Transcript Blast Search

BLAST
Lecture 3.1
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BLAST
• Basic Local Alignment Search Tool
• Developed in 1990 and 1997 (S. Altschul)
• A heuristic method for performing local
alignments through searches of high
scoring segment pairs (HSP’s)
• 1st to use statistics to predict significance
of initial matches - saves on false leads
• Offers both sensitivity and speed
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BLAST
• Looks for clusters of nearby or locally dense “similar
or homologous” k-tuples
• Uses “look-up” tables to shorten search time
• Uses larger “word size” than FASTA to accelerate the
search process
• Performs both Global and Local alignment
• Fastest and most frequently used sequence alignment
tool -- THE STANDARD
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BLAST Access
• NCBI BLAST
• http://www.ncbi.nlm.nih.gov/BLAST/
• Canadian Bioinformatics Resource BLAST
• http://cbr-rbc.nrc-cnrc.gc.ca/blast/
• European Bioinformatics Institute BLAST
• http://www.ebi.ac.uk/blastall/
• http://www.ebi.ac.uk/blast2/
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Different Flavours of BLAST
• BLASTP - protein query against protein DB
• BLASTN - DNA/RNA query against GenBank (DNA)
• BLASTX - 6 frame trans. DNA query against proteinDB
• TBLASTN - protein query against 6 frame GB transl.
• TBLASTX - 6 frame DNA query to 6 frame GB transl.
• PSI-BLAST - protein ‘profile’ query against protein DB
• PHI-BLAST - protein pattern against protein DB
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Other BLAST Services
• MEGABLAST - for comparison of large sets
of long DNA sequences
• RPS-BLAST - Conserved Domain Detection
• BLAST 2 Sequences - for performing pairwise
alignments for 2 chosen sequences
• Genomic BLAST - for alignments against
select human, microbial or malarial genomes
• VecScreen - for detecting cloning vector
contamination in sequenced data
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Running NCBI BLAST
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MT0895
• MMKIQIYGTGCANCQMLEKNAREAVKELG
IDAEFEKIKEMDQILEAGLTALPGLAVDG
ELKIMGRVASKEEIKKILS
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Running NCBI BLAST
• Paste in sequence (FASTA format, raw
sequence or type in GI or accession number)
OR
>Mysequence MT0895
KIQIYGTGCANCQMLEKNAREAVKELGIDAE
FEKIKEMDQILEAGLTALPGLAVDGELKIDS
>
KIQIYGTGCANCQMLEKNAREAVKELGIDAE
FEKIKEMDQILEAGLTALPGLAVDGELKIDS
OR
KIQIYGTGCANCQMLEKNAREAVKELGIDAE
FEKIKEMDQILEAGLTALPGLAVDGELKIDS
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Running NCBI BLAST
• Choose a range of interest in the sequence
“set subsequences” (not usually used)
• Select the database from pull-down menu
(usually choose nr = non-redundant)
• Keep CD Search “check box” on
• Leave “Options” unchanged (use defaults)
• Go to “Format” menu and adjust Number of
descriptions and alignments as desired
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Running NCBI BLAST
Select Database
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Conserved Domain Database
• Contains a collection of pre-identified
functional or structural domains
• Derived from Pfam and Smart databases
as well as other sources
• Uses Reverse Position Specific BLAST
(RPS-BLAST) to perform search
• Query sequence is compared to a PSSM
derived from each of the aligned domains
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Running NCBI BLAST
Click BLAST!
