1. CSE182-L9 Gene Finding (DNA signals) Genome Sequencing and assembly

2. Basic Math Given a vector V in an n- dimensional space –What is ||V||? –What is V/||V||? How can you compute the angle between two vectors? V V1V1

4. Transcription and translation We define a gene as a location on the genome that codes for proteins. The genic information is used to manufacture proteins through transcription, and translation. There is a unique mapping from triplets to amino-acids

5. Translation The ribosomal machinery reads mRNA. Each triplet is translated into a unique amino-acid until the STOP codon is encountered. There is also a special signal where translation starts, usually at the ATG (M) codon.

6. Translation The ribosomal machinery reads mRNA. Each triplet is translated into a unique amino-acid until the STOP codon is encountered. There is also a special signal where translation starts, usually at the ATG (M) codon. Given a DNA sequence, how many ways can you translate it?

7. Eukaryotic gene structure The coding regions of a gene are discontiguous regions (exons), separated by non-coding regions (introns). Transcription initially copies the entire region into RNA The introns are ‘spliced out’ to form the mature mRNA (message) Translation starts from an intitiating ATG somewhere in the message.

8. Gene Features ATG 5’ UTR intron exon 3’ UTR Acceptor Donor splice site Transcription start Translation start

9. Gene Features The gene can lie on any strand (relative to the reference genome) The code can be in one of 3 frames. AGTAGAGTATAGTGGACG S R V * W R V Q Y S G * S I V D Frame 1 Frame 2 Frame 3 -ve strand TCATCTCATATCACCTGC

10. Gene identification Eukaryotic gene definitions: –Location that codes for a protein –The transcript sequence(s) that encodes the protein –The protein sequence(s) Suppose you want to know all of the genes in an organism. This was a major problem in the 70s. PhDs, and careers were spent isolating a single gene sequence. All of that changed with better reagents and the development of high throughput methods like EST sequencing With genome sequencing, the initial problem became computational.

11. Computational Gene Finding Given Genomic DNA, identify all the coordinates of the gene TRIVIA QUIZ! What is the name of the FIRST gene finding program? (google testcode) ATG 5’ UTR intron exon 3’ UTR Acceptor Donor splice site Transcription start Translation start

12. Gene Finding: The 1st generation Given genomic DNA, does it contain a gene (or not)? Key idea: The distributions of nucleotides is different in coding (translated exons) and non- coding regions. Therefore, a statistical test can be used to discriminate between coding and non-coding regions.

13. Coding versus non-coding You are given a collection of exons, and a collection of intergenic sequence. Count the number of occurrences of ATGATG in Introns and Exons. –Suppose 1% of the hexamers in Exons are ATGATG –Only 0.01% of the hexamers in Intergenic are ATGATG How can you use this idea to find genes?

14. Generalizing AAAAAA AAAAAC AAAAAG AAAAAT IE Compute a frequency count for all hexamers. Exons, Intergenic and the sequence X are all vectors in a multi-dimensional space Use this to decide whether a sequence X is exonic/intergenic. 10 520 10 X 5 Frequencies (X10 -5 )

15. A geometric approach (2 hexamers) Plot the following vectors – E= [10, 20] – I = [10, 5] – V 3 = [6, 10] – V 4 = [9, 15] Is V 3 more like E or more like I? 5 20 15 10 15105 E I V3V3

16. Choosing between Intergenic and Exonic Normalize V’ = V/||V|| All vectors have the same length (lie on the unit circle) Next, compute the angle to E, and I. Choose the feature that is ‘closer’ (smaller angle. E I V3V3

17. Coding versus non-coding signals Fickett and Tung (1992) compared various measures Measures that preserve the triplet frame are the most successful. Genscan uses a 5th order Markov Model

18. 5th order markov chain Pr EXON [AAAAAACGAGAC..] =T[AAAAA,A] T[AAAAA,C] T[AAAAC,G] T[AAACG,A]…… = (20/T) (50/T)………. AAAAAA 20 1 AAAAAC 50 10 AAAAAG 5 30 AAAAAT 3.. T AAAAA A G C AAAAG AAAAC ExonIntron

19. Scoring for coding regions The coding differential can be computed as the log odds of the probability that a sequence is an exon vs. and intron. In Genscan, separate transition matrices are trained for each frame, as different frames have different hexamer distributions

