1. Roadmap The topics:  basic concepts of molecular biology  more on Perl  overview of the field  biological databases and database searching  sequence alignments  phylogenetics  structure prediction  microarray data analysis

2. Sequence alignments Introduction Introduction What is an alignment? What is an alignment? Why do alignments? Why do alignments? A bit of history A bit of history Dot matrix comparison Dot matrix comparison Scoring alignments Scoring alignments Alignment methods Alignment methods Significance of alignments Significance of alignments

3. What is Sequence alignment Sequence alignment is an arrangement of two or more sequences, highlighting their similarity.

4. Why do alignments? Sequence Alignment is useful for discovering structural, functional and evolutional information in biological sequences.

5. Over time, genes accumulate mutations Environmental factors Radiation Oxidation Mistakes in replication/repair Deletions, Duplications Insertions Inversions Point mutations

6. Comparing two sequences Point mutations, easy: ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT Point mutations, easy: ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGATTCGCCCTATCGTCTATCT Insertions/deletions, must align: ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT Insertions/deletions, must align: ACGTCTGATACGCCGTATAGTCTATCT CTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT ----CTGATTCGC---ATCGTCTATCT

7. Sequence Alignment Doolittle RF, Hunkapiller MW, Hood LE, Devare SG, Robbins KC, Aaronson SA, Antoniades HN. Science 221:275-277, 1983. A sequence for platelet derived growth factor (PDGF) from mammalian cells was virtually identical to the sequence for the retrovirus encoded oncogene known as v-sis (gene causing cancer in animals). Retrovirus had acquired the gene from the host cell as some kind of genetic exchange event and then had produced a mutant that could alter the function of the normal protein when it infected another animal. Russell F. Doolittle

8. Dot Matrix Comparison A: T C A G A G G T C T G B: T C A G A G C T G X X C X X T XXXXG XXT XC XXXXG XXA XXXXG XXA XC XXT GTGGAGACT

9. Interpretation of dot matrix Regions of similarity appear as diagonal runs of dots Regions of similarity appear as diagonal runs of dots Reverse diagonals (perpendicular to diagonal) indicate inversions Reverse diagonals (perpendicular to diagonal) indicate inversions Can link or "join" separate diagonals to form alignment with "gaps" Can link or "join" separate diagonals to form alignment with "gaps"

10. More on Dot Matrix Improving detection of matching regions by filtering Improving detection of matching regions by filtering using sliding window to compare the two sequences. For example, print a dot at a matrix position only if using sliding window to compare the two sequences. For example, print a dot at a matrix position only if 7 out of the next 11 positions in the sequence are identical 7 out of the next 11 positions in the sequence are identical Similarity score of the next 11 positions in the sequence is greater than 5. Similarity score of the next 11 positions in the sequence is greater than 5.

11. Sequence repeats Many sequences contains repetitive regions. Many sequences contains repetitive regions. a retrovirus vector sequence against itself using a window size of 9 and mismatch limit of 2 ( http://arbl.cvmbs.colostate.edu/molkit/dnadot/bkg.html)

12. More on Dot Matrix Dot matrix graphically presents regions of identity or similarity between two sequences Dot matrix graphically presents regions of identity or similarity between two sequences The use of windows and thresholds can reduce “noise” in dot matrix The use of windows and thresholds can reduce “noise” in dot matrix Inversions and duplications have unique “signatures” in dot matrix Inversions and duplications have unique “signatures” in dot matrix

13. Software Dotlet (java applet)– Dotlet (java applet)– www.ch.embnet.org Dnadot – Dnadot – arbl.cvmbs.colostate.edu/molkit/dnadot/ Dotter – Dotter – www.cgr.ki.se/cgr/groups/sonnhammer/Dotter.html Dottup – Dottup – www.emboss.org

14. How to measure the similarity Basically three kinds of changes can occur at any given position within a sequence: Mutation Mutation Insertion Insertion Deletion Deletion Insertion and deletion have been found to occur in nature at a significantly lower frequency than mutations. Insertion and deletion have been found to occur in nature at a significantly lower frequency than mutations.

