1. Graphical comparison of sequences using “Dotplots”. ACCTGCCCTGTCCAGCTTACATGCATGCTTATAGGGGCATTTTACAT ACCTGCCGATTCCATATTACGCATGCTTCTGGGTTACCGTTCAGGGCATTTTACATGTGCTG 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 =9 Basic Principles. A T G C A 1 0 0 0 T 0 1 0 0 G 0 0 1 0 C 0 0 0 1 A “word size” (11 say) A “Scoring scheme” (1 for a match, 0 for a mismatch, say) A “Cut-off score” (8 say)  ATGCTTCTGGG ATGCTTATAGG Diagonal runs of dots indicate similar regions Summary: Dotplots provide a comprehensive overview but NO detail.

2. Graphical comparison of sequences using “Dotplots”. DNA: Simplest Scheme is the Identity Matrix. A T G C A 1 0 0 0 T 0 1 0 0 G 0 0 1 0 C 0 0 0 1 More complex matrices can be used. For example, the default EMBOSS DNA scoring matrix is: A T G C A 5 -4 -4 -4 T -4 5 -4 -4 G -4 -4 5 -4 C -4 -4 -4 5 The use of negative numbers is only pertinent when these matrices are use for computing textual alignments. Using a wider spread of scores eases the Expansion of the scoring matrix to sensibly include ambiguity codes. Scoring Schemes.

3. Graphical comparison of sequences using “Dotplots”. A C G T S W R Y K M B V H D N U A 5 -4 -4 -4 -4 1 1 -4 -4 1 -4 -1 -1 -1 -2 -4 C -4 5 -4 -4 -4 1 -4 1 1 -4 -1 -4 -1 -1 -2 5 G -4 -4 5 -4 1 -4 1 -4 1 -4 -1 -1 -4 -1 -2 -4 T -4 -4 -4 5 1 -4 -4 1 -4 1 -1 -1 -1 -4 -2 -4 S -4 -4 1 1 -1 -4 -2 -2 -2 -2 -1 -1 -3 -3 -1 -4 W 1 1 -4 -4 -4 -1 -2 -2 -2 -2 -3 -3 -1 -1 -1 1 R 1 -4 1 -4 -2 -2 -1 -4 -2 -2 -3 -1 -3 -1 -1 -4 Y -4 -1 -4 1 -2 -2 -4 -1 -2 -2 -1 -3 -1 -3 -1 1 K -4 1 1 -4 -2 -2 -2 -2 -1 -4 -1 -3 -3 -1 -1 1 M 1 -4 -4 1 -2 -2 -2 -2 -4 -1 -3 -1 -1 -3 -1 -4 B -4 -1 -1 -1 -1 -3 -3 -1 -1 -3 -1 -2 -2 -2 -1 -1 V -1 -4 -1 -1 -1 -3 -1 -3 -3 -1 -2 -1 -2 -2 -1 -4 H -1 -1 -4 -1 -3 -1 -3 -1 -3 -1 -2 -2 -1 -2 -1 -1 D -1 -1 -1 -4 -3 -1 -1 -3 -1 -3 -2 -2 -2 -1 -1 -1 N -2 -2 -2 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 U -4 5 -4 -4 -4 1 -4 1 1 -4 -1 -4 -1 -1 -2 5 IUB DNA Alphabet Code Meaning A C G T/U M `aMino` A|C R `puRine` A|G W `Weak` A|T S `Strong` C|G Y `pYrimidine` C|T K `Keto` G|T V `not T` A|C|G H `not G` A|C|T D `not C` A|G|T B `not A` C|G|T N `aNy` A|C|G|T For Protein sequence dotplots more complex scoring schemes are required. Scores must reflect far more than alphabetic identity. A B C D E F G H I K L M N P Q R S T V W Y Z A 2 0 -2 0 0 -4 1 -1 -1 -1 -2 -1 0 1 0 -2 1 1 0 -6 -3 0 B 0 2 -4 3 2 -5 0 1 -2 1 -3 -2 2 -1 1 -1 0 0 -2 -5 -3 2 C -2 -4 12 -5 -5 -4 -3 -3 -2 -5 -6 -5 -4 -3 -5 -4 0 -2 -2 -8 0 -5 D 0 3 -5 4 3 -6 1 1 -2 0 -4 -3 2 -1 2 -1 0 0 -2 -7 -4 3 E 0 2 -5 3 4 -5 0 1 -2 0 -3 -2 1 -1 2 -1 0 0 -2 -7 -4 3 F -4 -5 -4 -6 -5 9 -5 -2 1 -5 2 0 -4 -5 -5 -4 -3 -3 -1 0 7 -5 G 1 0 -3 1 0 -5 5 -2 -3 -2 -4 -3 0 -1 -1 -3 1 0 -1 -7 -6 -1 H -1 1 -3 1 1 -2 -2 6 -2 0 -2 -2 2 0 3 2 -1 -1 -2 -3 0 2 I -1 -2 -2 -2 -2 1 -3 -2 5 -2 2 2 -2 -2 -2 -2 -1 0 4 -5 -1 -2 K -1 1 -5 0 0 -5 -2 0 -2 5 -3 0 1 -1 1 3 0 0 -2 -3 -4 0 L -2 -3 -6 -4 -3 2 -4 -2 2 -3 6 4 -3 -3 -2 -3 -3 -2 2 -2 -1 -3 M -1 -2 -5 -3 -2 0 -3 -2 2 0 4 6 -2 -2 -1 0 -2 -1 2 -4 -2 -2 N 0 2 -4 2 1 -4 0 2 -2 1 -3 -2 2 -1 1 0 1 0 -2 -4 -2 1 P 1 -1 -3 -1 -1 -5 -1 0 -2 -1 -3 -2 -1 6 0 0 1 0 -1 -6 -5 0 Q 0 1 -5 2 2 -5 -1 3 -2 1 -2 -1 1 0 4 1 -1 -1 -2 -5 -4 3 R -2 -1 -4 -1 -1 -4 -3 2 -2 3 -3 0 0 0 1 6 0 -1 -2 2 -4 0 S 1 0 0 0 0 -3 1 -1 -1 0 -3 -2 1 1 -1 0 2 1 -1 -2 -3 0 T 1 0 -2 0 0 -3 0 -1 0 0 -2 -1 0 0 -1 -1 1 3 0 -5 -3 -1 V 0 -2 -2 -2 -2 -1 -1 -2 4 -2 2 2 -2 -1 -2 -2 -1 0 4 -6 -2 -2 W -6 -5 -8 -7 -7 0 -7 -3 -5 -3 -2 -4 -4 -6 -5 2 -2 -5 -6 17 0 -6 Y -3 -3 0 -4 -4 7 -5 0 -1 -4 -1 -2 -2 -5 -4 -4 -3 -3 -2 0 10 -4 Z 0 2 -5 3 3 -5 -1 2 -2 0 -3 -2 1 0 3 0 0 -1 -2 -6 -4 3 Using a wider spread of scores eases the expansion of the scoring matrix to sensibly include ambiguity codes. Scoring Schemes.