20. Coding differential for 380 genes

21. Coding region can be detected Coding Plot the coding score using a sliding window of fixed length. The (large) exons will show up reliably. Not enough to predict gene boundaries reliably

22. Other Signals GT ATG AG Coding Signals at exon boundaries are precise but not specific. Coding signals are specific but not precise. When combined they can be effective

23. Combining Signals We can compute the following: –E-score[i,j] –I-score[i,j] –D-score[i] –I-score[i] –Goal is to find coordinates that maximize the total score ij

24. The second generation of Gene finding Ex: Grail II. Used statistical techniques to combine various signals into a coherent gene structure. It was not easy to train on many parameters. Guigo & Bursett test revealed that accuracy was still very low. Problem with multiple genes in a genomic region

25. Combining signals using D.P. An HMM is the best way to model and optimize the combination of signals Here, we will use a simpler approach which is essentially the same as the Viterbi algorithm for HMMs, but without the formalism.

26. Hidden states & gene structure Identifying a gene is equivalent to labeling each nucleotide as E/I/intergenic etc. These ‘labels’ are the hidden states For simplicity, consider only two states E and I IIIIIEEEEEEIIIIIIEEEEEEIIIIEEEEEE IIIII i1i1 i2i2 i3i3 i4i4

27. Gene finding reformulated Given a labeling L, we can score it as I-score[0..i 1 -1] + E-score[i 1..i 2 ] + D-score[i 2 +1] + I-score[i 2 +1..i 3 -1] + A-score[i 3 -1] + E-score[i 3..i 4 ] + ……. Goal is to compute a labeling with maximum score. IIIIIEEEEEEIIIIIIEEEEEEIIIIEEEEEE IIIII i1i1 i2i2 i3i3 i4i4

28. Optimum labeling using D.P. (Viterbi) Define V E (i) = Best score of a labeling of the prefix 1..i such that the i-th position is labeled E Define V I (i) = Best score of a labeling of the prefix 1..i such that the i-th position is labeled I Why is it enough to compute V E (i) & V I (i) ?

29. Optimum parse of the gene j i ji

30. Generalizing Note that we deal with two states, and consider all paths that move between the two states. E I i

31. Generalizing We did not deal with the boundary cases in the recurrence. Instead of labeling with two states, we can label with multiple states, –E init, E fin, E mid, –I, I G (intergenic) E init I E fin E mid IGIG Note: all links are not shown here

32. An HMM for Gene structure

33. Gene Finding via HMMs Gene finding can be interpreted as a d.p. approach that threads genomic sequence through the states of a ‘gene’ HMM. –E init, E fin, E mid, –I, I G (intergenic) E init I E fin E mid IGIG Note: all links are not shown here i

34. End of L9

35. Generalized HMMs, and other refinements A probabilistic model for each of the states (ex: Exon, Splice site) needs to be described In standard HMMs, there is an exponential distribution on the duration of time spent in a state. This is violated by many states of the gene structure HMM. Solution is to model these using generalized HMMs.

36. Length distributions of Introns & Exons

37. Generalized HMM for gene finding Each state also emits a ‘duration’ for which it will cycle in the same state. The time is generated according to a random process that depends on the state.

38. Forward algorithm for gene finding ji qkqk Emission Prob.: Probability that you emitted X i..X j in state q k (given by the 5th order markov model) Forward Prob: Probability that you emitted i symbols and ended up in state q k Duration Prob.: Probability that you stayed in state q k for j-i+1 steps

39. De novo Gene prediction: Summary Various signals distinguish coding regions from non-coding HMMs are a reasonable model for Gene structures, and provide a uniform method for combining various signals. Further improvement may come from improved signal detection

40. DNA Signals Coding versus non-coding Splice Signals Translation start ATG 5’ UTR intron exon 3’ UTR Acceptor Donor splice site Transcription start Translation start

41. DNA signal example: The donor site marks the junction where an exon ends, and an intron begins. For gene finding, we are interested in computing a probability –D[i] = Prob[Donor site at position i] Approach: Collect a large number of donor sites, align, and look for a signal.

