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Roadmap

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. Sequence alignments. Introduction What is an alignment?

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Roadmap

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  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 • What is an alignment? • Why do alignments? • A bit of history • Dot matrix comparison • Scoring alignments • Alignment methods • 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:ACGTCTGATACGCCGTATAGTCTATCTACGTCTGATTCGCCCTATCGTCTATCT • Insertions/deletions, must align:ACGTCTGATACGCCGTATAGTCTATCTCTGATTCGCATCGTCTATCT ACGTCTGATACGCCGTATAGTCTATCT----CTGATTCGC---ATCGTCTATCT

  7. Russell F. Doolittle Sequence Alignment Doolittle RF, Hunkapiller MW, Hood LE, Devare SG, Robbins KC, Aaronson SA, Antoniades HN. Science221:275-277, 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.

  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 T C A G A G G T C T G T X X X C X X A X X G X X X X A X X G X X X X C X X T X X X G X X X X

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

  10. More on Dot Matrix • 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 • 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.

  11. Sequence repeats • 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 • The use of windows and thresholds can reduce “noise” in dot matrix • Inversions and duplications have unique “signatures” in dot matrix

  13. Software • Dotlet (java applet)– www.ch.embnet.org • Dnadot – arbl.cvmbs.colostate.edu/molkit/dnadot/ • Dotter – www.cgr.ki.se/cgr/groups/sonnhammer/Dotter.html • 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 • Insertion • Deletion • Insertion and deletion have been found to occur in nature at a significantly lower frequency than mutations.

  15. A T C G A T C G A T C G A 5 -4 -4 -4 A 1 -5 -5 -1 A 1 0 0 0 T 0 1 0 0 T -4 5 -4 -4 T -5 1 -1 -5 C -4 -4 5 -4 C -5 -1 1 -5 C 0 0 1 0 G 0 0 0 1 G -4 -4 -4 5 G -1 -5 -5 1 Identity matrix BLAST matrix Transition-Transversion matrix 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)

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

  17. Gap opening and extension penalties • We want to find alignments that are evolutionarily likely. • Which of the following alignments seems more likely to you? ACGTCTGATACGCCGTATAGTCTATCTACGTCTGAT-------ATAGTCTATCTACGTCTGATACGCCGTATAGTCTATCTAC-T-TGA--CG-CGT-TA-TCTATCT • 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 • Open/extension penalty: –2/–1ACGTCTGATACGCCGTATAGTCTATCT ||||| ||| || ||||||||----CTGATTCGC---ATCGTCTATCT • Matches: 18 × (+1) • Mismatches: 2 × 0 • Open: 2 × (–2) • 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. chance to see the pair in homologous proteins chance to see the pair in unrelated proteins by chance Log-odds = log ( ) Part of PAM 250 Matrix

  21. Amino acid change Probability PAM 1 PAM 250 Phe to Ala 0.0002 0.04 Phe to Arg 0.0001 0.01 Phe to Asn 0.0001 0.02 Phe to Asp 0.0000 0.01 Phe to Cys 0.0000 0.01 ... … … 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

  22. Limitations of PAM Matrices • Constructed based on the phylogenetic relationships prior to scoring mutations; • Difficulty of determining ancestral relationships among sequences; • 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. • 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).

  24. chance to see the pair in homologous proteins chance to see the pair in unrelated proteins by chance Log-odds = log ( ) Part of BLOSUM 62 Matrix • BLOSUM62 was measured on pairs of sequences with an average of 62 % identical amino acids.

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

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