The biological meaning of pairwise alignments

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The biological meaning of pairwise alignments

• Arthur Gruber

Instituto de Ciências Biomédicas Universidade de
São Paulo
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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

• Output alignment of similar blocks or whole
  sequences

gi3323386gbU85705.1IFU85705 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 cacttttaactctctttccaaa
gtccttttcatctttccttcacagtacttgttcactat 62

Sbjct 386 cacttttaactctctttccaaag
aacttttcatctttccctcacggtacttgtttgctat 327
Query 63 cggtctcacgccaatatttagctttacgtgaaacttatca
cacattttgcgctcaaatcc 122

Sbjct 326 cggtctcgcgccaatatttagctttatg
tgaaacttatcacacattttgcgctcaaatcc 267 Query 123
caatgaacgcgactcaataaaagcgcaccgtacgtgga 160

Sbjct 266 cgatgaacgcgactctataaaggcgtaccgtacgtgga
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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 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 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, 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
Libraries can be contaminated by different sources
Genomic libraries

• 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

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Contaminant removal
Libraries can be contaminated by different sources
EST libraries

• All sources already mentioned
• Ribosomal RNA co-purification with the polyA
  fraction
• Organelle transcripts mitochondrion, plastid

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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 masking
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

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

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Aligning Two Sequences

• Human Hemoglobin (HH)
• VLSPADKTNVKAAWGKVGAHAGYEG
• Sperm Whale Myoglobin (SWM)
• VLSEGEWQLVLHVWAKVEADVAGHG

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Aligning Two Sequences

• (HH) VLSPADKTNVKAAWGKVGAHAGYEG
• (SWM) VLSEGEWQLVLHVWAKVEADVAGHG
• Gap Weight 12
• Length Weight 4
• Gaps 0
• Percent Similarity 40.000
• Percent Identity 36.000
• Matrix blosum62

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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

• (HH) VLSPADKTNVKAAWGKVGAH-AGYEG
• (SWM) VLSEGEWQLVLHVWAKVEADVAGH-G
• The score of the alignment is
• Matrix value at (V,V) (L,L) (S,S) (P,E)
  (penalty for gap insertion/deletion)gaps
  (penalty for gap extension)(total
  length of all gaps)

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Scoring System

• Identity An objective and quite well defined
  measure Count the number of identical matches,
  divide by length 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 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
Tiny
Alphatic
A
G
P
Hydrophobic
OH
L
I
S
C
V
Polar
T
Y
F
M
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 of matrices describing the extent to
  which two amino acids have been interchanged in
  evolution
• Very similar sequences were aligned, phylogenetic
  trees were built, and ancestral sequences were
  reconstructed
• Out of these alignments, the frequency of
  substitution between each pair of amino acids was
  calculated. Using this information, PAM matrices
  were 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
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BLOSUM

• Stands for Blocks Substitution Matrices
• Henikoff and Henikoff, 1992
• A series of matrices describing the extent to
  which two amino acids are interchangeable in
  conserved structures
• Built by extracting replacement information from
  the alignments in the BLOCKS database.

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BLOSUM

• The number in the series (BLOSUM62) represents
  the threshold percent similarity between
  sequences, for considering them in the
  calculation.
• For example, BLOSUM62 is derived from an
  alignment of sequences that share 62 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

• Lower PAMs and higher Blosums find short local
  alignment of highly similar sequences
• Higher PAMs and lower Blosums find longer weaker
  local alignment
• No single matrix answers all questions

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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. 215403-410.
• 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

• Search purpose/goal
• Program
• Query sequence
• Database

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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/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/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/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/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/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

• 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
BLAST query sequence

• The first line begins with the symbol 'gt'
  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\
• gtDNA sequence
• GCCCCCGGCCCCGCCCCGGCCCCGCCCCCGGCCCCGCCCCGCAAGGGTC
• ACAGGTCACGGGGCGGGGCCGAGGCGGAAGCGCCCGCAGCCCGGTACCG
• GCTCCTCCTGGGCTCCCTCTAGCGCCTTCCCCCCGGCCCGACTCCGCTG
• GTCAGCGCCAAGTGACTTACGCCCCCGACCTCTGAGCCCGGACCGCTAG

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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 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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