi.ac.uk/scanps/index.html
Implements modified SW optimized for parallel processing
• ParAlign www.paralign.org - parallel SW or heuristics
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NCBI - BLAST Programs
Glossary & Tutorials
BLAST
•
http://www.ncbi.nlm.nih.gov/BLAST/
•
http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/glossary2.html
•
http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html
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