blast and sequence alignment
TRANSCRIPT
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Lecture 2BLAST and Sequence
Alignment
Phylogenetics and Sequence Analysis
Fall 2015
Kurt Wollenberg, PhDPhylogenetics SpecialistBioinformatics and Computational Biosciences BranchOffice of Cyber Infrastructure and Computational Biology
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We Are BCBB!
Group of 37• Bioinformatics Software Developers• Computational Biologists• Project Management & Analysis Professionals
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Contact BCBB…
“NIH Users: Access a menu of BCBB services on the NIAID Intranet:
• http://bioinformatics.niaid.nih.gov/ Outside of NIH –
• search “BCBB” on the NIAID Public Internet Page: www.niaid.nih.gov
– or – use this direct link
Email us at:• [email protected]
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Course Organization
• Building a clean sequence• Collecting homologs• Aligning your sequences• Building trees• Further analyses
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Previously• Hierarchical and genealogical data
• Comparative sequence analysis
• Generating clean sequence
• Trim vector contamination
• Trim low-quality ends
• Align fragment overlap to build contig
• Export contig (consensus)
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Today…Pairwise sequence alignment• How does it work?
BLAST• How does it work?• The many flavors of BLAST• Demo
Multiple Sequence Alignment• How does it work?• Demo• Inspect and correct your MSA
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and BLAST: Basic Local Alignment Search Tool
• Sequence Alignment: Assigning homology to sites among a group of known sequences
• BLAST: Alignment of one sequence with many unknown sequences
PAIRWISE ALIGNMENT
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HOMOLOGY vs. ANALOGY
common ancestry convergence
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Pairing of sites based on an assessment of homology
Homology assessed using Substitution Matrices
PAIRWISE ALIGNMENT
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HBA_HUMAN GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKL G+ +VK+HGKKV A+++++AH+D++ +++++LS+LH KLHBB_HUMAN GNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKL
HBA_HUMAN GSAQVKGHGKKVADALTNAVAHV---D--DMPNALSALSDLHAHKL ++ ++++H+ KV + +A ++ +L+ L+++H+ KLGB2_LUPLU NNPELQAHAGKVFKLVYEAAIQLQVTGVVVTDATLKNLGSVHVSKG
HBA_HUMAN GSAQVKGHGKKVADALTNAVAHVDDMPNALSALSD----LHAHKL GS+ + G + +D L ++ H+ D+ A +AL D ++AH+F11G11.2 GSGYLVGDSLTFVDLL--VAQHTADLLAANAALLDEFPQFKAHQE
PAIRWISE ALIGNMENT
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Substitution Matrices
➡ Derived mathematically➡ Derived from data
“A substitution matrix (even one derived by arbitrarily assigning probabilities to pairs) is a statement of the
probability of observing these pairs in real alignment.”
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PAIRWISE ALIGNMENT
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• Single parameter - Jukes-Cantor- Equal base frequencies- Uniform rates of change
• Two parameter - Kimura- Equal base probabilities- Two rates of change
PAIRWISE ALIGNMENT
DNA Substitution Matrices
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• More parameters - HKY- Unequal base frequencies- Two rates of change
• Fully parameterized - GTR- Unequal base probabilities- Six rates of change
PAIRWISE ALIGNMENT
DNA Substitution Matrices
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PAIRWISE ALIGNMENT
Jukes-Cantor Substitution Probabilities
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μt = 0.25A C G T
A 0.5259 0.1580 0.1580 0.1580C 0.1580 0.5259 0.1580 0.1580
G 0.1580 0.1580 0.5259 0.1580
T 0.1580 0.1580 0.1580 0.5259
PAIRWISE ALIGNMENT
Jukes-Cantor Substitution Probabilities
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PAIRWISE ALIGNMENT
Kimura Two-Parameter Substitution Model
If the probability of transitions (A ⇔ G, C ⇔ T) is different from the probability of transversions (A ⇔T, G ⇔T, A ⇔C, G ⇔C), then there are two
relative rate parameters expressed as the transition/transversion rate ratio κ
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PAIRWISE ALIGNMENT
Kimura Two-Parameter Substitution Probabilities
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μt = 0.25 k = 2.0
A C G TA 0.4535 0.1580 0.2304 0.1580C 0.1580 0.4535 0.1580 0.2304
G 0.2304 0.1580 0.4535 0.1580
T 0.1580 0.2304 0.1580 0.4535
PAIRWISE ALIGNMENT
Kimura Two-Parameter Substitution Probabilities
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PAIRWISE ALIGNMENT
HKY Substitution Probabilities
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PAIRWISE ALIGNMENT
HKY Substitution Probabilities
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Jukes-Cantor(One substitution type, equal nucleotide frequencies)
Independent nucl. freq. Two substitution types .
F81/TN82 Kimura 2-Parameter (K2P)
Two substitution types Indep. nucl. freq. Three substitution types .
