lecture 3.11 blast. lecture 3.12 blast b asic l ocal a lignment s earch t ool developed in 1990 and...
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![Page 1: Lecture 3.11 BLAST. Lecture 3.12 BLAST B asic L ocal A lignment S earch T ool Developed in 1990 and 1997 (S. Altschul) A heuristic method for performing](https://reader030.vdocuments.us/reader030/viewer/2022033101/56649d385503460f94a10d58/html5/thumbnails/1.jpg)
Lecture 3.1 1
BLAST
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Lecture 3.1 2
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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Lecture 3.1 3
• 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
BLAST
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Lecture 3.1 4
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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Lecture 3.1 5
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Lecture 3.1 6
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Lecture 3.1 7
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Lecture 3.1 8
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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Lecture 3.1 9
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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Lecture 3.1 10
Running NCBI BLAST
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Lecture 3.1 11
MT0895
• MMKIQIYGTGCANCQMLEKNAREAVKELGIDAEFEKIKEMDQILEAGLTALPGLAVDGELKIMGRVASKEEIKKILS
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Lecture 3.1 12
• Paste in sequence (FASTA format, raw sequence or type in GI or accession number)
Running NCBI BLAST
>Mysequence MT0895 KIQIYGTGCANCQMLEKNAREAVKELGIDAEFEKIKEMDQILEAGLTALPGLAVDGELKIDS
> KIQIYGTGCANCQMLEKNAREAVKELGIDAEFEKIKEMDQILEAGLTALPGLAVDGELKIDS
OR
KIQIYGTGCANCQMLEKNAREAVKELGIDAEFEKIKEMDQILEAGLTALPGLAVDGELKIDS
OR
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Lecture 3.1 13
• 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
Running NCBI BLAST
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Lecture 3.1 14
Running NCBI BLAST
Select Database
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Lecture 3.1 15
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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Lecture 3.1 16
Running NCBI BLAST
Click BLAST!
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Lecture 3.1 17
Formatting Results
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Lecture 3.1 18
BLAST Format Options
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Lecture 3.1 19
BLAST Output
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Lecture 3.1 20
BLAST Output
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Lecture 3.1 21
BLAST Output
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Lecture 3.1 22
BLAST Output
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Lecture 3.1 23
BLAST Output
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Lecture 3.1 24
BLAST Output
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Lecture 3.1 25
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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Lecture 3.1 26
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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Lecture 3.1 27
Doolittle’s Curve
Evolutionary Distance VS Percent Sequence Identity
0
20
40
60
80
100
120
0 40 80 120 160 200 240 280 320 360 400
Number of Residues
Sequ
ence
Iden
tity
(%)
Twilight Zone
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Lecture 3.1 28
Getting the Most from BLAST
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Lecture 3.1 29
BLAST Options
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Lecture 3.1 30
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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Lecture 3.1 31
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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Lecture 3.1 32
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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Lecture 3.1 33
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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Lecture 3.1 34
How to Make Your Own Matrix
ACDEFGH..ACDEFGK..AADEFGH..GCDEFGH..ACAEYGK..ACAEFAH..
Perform Calculate Fill SubAlignment Frequencies Matrix
f(A,A) =
AA
C
D
0.8 -- -- C D ...
E
0.2 0.8 --
#Aobs
#Aexp
0.0 0.3 1.0
-- -- -- f(C,A) =#C/Aobs
#Aexp #Cexp+
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Lecture 3.1 35
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
• 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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Lecture 3.1 36
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
• 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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Lecture 3.1 37
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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Lecture 3.1 38
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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Lecture 3.1 39
Scraping the Bottom of the Barrel with Psi-BLAST
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Lecture 3.1 40
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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Lecture 3.1 41
PSI-BLAST
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Lecture 3.1 42
PSI-BLASTPresS Iterate!
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Lecture 3.1 43
PSI-BLAST
PresS Iterate!
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Lecture 3.1 44
PSI-BLAST
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Lecture 3.1 45
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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Lecture 3.1 46
BLAST against PDB
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Lecture 3.1 47
Still Confused?http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html
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Lecture 3.1 48
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