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Formatting Results
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BLAST Format Options
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BLAST Output
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BLAST Output
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BLAST Output
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BLAST Output
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BLAST Output
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BLAST Output
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BLAST Parameters
• Identities - No. & % exact residue matches
• Positives - No. and % similar & ID matches
• Gaps - No. & % gaps introduced
• Score - Summed HSP score (S)
• Bit Score - a normalized score (S’)
• Expect (E) - Expected # of chance HSP aligns
• P - Probability of getting a score > X
• T - Minimum word or k-tuple score (Threshold)
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BLAST - Rules of Thumb
• Expect (E-value) is equal to the number of BLAST
alignments with a given Score that are expected to
be seen simply due to chance
• Don’t trust a BLAST alignment with an Expect score
> 0.01 (Grey zone is between 0.01 - 1)
• Expect and Score are related, but Expect contains
more information. Note that %Identies is more
useful than the bit Score
• Recall Doolittle’s Curve (%ID vs. Length, next slide)
%ID > 30 - numres/50
• If uncertain about a hit, perform a PSI-BLAST search
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Doolittle’s Curve
Evolutionary Distance VS Percent Sequence Identity
Sequence Identity (%)
120
100
80
60
Twilight Zone
40
20
0
0
40
80
120
160
200
240
280
320
360
400
Number of Residues
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Getting the Most from
BLAST
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BLAST Options
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BLAST Options
•
•
•
•
•
•
•
Composition-based statistics (Yes)
Sequence Complexity Filter (Yes)
Expect (E) value (10)
Word Size (3)
Substitution or Scoring Matrix (Blosum62)
Gap Insertion Penalty (11)
Gap Extension Penalty (1)
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Composition Statistics
• Recent addition to BLAST algorithm
• Permits calculated E (Expect) values to
account for amino acid composition of
queries and database hits
• Improves accuracy and reduces false
positives
• Effectively conducts a different scoring
procedure for each sequence in database
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LCR’s (low complexity)
• Watch out for…
– transmembrane or signal peptide regions
– coil-coil regions
– short amino acid repeats (collagen, elastin)
– homopolymeric repeats
• BLAST uses SEG to mask amino acids
• BLAST uses DUST to mask bases
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Scoring Matrices
• BLOSUM Matrices
– Developed by Henikoff & Henikoff (1992)
– BLOcks SUbstitution Matrix
– Derived from the BLOCKS database
• PAM Matrices
– Developed by Schwarz and Dayhoff (1978)
– Point Accepted Mutation
– Derived from manual alignments of closely
related proteins
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How to Make Your Own Matrix
ACDEFGH..
ACDEFGK..
AADEFGH..
GCDEFGH..
ACAEYGK..
ACAEFAH..
Perform
Alignment
Lecture 3.1
f
f
#Aobs
(A,A) =
#Aexp
#C/Aobs
(C,A) =
+ #Cexp
#Aexp
Calculate
Frequencies
A
A 0.8
C 0.2
D 0.0
E --
C D ...
-- -0.8 -0.3 1.0
--
--
Fill Sub
Matrix
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PAM versus BLOSUM
• First useful scoring
matrix for protein
• Assumed a Markov
Model of evolution (I.e.
all sites equally mutable
and independent)
• Derived from small,
closely related proteins
with ~15% divergence
Lecture 3.1
• Much later entry to matrix
“sweepstakes”
• No evolutionary model is
assumed
• Built from PROSITE
derived sequence blocks
• Uses much larger, more
diverse set of protein
sequences (30% - 90% ID)
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PAM versus BLOSUM
• Higher PAM numbers to
detect more remote
sequence similarities
• Lower PAM numbers to
detect high similarities
• 1 PAM ~ 1 million years
of divergence
• Errors in PAM 1 are
scaled 250X in PAM 250
Lecture 3.1
• Lower BLOSUM numbers
to detect more remote
sequence similarities
• Higher BLOSUM numbers
to detect high similarities
• Sensitive to structural
and functional subsitution
• Errors in BLOSUM arise
from errors in alignment
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PAM Matricies
• PAM 40 - prepared by multiplying PAM 1 by
itself a total of 40 times
best for short alignments with high similarity
• PAM 120 - prepared by multiplying PAM 1 by
itself a total of 120 times
best for general alignment
• PAM 250 - prepared by multiplying PAM 1 by
itself a total of 250 times
best for detecting distant sequence similarity
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BLOSUM Matricies
• BLOSUM 90 - prepared from BLOCKS
sequences with >90% sequence ID
best for short alignments with high similarity
• BLOSUM 62 - prepared from BLOCKS
sequences with >62% sequence ID
best for general alignment (default)
• BLOSUM 30 - prepared from BLOCKS
sequences with >30% sequence ID
best for detecting weak local alignments
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Scraping the Bottom of
the Barrel with Psi-BLAST
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PSI-BLAST Algorithm
• Perform initial alignment with BLAST using
BLOSUM 62 substitution matrix
• Construct a multiple alignment from matches
• Prepare position specific scoring matrix
• Use PSSM profile as the scoring matrix for a
second BLAST run against database
• Repeat steps 3-5 until convergence
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PSI-BLAST
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PresS Iterate!
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PSI-BLAST
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PSI-BLAST
PresS Iterate!
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PSI-BLAST
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PSI-BLAST
• For Protein Sequences ONLY
• Much more sensitive than BLAST
• Slower (iterative process)
• Often yields results that are as good as
many common threading methods
• SHOULD BE YOUR FIRST CHOICE IN
ANALYZING A NEW SEQUENCE
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BLAST against PDB
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Still Confused?
http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html
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Conclusions
• BLAST is the most important program in
bioinformatics (maybe all of biology)
• BLAST is based on sound statistical
principles (key to its speed and sensitivity)
• A basic understanding of its principles is
key for using/interpreting BLAST output
• Use NBLAST or MEGABLAST for DNA
• Use PSI-BLAST for protein searches
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