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The biological meaning of pairwise alignments

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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The biological meaning of pairwise alignments

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  1. Arthur Gruber The biological meaning of pairwise alignments Instituto de Ciências Biomédicas Universidade de São Paulo AG-ICB-USP

  2. 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 AG-ICB-USP

  3. 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 AG-ICB-USP

  4. 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 AG-ICB-USP

  5. 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

  6. 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

  7. 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

  8. 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: AG-ICB-USP

  9. 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: AG-ICB-USP

  10. Vector masking A typical read contains sequence stretches that are not originally part of the insert insert Sequencing reaction Vector sequence Vector sequence AG-ICB-USP

  11. 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 AG-ICB-USP

  12. Aligning Two Sequences Human Hemoglobin (HH): VLSPADKTNVKAAWGKVGAHAGYEG Sperm Whale Myoglobin (SWM): VLSEGEWQLVLHVWAKVEADVAGHG AG-ICB-USP

  13. (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 AG-ICB-USP

  14. 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 AG-ICB-USP

  15. 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) AG-ICB-USP

  16. 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) AG-ICB-USP

  17. 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

  18. 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 AG-ICB-USP

  19. 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). AG-ICB-USP

  20. 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 AG-ICB-USP

  21. 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. AG-ICB-USP

  22. 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 AG-ICB-USP

  23. 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 AG-ICB-USP

  24. 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 AG-ICB-USP

  25. 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

  26. Blast search – four components • Search purpose/goal • Program • Query sequence • Database AG-ICB-USP

  27. 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

  28. 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 AG-ICB-USP

  29. 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

  30. 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

  31. 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

  32. 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

  33. 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

  34. 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 AG-ICB-USP

  35. 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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  46. 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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