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Arthur Gruber. The biological meaning of pairwise alignments. Instituto de Ciências Biomédicas Universidade de São Paulo. AG-ICB-USP. What is a pairwise alignment?. Comparison of 2 sequences – nucleotide or protein sequences

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Arthur gruber

Arthur Gruber

The biological meaning of pairwise alignments

Instituto de Ciências Biomédicas Universidade de São Paulo

AG-ICB-USP


What is a pairwise alignment
What is a pairwise alignment?

  • Comparison of 2 sequences – nucleotide or protein sequences

  • We can compare a sequence to an entire database of sequences – one pairwise alignment at a time

  • Different types of alignments – global and local alignment

  • Different algorithms – Needleman-Wunsch, Smith-Waterman, FastA, BLAST

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Pairwise alignment
Pairwise alignment

  • Output: alignment of similar blocks or whole sequences

gi|3323386|gb|U85705.1|IFU85705 Isospora felis 28S large subunit ribosomal RNA gene, complete sequence Length = 3227 Score = 218 bits (110), Expect = 2e-54 Identities = 146/158 (92%) Strand = Plus / Minus

Query: 3 cacttttaactctctttccaaagtccttttcatctttccttcacagtacttgttcactat 62

||||||||||||||||||||||| |||||||||||||| |||| ||||||||| ||||

Sbjct: 386 cacttttaactctctttccaaagaacttttcatctttccctcacggtacttgtttgctat 327

Query: 63 cggtctcacgccaatatttagctttacgtgaaacttatcacacattttgcgctcaaatcc 122

||||||||||||||||||||||||| |||||||||||||||||||||||||||||||||

Sbjct: 326 cggtctcgcgccaatatttagctttatgtgaaacttatcacacattttgcgctcaaatcc 267

Query: 123 caatgaacgcgactcaataaaagcgcaccgtacgtgga 160

| ||||||||||||| ||||| ||| ||||||||||||

Sbjct: 266 cgatgaacgcgactctataaaggcgtaccgtacgtgga 229

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Some applications of pairwise alignments
Some applications of pairwise alignments

  • Annotation – description of the characteristics of a sequence

  • Function ascribing – similar sequences MAY share similar functions

  • Identification of structural domains – similar sequences MAY share similar structures

  • Identification of protein domains – defines protein architecture

  • Phylogenetic inference – identification of similar sequences that MAY have a common ancestry

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Some applications of pairwise alignments1
Some applications of pairwise alignments

  • Identification of contaminant sequences in a sequencing project – query sequence x databases (bacterial, ribosomal, mitochondrial, etc.)

  • Identification of vector sequences in sequencing reads – alignment and masking

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Identity similarity homology
Identity, similarity, homology

  • Identity – refers to nucleotide or amino acid residues that are identical

  • Similarity - measurable quantity: percentage of identities between two sequences, percentage of similar amino acid residues (conserved along the evolution).

  • Homology – based on a evolutionary conclusion that implies that two sequences has a common ancestral sequence. They are said to share the same evolutionary history. Homology is not quantitative. Two sequences can be or not to be homologous.

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Identity similarity homology1
Identity, similarity, homology

  • A high degree of similarity between two sequences MAY suggest that they share a common evolutionary history. Other analyses and experimental work should be done to validate such hypothesis

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Contaminant removal
Contaminant removal

Other organisms and/or cells – co-purification

Bacterial DNA - E. coli used as the host cell

Human – contamination during manipulation

Other genomes being manipulated in the lab – cross-contamination

Libraries can be contaminated by different sources

Genomic libraries:

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Contaminant removal1
Contaminant removal

All sources already mentioned

Ribosomal RNA – co-purification with the polyA fraction

Organelle transcripts – mitochondrion, plastid

Libraries can be contaminated by different sources

EST libraries:

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Vector masking
Vector masking

A typical read contains sequence stretches that are not originally part of the insert

insert

Sequencing reaction

Vector

sequence

Vector

sequence

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Vector masking1
Vector masking