42. PWMs Fixed length for the splice signal. Each position is generated independently according to a distribution Figure shows data from > 1200 donor sites 321123456 AAGGTGAGT CCGGTAAGT GAGGTGAGG TAGGTAAGG

43. Improvements to signal detection GGTAPr[ GGTA ] is a donor site? –0.5*0.5 CGTAPr[ CGTA ] is a donor site? –0.5*0.5 Is something wrong with this explanation? GGTA CGTG

44. MDD PWMs do not capture correlations between positions Many position pairs in the Donor signal are correlated

45. Maximal Dependence Decomposition Choose the position i which has the highest correlation score. Split sequences into two: those which have the consensus at position i, and the remaining. Recurse until –Stop if #sequences is ‘small enough’

46. MDD for Donor sites

47. Gene prediction: Summary Various signals distinguish coding regions from non-coding HMMs are a reasonable model for Gene structures, and provide a uniform method for combining various signals. Further improvement may come from improved signal detection

48. How many genes do we have? Nature Science

49. Alternative splicing

50. Comparative methods Gene prediction is harder with alternative splicing. One approach might be to use comparative methods to detect genes Given a similar mRNA/protein (from another species, perhaps?), can you find the best parse of a genomic sequence that matches that target sequence Yes, with a variant on alignment algorithms that penalize separately for introns, versus other gaps. There is a genome sequencing project for a different Hirudo species. You could compare the Hirudo ESTs against the genome to do gene finding.

51. Comparative gene finding tools Procrustes/Sim4: mRNA vs. genomic Genewise: proteins versus genomic CEM: genomic versus genomic Twinscan: Combines comparative and de novo approach. Mass Spec related? –Later in the class we will consider mass spectrometry data. –Can we use this data to identify genes in eukaryotic genomes? (Research project)

52. Databases RefSeq and other databases maintain sequences of full- length transcripts/genes. We can query using sequence.

53. Course Sequence Comparison (BLAST & other tools) Protein Motifs: –Profiles/Regular Expression/HMMs Discovering protein coding genes –Gene finding HMMs –DNA signals (splice signals) How is the genomic sequence itself obtained? Protein sequence analysis ESTs Gene finding

54. Silly Quiz Who are these people, and what is the occasion?

55. Genome Sequencing and Assembly

56. DNA Sequencing DNA is double- stranded The strands are separated, and a polymerase is used to copy the second strand. Special bases terminate this process early.

57. Sequencing A break at T is shown here. Measuring the lengths using electrophoresis allows us to get the position of each T The same can be done with every nucleotide. Fluorescent labeling can help separate different nucleotides

58. Automated detectors ‘read’ the terminating bases. The signal decays after 1000 bases.

59. Sequencing Genomes: Clone by Clone Clones are constructed to span the entire length of the genome. These clones are ordered and oriented correctly (Mapping) Each clone is sequenced individually

60. Shotgun Sequencing Shotgun sequencing of clones was considered viable However, researchers in 1999 proposed shotgunning the entire genome.

61. Library Create vectors of the sequence and introduce them into bacteria. As bacteria multiply you will have many copies of the same clone.

62. Sequencing

63. Questions Algorithmic: How do you put the genome back together from the pieces? Will be discussed in the next lecture. Statistical? EX: Let G be the length of the genome, and L be the length of a fragment. How many fragments do you need to sequence? –The answer to the statistical questions had already been given in the context of mapping, by Lander and Waterman.

64. Lander Waterman Statistics G L Island

65. LW statistics: questions As the coverage c increases, more and more areas of the genome are likely to be covered. Ideally, you want to see 1 island. Q1: What is the expected number of islands? Ans: N exp(-c  ) The number increases at first, and gradually decreases.

66. Analysis: Expected Number Islands Computing Expected # islands. Let X i =1 if an island ends at position i, X i =0 otherwise. Number of islands = ∑ i X i Expected # islands = E(∑ i X i ) = ∑ i E(X i )

67. Prob. of an island ending at i E(X i ) = Prob (Island ends at pos. i) = Prob(clone began at position i-L+1 AND no clone began in the next L-T positions) i L T

68. LW statistics Pr[Island contains exactly j clones]? Consider an island that has already begun. With probability e - c , it will never be continued. Therefore Pr[Island contains exactly j clones]= Expected # j-clone islands

69. Expected # of clones in an island Why?

70. Expected length of an island