HKY85/F84 Kimura 3 subst. type (K3ST)
Three substitution types Six substitution types .
Tamura-Nei (TrN) Symmetric (SYM)
Six substitution types Independent nucl. freq. .
General time-reversible (GTR)
Substitution Models
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Protein Score MatricesSimilarity of Amino Acids
From Esquivel RO, et al.. 2013. Advances in Quantum Mechanics, Chapter 27 InTech.
PAIRWISE ALIGNMENT
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Protein Score Matrices
• Derived from empirical data• Account for depth of relationship among the data• Expressed as log-odds ratio:- Logarithm of the ratio of the probabilities of two
residues being aligned due to homology versus random chance
PAIRWISE ALIGNMENT
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Protein Score (Substitution) Matrices
PAIRWISE ALIGNMENT
The log-odds ratio:s(a,b) = log(pab/qaqb)
qa = frequency of residue a in the data
pab = probability that residues a and b have been derived from a common ancestor
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Protein Substitution Matrices
• PAM250: Based on phylogenies where all sequences differ by no more than 15%.
• BLOSUM62: Based on clusters of sequences with greater than 62% identical residues.
PAIRWISE ALIGNMENT
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Protein Substitution Matrices
PAM250
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BLOSUM62
Protein Substitution Matrices
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Protein Substitution Matrices
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BLAST and Sequence Alignment
• Global alignment (Needleman-Wunsch)• Assign homology across the entire sequence• Clustal
• Local alignment (Smith-Waterman)• Assign homology for subsequences• MUSCLE and BLAST• Good for aligning very divergent sequences
How do two sequences get “aligned”?
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HEAGAWGHEE ⇔ PAWHEAE
SEQUENCE ALIGNMENT
Build a matrix of score values for all site pairs
H E A G A W G H E EP 0 -1 1 0 1 -5 0 0 -1 -1A -1 0 2 1 2 -6 1 -1 0 0W -3 -7 -6 -7 -6 17 -7 -3 -7 -7H 6 1 -1 -2 -1 -3 -2 6 1 1E 1 4 0 0 0 -7 0 1 4 4A -1 0 2 1 2 -6 1 -1 0 0E 1 4 0 0 0 -7 0 1 4 4
H E A G A W G H E EP -2 -1 -1 -2 -1 -4 -2 -2 -1 -1A -2 -1 4 0 4 -3 0 -2 -1 -1W -2 -3 -3 -2 -3 11 -2 -2 -3 -3H 8 0 -2 -2 -2 -2 -2 8 0 0E 0 5 -1 -2 -1 -3 -2 0 5 5A -2 -1 4 0 4 -3 0 -2 -1 -1E 0 5 -1 -2 -1 -3 -2 0 5 5
PAM250 BLOSUM62
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What about gaps?
• Score penalty for opening• Score penalty for extending
Penalties are log probabilities of a gap of a specific length
SEQUENCE ALIGNMENT
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Standard gap costs
Substitution Matrix Gap Costs (Open, Extend)PAM30 (9,1)
PAM70 (10,1)
BLOSUM80 (10,1)
BLOSUM62 (10,1)
BLOSUM45 (15,2)
SEQUENCE ALIGNMENT
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Dynamic Programming: Calculate a matrix of alignment scores
H E AP -2 -1 -1A -2 -1 4W -2 -3 -3
BLOSUM62 H E A 0 -8 -16 -24
P -8
A -16
W -24
-2 -17-9
-10
-18 -11
-3 -5
-6
SEQUENCE ALIGNMENT
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Dynamic Programming
1) Calculate a full matrix2) Traceback to get the Global Alignment
H E A G A W G H E E 0 -8 -16 -24 -32 -40 -48 -56 -64 -72 -80P -8 -2 -9 -17 -25 -33 -41 -49 -57 -65 -73A -16 -10 -3 -5 -13 -21 -29 -37 -45 -53 -61W -24 -18 -11 -6 -7 -15 -10 -18 -26 -34 -41H -32 -16 -18 -13 -8 -9 -17 -12 -10 -18 -26E -40 -24 -11 -19 -15 -9 -12 -19 -12 -5 -13A -48 -32 -19 -7 -15 -11 -12 -12 -20 -13 -6E -58 -40 -27 -15 -9 -16 -14 -14 -12 -15 -8
H E A G A W G H E E- - P - A W H E A E
-8-13
-12-10
-21-25-17
-12
-16-80
SEQUENCE ALIGNMENT
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Local Alignment• Alignment of subsequences• Good for aligning very divergent sequences
Score Calculation• Minimum score is zero• Traceback begins at the highest score• Score = 0 End of subsequence
SEQUENCE ALIGNMENT
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Local Alignment H E A G A W G H E E 0 0 0 0 0 0 0 0 0 0 0P 0 0 0 0 0 0 0 0 0 0 0A 0 0 0 4 0 4 0 0 0 0 0W 0 0 0 0 0 0 15 7 0 0 0H 0 8 0 0 0 0 7 13 15 7 0E 0 0 13 5 0 0 0 5 13 20 12A 0 0 5 17 9 4 0 0 5 12 17E 0 0 5 9 15 8 0 0 0 10 17
A W G H EA W - H E
H E A G A W G H E ep a w H E A e p A W - H E a e
H E A G A W G H E e p A W - H E a e
Repeat Match Overlap Match
20
15 74
15
1713
8
SEQUENCE ALIGNMENT
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Scoring alignments and expect values
Score := Value in the dynamic programming matrix where the traceback began.