“X” bases will not be taken into account by assembly/clustering programs

Masking consists in a substitution of bases that are not part of the insert by Xs

insert

Vector

sequence

Vector

sequence

insert

xxxxxxxxx

xxxxxxxxxxxxxxxx

Vector

sequence

Vector

sequence

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Aligning two sequences
Aligning Two Sequences

Human Hemoglobin (HH):

VLSPADKTNVKAAWGKVGAHAGYEG

Sperm Whale Myoglobin (SWM):

VLSEGEWQLVLHVWAKVEADVAGHG

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Aligning two sequences1

(HH)VLSPADKTNVKAAWGKVGAHAGYEG

||| | | || | |

(SWM)VLSEGEWQLVLHVWAKVEADVAGHG

Gap Weight: 12

Length Weight: 4

Gaps: 0

Percent Similarity: 40.000

Percent Identity: 36.000

Matrix: blosum62

Aligning Two Sequences

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Gap insertion deletion
Gap Insertion/Deletion

(HH)VLSPADKTNVKAAWGKVGAH-AGYEG



(SWM)VLSEGEWQLVLHVWAKVEADVAGH-G

-gap insertion/deletion

Gap Weight: 4

Length Weight: 1

Gaps: 2

Percent Similarity: 54.167

Percent Identity: 45.833

BLOSUM62

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Scoring
Scoring

(HH)VLSPADKTNVKAAWGKVGAH-AGYEG

|||| | || || |

(SWM)VLSEGEWQLVLHVWAKVEADVAGH-G

The score of the alignment is:

Matrix valueat (V,V) + (L,L) + (S,S) + (P,E) + …(penalty forgap insertion/deletion)*gaps(penalty forgap extension)*(total length of all gaps)

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Scoring system
Scoring System

  • Identity:An objective and quite well defined measureCount thenumber of identical matches, divide bylength of aligned region

  • Similarity:A less well defined measure

    Category Amino acid

    Acids and Amides Asp (D) Glu(E) Asn (N) Gln (Q)

    Basic His (H) Lys (K) Arg (R)

    Aromatic Phe (F) Tyr (Y) Trp (W)

    Hydrophilic Ala (A) Cys (C) Gly (G) Pro (P) Ser (S) Thr (T)

    Hydrophobic Ile (I) Leu (L) Met (M) Val (V)

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Scoring system1
Scoring system

Rates of amino acid substitution are not uniform

Some amino acids are more conserved than others (e.g. C, H, W compared to A, L, I)

Some substitutions are more common than others

(e.g. A I, A L compared to D L)

Conclusion: there are evolutionary pressures that probably reflect structural and functional constraints

Scoring matrices – matrices that are used for scoring amino acid substitutions in pairwise alignments

They reflect substitution rates that are originated by evolutionary events

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Amino acids chemical relationships
Amino acids - chemical relationships

Tiny

Alphatic

P

A

G

Hydrophobic

OH

I

L

S

C

V

Polar

T

Y

M

F

Hydrophilic

W

K

D

N

H

NH2

R

E

K

Aromatic

Charged

Positive

Negative

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PAM

  • Stands for Point Accepted Mutation

  • Dayhoff Matrix, 1978

  • A series ofmatricesdescribing the extent to which two amino acids have been interchanged inevolution

  • Very similar sequences werealigned, phylogenetic trees were built, and ancestral sequences were reconstructed

  • Out of these alignments, thefrequency of substitutionbetween each pair of amino acids was calculated. Using this information,PAM matriceswere built (PAM1 i.e. one accepted point mutation per 100 amino acids).