15. Scoring Matrices for Aligning DNA Sequences Transition --- substitutions in which a purine (A/G) is replaced by another purine (A/G) or a pyrimadine (C/T) is replaced by another pyrimadine (C/T). Transversions --- (A/G)  (C/T) 1000G 0100C 0010T 0001AGCTA Identity matrix 5-4-4-4G -45-4-4C -4-45-4T -4-4-45AGCTA BLAST matrix 1-5-5G -51-5C -51-5T-5-51AGCTA Transition-Transversion matrix

16. Scoring a sequence alignment Match score:+1 Match score:+1 Mismatch score:+0 Mismatch score:+0 Gap penalty:–1 Gap penalty:–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Matches: 18 × (+1) Mismatches: 2 × 0 Mismatches: 2 × 0 Gaps: 7 × (– 1) Gaps: 7 × (– 1) Score = +11

17. Gap opening and extension penalties We want to find alignments that are evolutionarily likely. We want to find alignments that are evolutionarily likely. Which of the following alignments seems more likely to you? Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCT ACGTCTGAT-------ATAGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT AC-T-TGA--CG-CGT-TA-TCTATCT We can achieve this by penalizing more for a new gap, than for extending an existing gap We can achieve this by penalizing more for a new gap, than for extending an existing gap  

18. Scoring a sequence alignment Match/mismatch score:+1/+0 Match/mismatch score:+1/+0 Open/extension penalty:–2/–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Open/extension penalty:–2/–1 ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || |||||||| ----CTGATTCGC---ATCGTCTATCT Matches: 18 × (+1) Matches: 18 × (+1) Mismatches: 2 × 0 Mismatches: 2 × 0 Open: 2 × (–2) Open: 2 × (–2) Extension: 5 × (–1) Extension: 5 × (–1) Score = +9

19. Amino Acid Substitution Matrices PAM - point accepted mutation based on global alignment [evolutionary model] BLOSUM - block substitutions based on local alignments [similarity among conserved sequences]

20. Part of PAM 250 Matrix CSTPAG C12 S02 T-213 P-3106 A-21112 G-31015 Log-odds = log ( ) chance to see the pair in homologous proteins chance to see the pair in unrelated proteins by chance

21. PAM matrices PAM 1 Matrix reflects an amount of evolution producing on average one mutation per hundred amino acids (1 unit evolution). PAM 250 --- 250 unit evolution 0.01 … 0.01 0.02 0.010.04 Probability PAM 250 0.0000 Phe to Cys …... 0.0000 Phe to Asp 0.0001 Phe to Asn 0.0001 Phe to Arg 0.0002 Phe to Ala PAM 1 Amino acid change

22. Limitations of PAM Matrices Constructed based on the phylogenetic relationships prior to scoring mutations; Constructed based on the phylogenetic relationships prior to scoring mutations; Difficulty of determining ancestral relationships among sequences; Difficulty of determining ancestral relationships among sequences; Based on a small set of closely related proteins; Based on a small set of closely related proteins; …

23. BLOSUM Matrices Based on the observed amino acid substitutions in a large set of ~2000 conserved amino acid patterns (blocks). The blocks are found in a database of protein sequences representing more than 500 families of related proteins and act as signatures of these protein families. Based on the observed amino acid substitutions in a large set of ~2000 conserved amino acid patterns (blocks). The blocks are found in a database of protein sequences representing more than 500 families of related proteins and act as signatures of these protein families. The matrices are measured on the multiple alignment of the blocks. The matrices are measured on the multiple alignment of the blocks. The entries of the matrices are computed based on the same principle used in PAM -- log(odds’ ratio). The entries of the matrices are computed based on the same principle used in PAM -- log(odds’ ratio).

24. Part of BLOSUM 62 Matrix CSTPAG C9 S4 T15 P-37 A0104 G-30-2-206 BLOSUM62 was measured on pairs of sequences with an average of 62 % identical amino acids. BLOSUM62 was measured on pairs of sequences with an average of 62 % identical amino acids. Log-odds = log ( ) chance to see the pair in homologous proteins chance to see the pair in unrelated proteins by chance

25. PAM vs. BLOSUM PAM PAM Based on mutational model of evolution ( Markov process ) Based on mutational model of evolution ( Markov process ) PAM1 is based on sequences of 85% similarity PAM1 is based on sequences of 85% similarity Designed to track the evolutionary origins Designed to track the evolutionary origins BLOSUM BLOSUM Based on the multiple alignment of blocks Based on the multiple alignment of blocks Good to be used to compare distant sequences Good to be used to compare distant sequences Designed to find proteins’ conserved domains Designed to find proteins’ conserved domains

26. Gap Penalty Optimal penalties vary from sequence to sequence, and finding the most adequate value is a matter of empirical trial and error. Optimal penalties vary from sequence to sequence, and finding the most adequate value is a matter of empirical trial and error. When compare distantly related sequences, a high gap- opening penalty and a very low gap-extension penalty often give better results When compare distantly related sequences, a high gap- opening penalty and a very low gap-extension penalty often give better results When compare closely related sequences, gaps should be penalized on both a gap-opening and gap-extension When compare closely related sequences, gaps should be penalized on both a gap-opening and gap-extension