4. If the maximum possible cut-off score (still 11) is not achievedOnly if the maximum possible cut-off score (11) is achieved Graphical comparison of sequences using “Dotplots”. To detect perfectly matching words, a dotplot program has a choice of strategies Select a scoring scheme A T G C A 1 0 0 0 T 0 1 0 0 G 0 0 1 0 C 0 0 0 1 ATGCTTATAGG ATGCTTCTGGG 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 =11 1) For every pair of words, compute a word match score in the normal way and a word size (11, say)  ATGCTTATAGG ATGCTTCTGGG 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 =9 Celebrate with a dotDo not celebrate with a dot   Faster plots for perfect matches.

5. Graphical comparison of sequences using “Dotplots”. To detect perfectly matching words, a dotplot program has a choice of strategies 2) OR If they are notIf they are ATGCTTATAGG ATGCTTCTGGG  For every pair of words, ……… see if the letters are exactly the same  ATGCTTATAGG ATGCTTCTGGG Celebrate with a dotDo not celebrate with a dot    To detect exactly matching words, fast character string matching can replace laborious computation of match scores to be compared with a cut-off score Many packages include a dotplot option specifically for detecting exactly matching words. Particular advantage when seeking strong matches in long DNA sequences. Faster plots for perfect matches.

6. Graphical comparison of sequences using “Dotplots”. There are three parameters to consider for a dotplot: 1)The scoring scheme. 2)The cut-off score 3)The word size Dotplot parameters.

7. Graphical comparison of sequences using “Dotplots”. The Scoring scheme. DNA Usually, DNA Scoring schemes award a fixed reward for each matched pair of bases and a fixed penalty for each mismatched pair of bases. Choosing between such scoring schemes will affect only the choice of a sensible cut-off score and the way ambiguity codes are treated. Protein Protein scoring schemes differ in the evolution distance assumed between the proteins being compared. The choice is rarely crucial for dotplot programs. Dotplot parameters.

8. Graphical comparison of sequences using “Dotplots”. The Cut-off score. The lower the cut-off score the more dots will be plotted. But, dots are more likely to indicate a chance match (noise). The higher the cut-off score the less dots will be plotted. But, each dot is more likely to be significant. Dotplot parameters.

9. Scoring Scheme: PAM 250, Word Size: 25, Cut-off score: 3020510 Graphical comparison of sequences using “Dotplots”. The Cut-off score. 4 clear strong regions apparent 4 regions become clearer, some other weaker features appear More “features”, probably noise, appear obscuring the original 4 clear regions. Cut-off now clearly too low. Too much noise to see interesting regions. Dotplot parameters.

10. Graphical comparison of sequences using “Dotplots”. The Word size. Large words can miss small matches.Smaller words pick up smaller features. The smallest “features” are often just “noise”. Dotplot parameters.

11. Graphical comparison of sequences using “Dotplots”. The Word size. For sequences with regions of small matching features. Small words pick small features Individually. Larger words show matching regions more clearly. The lack of detail can be an advantage Dotplot parameters.

12. Displaying the word 11 plot alone shows that major features are drawn in more “carefully”. Arguably, less usefully if a broad overview is the objective. Graphical comparison of sequences using “Dotplots”. Using a relatively large word size of 25, features are drawn with a broad brush. Detail can be missed Superimposing a plot with a smaller word size of 11 shows the emergence of extra dots. In this case probably all noise. The Word size. Dotplot parameters.

13. Graphical comparison of sequences using “Dotplots”. Detection of Repeats Other uses of dotplots.

14. Graphical comparison of sequences using “Dotplots”. Detection of Repeats Other uses of dotplots.

15. Graphical comparison of sequences using “Dotplots”. Detection of Repeats Other uses of dotplots.

16. Graphical comparison of sequences using “Dotplots”. Detection of Repeats Other uses of dotplots.

17. Graphical comparison of sequences using “Dotplots”. Other uses of dotplots. Detection of Stem Loops

18. Graphical comparison of sequences using “Dotplots”. Other uses of dotplots. Detection of Stem Loops

19. The End.