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PAM250 - amino acid substitution matrix

GAP_CREATE 12

GAP_EXTEND 4

A B C D E F G H I K L M N P Q R S T V W

A 2 0 -2 0 0 -4 1 -1 -1 -1 -2 -1 0 1 0 -2 1 1 0 -6

B 0 2 -4 3 2 -5 0 1 -2 1 -3 -2 2 -1 1 -1 0 0 -2 -5

C -2 -4 12 -5 -5 -4 -3 -3 -2 -5 -6 -5 -4 -3 -5 -4 0 -2 -2 -8

D 0 3 -5 4 3 -6 1 1 -2 0 -4 -3 2 -1 2 -1 0 0 -2 -7

E 0 2 -5 3 4 -5 0 1 -2 0 -3 -2 1 -1 2 -1 0 0 -2 -7

F -4 -5 -4 -6 -5 9 -5 -2 1 -5 2 0 -4 -5 -5 -4 -3 -3 -1 0

G 1 0 -3 1 0 -5 5 -2 -3 -2 -4 -3 0 -1 -1 -3 1 0 -1 -7

H -1 1 -3 1 1 -2 -2 6 -2 0 -2 -2 2 0 3 2 -1 -1 -2 -3

I -1 -2 -2 -2 -2 1 -3 -2 5 -2 2 2 -2 -2 -2 -2 -1 0 4 -5

K -1 1 -5 0 0 -5 -2 0 -2 5 -3 0 1 -1 1 3 0 0 -2 -3

L -2 -3 -6 -4 -3 2 -4 -2 2 -3 6 4 -3 -3 -2 -3 -3 -2 2 -2

M -1 -2 -5 -3 -2 0 -3 -2 2 0 4 6 -2 -2 -1 0 -2 -1 2 -4

N 0 2 -4 2 1 -4 0 2 -2 1 -3 -2 2 -1 1 0 1 0 -2 -4

P 1 -1 -3 -1 -1 -5 -1 0 -2 -1 -3 -2 -1 6 0 0 1 0 -1 -6

Q 0 1 -5 2 2 -5 -1 3 -2 1 -2 -1 1 0 4 1 -1 -1 -2 -5

R -2 -1 -4 -1 -1 -4 -3 2 -2 3 -3 0 0 0 1 6 0 -1 -2 2

S 1 0 0 0 0 -3 1 -1 -1 0 -3 -2 1 1 -1 0 2 1 -1 -2

T 1 0 -2 0 0 -3 0 -1 0 0 -2 -1 0 0 -1 -1 1 3 0 -5

V 0 -2 -2 -2 -2 -1 -1 -2 4 -2 2 2 -2 -1 -2 -2 -1 0 4 -6

W -6 -5 -8 -7 -7 0 -7 -3 -5 -3 -2 -4 -4 -6 -5 2 -2 -5 -6 17

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Blosum
BLOSUM

Stands forBlocksSubstitution Matrices

Henikoff and Henikoff, 1992

A series of matrices describing the extent to whichtwo amino acids are interchangeablein conserved structures

Built by extracting replacement information from the alignments in the BLOCKS database.

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Blosum1
BLOSUM

The number in the series (BLOSUM62) represents the thresholdpercentsimilarity between sequences, for considering them in the calculation.

For example,BLOSUM62is derived from an alignment of sequences that share62% similarity, BLOSUM45 is based on 45% sequence similarity in aligned sequences

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BLOSUM62 - amino acid substitution matrix

Reference: Henikoff, S. and Henikoff, J. G. (1992). Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89: 10915-10919.

A R N D C Q E G H I L K M F P S T W Y V B Z X *A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -4 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 -4 * -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1

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Guidelines
Guidelines

Lower PAMsandhigher Blosumsfind short local alignment of highly similar sequences

Higher PAMsand lower Blosumsfind longer weaker local alignment

No single matrix answers all questions

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Blast b asic l ocal a lignment s earch t ool
BLAST – Basic Local Alignment Search Tool

  • Algorithm first described in 1990

    Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. (1990) "Basic local alignment search tool." J. Mol. Biol.215:403-410.

  • And improved in 1997

    Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W. & Lipman, D.J.(1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res.25: 3389-3402.

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Blast search four components
Blast search – four components

  • Search purpose/goal

  • Program

  • Query sequence

  • Database

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Blast search purpose goal
BLAST – search purpose/goal

  • What is the biological question? Examples:

    • Which proteins of the database are similar to my protein sequence?

    • Which proteins of the database are similar to the conceptual translation of my DNA sequence?