Expect (E) value := Number of matches expected due to chance, with a score greater than S, based on a stochastic sequence model.
P value := Probability of finding at least one match with score ≥ S
P = 1-e-E(S)
SEQUENCE ALIGNMENT
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BLAST(Basic Local Alignment Search Tool)
• Create a list of query sequence “words”
• Word lengths: 11 nucleotides, 3 amino acids
• Create a list of neighborhood words
• Similar to query words and above a score threshold
• Search for matches in the database
• Extend matches
• Below threshold?
• Above threshold?
• Format and output maximally extended matches
How does BLAST work?
Discard!
Keep it!
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How does BLAST work?
How does BLAST evaluate matches?
It uses (local) alignment scores
BLAST(Basic Local Alignment Search Tool)
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The Many Flavors of BLAST
• BLASTn and BLASTp• short, nearly-exact match BLAST• Translated BLAST
• BLASTx nt → aa protein db➯• tBLASTn aa protein db ← DNA db➯• tBLASTx nt → aa protein db ← DNA db➯
• PSI-BLAST (Position-Specific Iterated BLAST)• PHI-BLAST (Pattern Hit Initiated BLAST)• bl2seq
BLAST
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short, nearly-exact match BLAST
• Increase Expect threshold• Reduce word size (7 for nt, 2 for aa)• Turn off low complexity filter• Protein: Use a more stringent substitution matrix
BLAST
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PSI-BLAST(Position-Specific Iterated BLAST)
• Perform initial BLASTp search• Generate a sequence profile from results • BLASTp using the profile• Iterate until no new sequences are found • Convergence
BLAST
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PHI-BLAST(Pattern Hit Initiated BLAST)
[LIVMF]-G-E-x-[GAS]-[LIVM]-x(5,11)-R-[STAQ]-A-x-[LIVMA]-x-[STACV]
[ ] = Any of the residues within the brackets - = spacer separating sites in the profile x = Any residuex(a,b) = Any residues a to b in length
VGERGLEEDKRKRSAWMQCMGETALRRRKKEDEERTANVYTFGEAAMPGGPHQSRSAFAWV
Sequence Profile
BLAST
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Access to BLAST
• NCBI
• Your own computer
• NIAID HPC cluster
BLAST
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Multiple Sequence Alignment
Multiple Sequence AlignmentThe Progressive Alignment Algorithm
UnalignedSequences
Distance Matrix
Aligned Sequences MSA1
Kimura Distance Matrix
Aligned Sequences MSA2
Subtree Alignment Profiles
Align Subtree Profiles MSAsp
Better!
Not Better
From RC Edgar 2004, NAR 32: 1792-1797
UPGMA tree
UPGMA tree 2Delete edge from UPGMA tree 2
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• Clustal• Your own computer• Web Server• NIAID HPC cluster
• MUSCLE• Your own computer• Web Server• NIAID HPC cluster
• MAFFT• Web Server
Programs
Multiple Sequence Alignment
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NEVER directly input the output of a MSA program into
an analysis program!
ALWAYS inspect the alignment to improve it.
Multiple Sequence Alignment
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Multiple Sequence Alignment Editors• MacVector
• Commercial software• MegAlign (Lasergene)
• Commercial software• AliView
• Public domain• GeneDoc
• Public domain• BioEdit
• Public domain
Multiple Sequence Alignment
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ClustalW http://www.clustal.org/
Muscle http://www.drive5.com/muscle/download3.6.html
MAFFThttp://mafft.cbrc.jp/alignment/server/
AliViewhttp://www.ormbunkar.se/aliview/
GeneDoc http://www.nrbsc.org/downloads/
BioEdit http://www.mbio.ncsu.edu/BioEdit/bioedit.html
Web Resources
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• BLAST search for contig0001 homologs
• Download selected sequence records
• Align sequence records with Clustal2
Recapitulation
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Seminar Follow-Up Site
For access to past recordings, handouts, slides visit this site from the NIH network: http://collab.niaid.nih.gov/sites/research/SIG/Bioinformatics/
1. Select a Subject Matter
View:• Seminar Details
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Login• If prompted to log in use
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Retrieving Slides/Handouts
This lecture series
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Retrieving Slides/Handouts
This lecture
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Questions?
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Next LectureTuesday, 10 November at 0930