CS 598SS Lecture 2

1. CS 598SSLecture 2 Saurabh Sinha

2. Transcription • Process of making a single stranded mRNA using double stranded DNA as template • Only genes are transcribed, not all DNA

3. Transcriptional Regulationin action

4. Segmentation of fruitfly embryo Adult fruitfly Cavity with Single cell Early Embryo Source: From DNA to Diversity, Carroll et al.

5. How does the asymmetry arise?

6. Some genes are asymmetrically deposited by mother Target genes are expressed in “gapped” domains Further refinement of striped pattern.

7. Asymmetry of gap genes Transcription Factor R (ACTIVATOR) Gene G

8. Gene off here Asymmetry of gap genes Transcription Factor R1 (REPRESSOR) Transcription Factor R (ACTIVATOR) Gene G

9. Gene on here Gene off here Gene off here Asymmetry of gap genes Transcription Factor R1 (REPRESSOR) Transcription Factor R (ACTIVATOR) Transcription Factor R2 (REPRESSOR) Gene G

10. Gene on here Module Asymmetry of gap genes Gene G

11. This is how an asymmetric expression pattern arises

12. Module Kr Gt Gt Kr Gt Kr Repressors Activators bcd bcd bcd Hb bcd bcd Another example: eve stripe 2 module Gene SOURCE: http://www.nyu.edu/fas/dept/biology/faculty/small/smallfig7_big.html

13. “Module” • A “module” has a cluster of binding sites that mediate the action of several transcription factors, to control a target gene’s expression • Modules are typically 200-1000 bp long • One or many occurrences of binding sites for transcription factors • Typically, 3-6 transcription factors are involved in regulating a module

14. From Steve Small, NYU Why “module”? Expression pattern of even-skipped (eve) gene

15. Why “module”? Expression pattern of even-skipped (eve) gene Eve stripe 2

16. Eve Stripe 2 From Steve Small, NYU Why “module”? Expression pattern of even-skipped (eve) gene Eve stripe 2 Eve gene on Chromosome 2R

17. Eve Stripe 2 From Steve Small, NYU Why “module”? Expression pattern of even-skipped (eve) gene Eve stripe 2 Regulatory sequence associated with eve Stripe 2

18. Eve Stripe 2 From Steve Small, NYU Why “module”? Regulatory sequence associated with eve Stripe 2

19. Eve Stripe 2 From Steve Small, NYU Why “module”? Reporter gene Regulatory sequence associated with eve Stripe 2

20. Eve Stripe 2 From Steve Small, NYU Why “module”? Reporter gene Reporter gene shows same pattern ! Regulatory sequence associated with eve Stripe 2

21. Binding sites and motifs

22. Binding sites • Binding sites of transcription factor “Bicoid”, collected experimentally

23. http://webdisk.berkeley.edu/~dap5/data_04/motifs/bicoid.gif

24. T A A T C C C Motif http://webdisk.berkeley.edu/~dap5/data_04/motifs/bicoid.gif

25. W A A T C C N Motif W = T or A N = A,C,G,T “Consensus String” http://webdisk.berkeley.edu/~dap5/data_04/motifs/bicoid.gif

26. Motif • Common sequence “pattern” in the binding sites of a transcription factor • A succinct way of capturing variability among the binding sites

27. Alternative way to represent motif Position weight matrix (PWM) Or simply, “weight matrix”

28. Motif representation • Consensus string • May allow “degenerate” symbols in string, e.g., N = A/C/G/T; W = A/T; S = C/G; R = A/G; Y = T/C etc. • Position weight matrix • More powerful representation • Probabilistic treatment

29. The motif finding problem • Suppose a transcription factor (TF) controls five different genes • Each of the five genes should have binding sites for TF in their promoter region Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Binding sites for TF

30. The motif finding problem • Now suppose we are given the promoter regions of the five genes G1, G2, … G5 • Can we find the binding sites of TF, without knowing about them a priori ? • Binding sites are similar to each other, but not necessarily identical • This is the motif finding problem • To find a motif that represents binding sites of an unknown TF

31. A variant of motif finding • Given a motif (e.g., consensus string, or weight matrix), find the binding sites • For consensus string, problem is trivial • For weight matrix, not so trivial

32. Given a string s of length l = 7 • s = s1s2…sl • Pr(s | W) = • Example: • Pr(CTAATCCG) = • 0.66 x 0.88 x 0.99 x 0.99 x 0.88 • x 0.99 x 0.88 x 0.11 Binding sites from a weight matrix motif W Probability of each base In each column Counts of each base In each column Wk = probability of base  in column k

33. Binding sites from a weight matrix motif • Given promoter sequence S (e.g., 1000 base-pairs long) • For each substring s of S, • Compute Pr(s|W) • If Pr(s|W) > some threshold, call that a binding site • Look at S, as well as its “reverse complement” • Rev.Compl. of AGTTACACCA is TGGTGTAACT • (That’s what is on the other strand of DNA)

34. Regulatory network Genetic regulatory network controlling the development of the body plan of the sea urchin embryo Davidson et al., Science, 295(5560):1669-1678.

35. Regulatory network

36. Regulatory network • Computational problem is to unravel the entire regulatory network • Sequence data • Other forms of data (e.g., information about which genes are on and which genes are off, under different conditions)

37. Digression:Tandem repeats

38. DNA Replication http://www.ncc.gmu.edu/dna/repanim.htm

39. “Slippage” in replication SOURCE: http://www.virtuallaboratory.net/Biofundamentals/lectureNotes/Topic3-8_repair.htm

40. Tandem repeats • Short string repeated • Almost identical strings next to each other • Result of slippage during replication ACGCGGACGGTGAACGCTGTATACTA Tandem repeat

41. Tandem repeats • Very frequent in some genomes • Mechanism for evolution • Motif finding algorithms should consider the existence of tandem repeats • Why ?