    • Which nucleotide sequences in the database are similar to my nucleotide sequence?

    • Which proteins coded by the conceptual translation of the database sequences are similar to my protein sequence?

    • Which proteins coded by the conceptual translation of the database sequences are similar to the conceptual translation of my DNA sequence?

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Blast search purpose goal1
BLAST – search purpose/goal

  • Which proteins of the database are similar to my protein sequence?

    • I have sequenced a gene and derived the protein sequence by concetpual translation. Alternatively, I obtained the protein sequence directly. I am now interested to find out its possible fnction.

    • Using a similarity search, I can find protein sequences in databases that are similar to mine: orthologs and paralogs.

    • BLASTP – protein query x protein database

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Blast search purpose goal2
BLAST - search purpose/goal

  • Which proteins of the database are similar to the conceptual translation of my DNA sequence?

    • I have sequenced an EST (expressed sequence tag) that contains a protein coding region.

    • I am interested to find out which proteins of the database are similar to the conceptual translation of my nucleic acid sequence.

    • BLASTX – nucleotide (translated) query x protein database

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Blast search purpose goal3
BLAST – search purpose/goal

  • Which nucleotide sequences of the database are similar to my DNA sequence?

    • I have sequenced a DNA fragment.

    • I am interested to find out which DNA sequences of the database are similar to my nucleic acid sequence.

    • BLASTN – nucleotide query x nucleotide database

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Blast search purpose goal4
BLAST - search purpose/goal

  • Which proteins translated from a nucleic acid database are similar to the conceptual translation of my DNA sequence?

    • I have sequenced an EST (expressed sequence tag) that contains a protein coding region.

    • I am interested to find out which ESTs of other organisms may be coding for homologous proteins.

    • TBLASTX – nucleotide (translated) query x nucleotide (translated) database

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Blast search purpose goal5
BLAST – search purpose/goal

  • Which proteins coded by the conceptual translation of the database sequences are similar to my protein sequence?

    • I have a protein sequence on hands and am interested to find out which genes of other organisms may be coding for homologous proteins.

    • TBLASTN – protein query x nucleotide (translated) database

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Blast programs
BLAST - programs

  • BLASTP – protein query x protein database

  • BLASTN – nucleotide query x nucleotide database

  • BLASTX – nucleotide (translated) query x protein database

  • TBLASTN – protein query x nucleotide (translated) database

  • TBLASTX – nucleotide query (translated) x nucleotide (translated) database

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Fasta format
FastA format

The first line begins with the symbol '>' followed by the name of the sequence

The sequence is on the remaining lines.

The sequence must not contain blanks.

The sequence could be in upper or lower case.

Below is an example sequence in FASTA format:\

>DNA sequence

GCCCCCGGCCCCGCCCCGGCCCCGCCCCCGGCCCCGCCCCGCAAGGGTC

ACAGGTCACGGGGCGGGGCCGAGGCGGAAGCGCCCGCAGCCCGGTACCG

GCTCCTCCTGGGCTCCCTCTAGCGCCTTCCCCCCGGCCCGACTCCGCTG

GTCAGCGCCAAGTGACTTACGCCCCCGACCTCTGAGCCCGGACCGCTAG

BLAST – query sequence

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Blast database
BLAST – database

  • Nucleotide databases

    • nr, refseq, est_human, est_mouse, est_others, wgs, etc.

  • Protein databases – nr, Swiss-Prot, refseq, etc.

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Blast programs1
Blast programs

  • PSI-BLAST – Position-Specific Iterated BLAST program - performs an iterative search in which sequences found in one round of searching are used to build a score model for the next round of searching. In PSI-BLAST the algorithm is not tied to a specific score matrix.

  • PHI-BLAST – Pattern-Hit Initiated BLAST -a search program that combines matching of regular expressions with local alignments surrounding the match.

  • MEGABLAST – uses the greedy algorithm for nucleotide sequence alignment search - it can be up to 10 times faster than more common sequence similarity programs and handles much longer DNA sequences than the blastn program

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