The biological meaning of pairwise alignments Arthur Gruber
The biological meaning of pairwise alignments Arthur Gruber Instituto de Ciências Biomédicas Universidade de São Paulo AG-ICB-USP
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 – Waterman, Fast. A, BLAST Needleman-Wunsch, Smith- AG-ICB-USP
Pairwise alignment • Output: alignment of similar blocks or whole sequences gi|3323386|gb|U 85705. 1|IFU 85705 Isospora felis 28 S large subunit ribosomal RNA gene, complete sequence Length = 3227 Score = 218 bits (110), Expect = 2 e-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 cgatgaacgcgactctataaaggcgtacgtgga 229 AG-ICB-USP
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 architecture of protein domains – defines protein • Phylogenetic inference – identification of similar sequences that MAY have a common ancestry AG-ICB-USP
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 AG-ICB-USP
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. AG-ICB-USP
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 AG-ICB-USP
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 – crosscontamination AG-ICB-USP
Contaminant removal Libraries can be contaminated by different sources EST libraries: • All sources already mentioned • Ribosomal RNA – co-purification with the poly. A fraction • Organelle transcripts – mitochondrion, plastid AG-ICB-USP
Vector masking A typical read contains sequence stretches that are not originally part of the insert Sequencing reaction Vector sequence AG-ICB-USP
Vector masking Masking consists in a substitution of bases that are not part of the insert by Xs insert Vector sequence xxxxx Vector sequence insert xxxxxxxx Vector sequence • “X” bases will not be taken assembly/clustering programs into account by AG-ICB-USP
Aligning Two Sequences Human Hemoglobin (HH): VLSPADKTNVKAAWGKVGAHAGYEG Sperm Whale Myoglobin (SWM): VLSEGEWQLVLHVWAKVEADVAGHG AG-ICB-USP
Aligning Two Sequences (HH) VLSPADKTNVKAAWGKVGAHAGYEG ||| | | (SWM) VLSEGEWQLVLHVWAKVEADVAGHG • Gap Weight: • Length Weight: • Gaps: • Percent Similarity: 40. 000 • Percent Identity: • Matrix: blosum 62 12 4 0 36. 000 AG-ICB-USP
Gap Insertion/Deletion (HH) VLSPADKTNVKAAWGKVGAH-AGYEG (SWM) VLSEGEWQLVLHVWAKVEADVAGH-G - gap insertion/deletion • Gap Weight: • Length Weight: • Gaps: • Percent Similarity: 54. 167 • Percent Identity: • BLOSUM 62 4 1 2 45. 833 AG-ICB-USP
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) AG-ICB-USP
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) AG-ICB-USP
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 AG-ICB-USP
Amino acids - chemical relationships Tiny Alphatic P Hydrophobic I A L S V M F H Aromatic Positive OH C Polar T Y W G Hydrophilic K D N R E K Negative NH 2 Charged AG-ICB-USP
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 (PAM 1 i. e. one accepted point mutation per 100 amino acids). AG-ICB-USP
PAM 250 - amino acid substitution matrix GAP_CREATE 12 GAP_EXTEND 4 A B C D E A 2 0 -2 0 0 B 0 2 -4 3 2 C -2 -4 12 -5 -5 D 0 3 -5 4 3 E 0 2 -5 3 4 F -4 -5 -4 -6 -5 G 1 0 -3 1 0 H -1 1 -3 1 1 I -1 -2 -2 K -1 1 -5 0 0 L -2 -3 -6 -4 -3 M -1 -2 -5 -3 -2 N 0 2 -4 2 1 P 1 -1 -3 -1 -1 Q 0 1 -5 2 2 R -2 -1 -4 -1 -1 S 1 0 0 T 1 0 -2 0 0 V 0 -2 -2 W -6 -5 -8 -7 -7 F G H I K -4 1 -1 -5 0 1 -2 1 -4 -3 -3 -2 -5 -6 1 1 -2 0 -5 0 1 -2 0 9 -5 -2 1 -5 -5 5 -2 -3 -2 -2 -2 6 -2 0 1 -3 -2 5 -2 -5 -2 0 -2 5 2 -4 -2 2 -3 0 -3 -2 2 0 -4 0 2 -2 1 -5 -1 0 -2 -1 -5 -1 3 -2 1 -4 -3 2 -2 3 -3 1 -1 -1 0 -3 0 -1 0 0 -1 -1 -2 4 -2 0 -7 -3 -5 -3 L M N P Q -2 -1 0 -3 -2 2 -1 1 -6 -5 -4 -3 2 -1 2 -3 -2 1 -1 2 2 0 -4 -5 -5 -4 -3 0 -1 -1 -2 -2 2 0 3 2 2 -2 -3 0 1 -1 1 6 4 -3 -3 -2 4 6 -2 -2 -1 -3 -2 2 -1 1 -3 -2 -1 6 0 -2 -1 1 0 4 -3 0 0 0 1 -3 -2 1 1 -1 -2 -1 0 0 -1 2 2 -2 -1 -2 -2 -4 -4 -6 -5 R S T V W -2 1 1 0 -6 -1 0 0 -2 -5 -4 0 -2 -2 -8 -1 0 0 -2 -7 -4 -3 -3 -1 0 -3 1 0 -1 -7 2 -1 -1 -2 -3 -2 -1 0 4 -5 3 0 0 -2 -3 -3 -3 -2 2 -2 0 -2 -1 2 -4 0 1 0 -1 -6 1 -1 -1 -2 -5 6 0 -1 -2 2 0 2 1 -1 -2 -1 1 3 0 -5 -2 -1 0 4 -6 2 -2 -5 -6 17 AG-ICB-USP
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. AG-ICB-USP
BLOSUM • The number in the series (BLOSUM 62) represents the threshold percent similarity between sequences, for considering them in the calculation. • For example, BLOSUM 62 is derived from an alignment of sequences that share 62% similarity, BLOSUM 45 is based on 45% sequence similarity in aligned sequences AG-ICB-USP
BLOSUM 62 - 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 -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 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 -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 -3 2 4 -2 2 0 -3 -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 -3 -1 -1 -4 F -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 -1 -2 -2 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -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 -2 0 0 -2 -1 -1 -1 -4 * -4 -4 -4 -4 -4 -4 1 AG-ICB-USP
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 AG-ICB-USP
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. AG-ICB-USP
Blast search – four components • Search purpose/goal • Program • Query sequence • Database AG-ICB-USP
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? AG-ICB-USP
BLAST – search purpose/goal • Which proteins of the database are similar to my protein sequence? • I have sequence obtained interested sequenced a gene and derived the protein by concetpual translation. Alternatively, I the protein sequence directly. I am now 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 AG-ICB-USP
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 AG-ICB-USP
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 AG-ICB-USP
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 AG-ICB-USP
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 AG-ICB-USP
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 AG-ICB-USP
BLAST – query sequence Fast. A 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 GCCCCCGGCCCCGCCCCCGGCCCCGCAAGGGTC ACAGGTCACGGGGCCGAGGCGGAAGCGCCCGCAGCCCGGTACCG GCTCCTCCTGGGCTCCCTCTAGCGCCTTCCCCCCGGCCCGACTCCGCTG GTCAGCGCCAAGTGACTTACGCCCCCGACCTCTGAGCCCGGACCGCTAG AG-ICB-USP
BLAST – database • Nucleotide databases • nr, refseq, est_human, est_mouse, est_others, wgs, etc. • Protein databases – nr, Swiss-Prot, refseq, etc. AG-ICB-USP
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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 AG-ICB-USP
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