Gene Prediction: Computational Challenge

1. Gene Prediction: Computational Challenge • Gene: A sequence of nucleotides coding for protein • Gene Prediction Problem: Determine the beginning and end positions of genes in a genome

2. Central Dogma and Splicing intron1 intron2 exon2 exon3 exon1 transcription splicing translation exon = coding intron = non-coding Batzoglou

3. Donor site 5’ 3’ Position % Splice site detection From lectures by Serafim Batzoglou (Stanford)

4. Six Frames in a DNA Sequence CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC • stop codons – TAA, TAG, TGA • start codons - ATG CTGCAGACGAAACCTCTTGATGTAGTTGGCCTGACACCGACAATAATGAAGACTACCGTCTTACTAACAC GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG GACGTCTGCTTTGGAGAACTACATCAACCGGACTGTGGCTGTTATTACTTCTGATGGCAGAATGATTGTG

5. Codon Usage in Human Genome

6. Promoter Structure in Prokaryotes (E.Coli) • Transcription starts at offset 0. • Pribnow Box (-10) • Gilbert Box (-30) • Ribosomal Binding Site (+10)

7. TestCode Statistics • Define a window size no less than 200 bp, slide the window the sequence down 3 bases. In each window: • Calculate for each base {A, T, G, C} • max (n3k+1, n3k+2, n3k) / min ( n3k+1, n3k+2, n3k) • Use these values to obtain a probability from a lookup table (which was a previously defined and determined experimentally with known coding and noncoding sequences

8. Distribution of Each Base

9. Position Parameter

10. TestCode Sample Output Coding No opinion Non-coding

11. Popular Gene Prediction Algorithms • GENSCAN: uses Hidden Markov Models (HMMs) • TWINSCAN • Uses both HMM and similarity (e.g., between human and mouse genomes)

12. Statistics

13. Weight

14. TestCode Method • Compute A,C,G,T position and content parameters • Look up from probability of coding value and get p1, p2, …p8 • Get corresponding weights w1, w2, …w8 • Compute p1 w1+ p2 w2 +…+p8 w8 • This is the indicator of coding function

15. Comparing Genes in Two Genomes • Small islands of similarity corresponding to similarities between exons

16. Frog Genes (known) Human Genome Finding Local Alignments Use local alignments to find all islands of similarity

17. Exon Chaining Problem: Graph Representation • This problem can be solved with dynamic programming in O(n) time.

18. Frog Genes (known) Human Genome Infeasible Chains Red local similarities form two non -overlapping intervals but do not form a valid global alignment

19. Genome Rearrangements

21. Simple Rearrangements

22. Phylogenetic Reconstruction

23. Rearrangement Phylogeny

25. Turnip vs Cabbage: Look and Taste Different • Although cabbages and turnips share a recent common ancestor, they look and taste different

26. Turnip vs Cabbage: Comparing Gene Sequences Yields No Evolutionary Information

27. Turnip vs Cabbage: Almost Identical mtDNA gene sequences • In 1980s Jeffrey Palmer studied evolution of plant organelles by comparing mitochondrial genomes of the cabbage and turnip • 99% similarity between genes • These surprisingly identical gene sequences differed in gene order • This study helped pave the way to analyzing genome rearrangements in molecular evolution

28. Turnip vs Cabbage: Different mtDNA Gene Order • Gene order comparison:

29. Turnip vs Cabbage: Different mtDNA Gene Order • Gene order comparison:

30. Turnip vs Cabbage: Different mtDNA Gene Order • Gene order comparison:

31. Turnip vs Cabbage: Different mtDNA Gene Order • Gene order comparison:

32. Turnip vs Cabbage: Different mtDNA Gene Order • Gene order comparison: Before After Evolution is manifested as the divergence in gene order

33. Transforming Cabbage into Turnip

34. Genome rearrangements Mouse (X chrom.) Unknown ancestor~ 75 million years ago • What are the similarity blocks and how to find them? • What is the architecture of the ancestral genome? • What is the evolutionary scenario for transforming one genome into the other? Human (X chrom.)

35. History of Chromosome X Rat Consortium, Nature, 2004

36. 1 2 3 9 10 8 4 7 5 6 Reversals • Blocks represent conserved genes. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

37. Reversals 1 2 3 9 10 8 4 7 5 6 1, 2, 3, -8, -7, -6, -5, -4, 9, 10 • Blocks represent conserved genes. • In the course of evolution or in a clinical context, blocks 1,…,10 could be misread as 1, 2, 3, -8, -7, -6, -5, -4, 9, 10.

38. Reversals and Breakpoints 1 2 3 9 10 8 4 7 5 6 1, 2, 3, -8, -7, -6, -5, -4, 9, 10 The reversion introduced two breakpoints(disruptions in order).

39. Reversals: Example 5’ ATGCCTGTACTA 3’ 3’ TACGGACATGAT 5’ Break and Invert 5’ ATGTACAGGCTA 3’ 3’ TACATGTCCGAT 5’

40. Types of Rearrangements Reversal 1 2 3 4 5 6 1 2 -5 -4 -3 6 Translocation 1 2 3 45 6 1 2 6 4 5 3 Fusion 1 2 3 4 5 6 1 2 3 4 5 6 Fission

41. Comparative Genomic Architectures: Mouse vs Human Genome • Humans and mice have similar genomes, but their genes are ordered differently • ~245 rearrangements • Reversals • Fusions • Fissions • Translocation

42. Waardenburg’s Syndrome: Mouse Provides Insight into Human Genetic Disorder • Waardenburg’s syndrome is characterized by pigmentary dysphasia • Gene implicated in the disease was linked to human chromosome 2 but it was not clear where exactly it is located on chromosome 2

43. Waardenburg’s syndrome and splotch mice • A breed of mice (with splotch gene) had similar symptoms caused by the same type of gene as in humans • Scientists succeeded in identifying location of gene responsible for disorder in mice • Finding the gene in mice gives clues to where the same gene is located in humans

44. Comparative Genomic Architecture of Human and Mouse Genomes To locate where corresponding gene is in humans, we have to analyze the relative architecture of human and mouse genomes

45. Reversals: Example p = 1 2 3 4 5 6 7 8 r(3,5) 1 2 5 4 3 6 7 8

46. Reversals: Example p = 1 2 3 4 5 6 7 8 r(3,5) 1 2 5 4 3 6 7 8 r(5,6) 1 2 5 4 6 3 7 8

47. Reversals and Gene Orders • Gene order is represented by a permutation p: p = p1------ pi-1 pi pi+1 ------pj-1 pj pj+1 -----pn p1------ pi-1pj pj-1 ------pi+1 pipj+1 -----pn • Reversal r ( i, j ) reverses (flips) the elements from i to j inp r(i,j)

48. Reversal Distance Problem • Goal: Given two permutations, find the shortest series of reversals that transforms one into another • Input: Permutations pand s • Output: A series of reversals r1,…rttransforming p into s, such that t is minimum • t - reversal distance between p and s • d(p, s) - smallest possible value of t, given p and s

49. Sorting By Reversals Problem • Goal: Given a permutation, find a shortest series of reversals that transforms it into the identity permutation (1 2 … n ) • Input: Permutation p • Output: A series of reversals r1,… rt transforming p into the identity permutation such that t is minimum

50. Sorting By Reversals: Example • t =d(p ) - reversal distance of p • Example : p = 3 4 2 1 5 6 7 10 9 8 4 3 2 1 5 6 7 10 9 8 4 3 2 1 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 So d(p ) = 3