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CS5238 Combinatorial methods in bioinformatics

CS5238 Combinatorial methods in bioinformatics. Topic: Gene Finding – Promoter Recognition Cen Cen, Er Inn Inn, Miao Xiaoping, Piyush Kanti Bhunre, Yin Jun. 1 November 2002. Outline of Presentation. Biological Background Gene Finding Promoter Recognition Dragon Promoter Finder

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CS5238 Combinatorial methods in bioinformatics

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  1. CS5238 Combinatorial methods in bioinformatics Topic: Gene Finding – Promoter Recognition Cen Cen, Er Inn Inn, Miao Xiaoping, Piyush Kanti Bhunre, Yin Jun 1 November 2002

  2. Outline of Presentation • Biological Background • Gene Finding • Promoter Recognition • Dragon Promoter Finder • Open Problem and Future Research • New Algorithm • Conclusion

  3. Biological Background • What is gene? • A sequence of DNA that encodes a protein or an RNA molecule. • Gene has 4 regions: Coding region, 5’ UTR, 3’ UTR and regulatory region (promoter – regulate the transcription process) • Human genome – 3G bp, but only 3% is coding region.

  4. Central Dogma • Central Dogma- process where DNA sequence generates a protein • Transcription & Translation • Promoter – responsible for initiation and regulation of transcription • RNA-polymerase binds to a TATA base sequence in promoter region

  5. Central Dogma

  6. Promoter Region • Core Promoter – • TATA-box • Initiator (Inr) • Downstream promoter element • 3 types of core promoter • TATA-box • TATA-less, Inr-containing • Inr + DPE • Upstream promoter elements • TSS -where transcription starts on DNA The biology of eukaryotic promoter prediction – a review by Pedersen, A.G. et. al.

  7. Outline of Presentation • Biological Background • Gene Finding • Promoter Recognition • Dragon Promoter Finder • Open Problem and Future Research • New Algorithm • Conclusion

  8. What is Gene Finding? • Generate predictions of gene locations from primary genomic sequence (DNA sequence) by computational methods. • Task of gene finding – separate the coding regions, non-coding regions and intergenic regions. • Input: A seq of DNA, X = x1x2x…xn, where xi belongs to {A, C, G, T} • Output: Correct labeling of each element in X as a belonging to CR, NCR, Intergenic Region

  9. Gene Finding • 3 major kinds of gene finding strategies: • Content-based – overall properties of the sequence when making predictions • Site-based – make use of presence or absence of a specific sequence, pattern or consensus • Comparative – sequence homology (database searching) • Combinatorial approach - GeneMachine • GRAIL, FGENEH, MZEF, GenScan, GeneID, GeneParser, HMMgene and so on.

  10. Gene Finding – Open Problems • Overlapping genes – no existing method that can deal with this problem • Alternative splicing, alternative transcription/translation problem • Sequencing errors • Difficult to identify promoter region (PR) & polyA (high true pos + high false pos)

  11. Outline of Presentation • Biological Background • Gene Finding • Promoter Recognition • Dragon Promoter Finder • Open Problem and Future Research • New Algorithm • Conclusion

  12. Promoter Recognition • Accurate PR can help to: • Detect a respective gene more easily • Determine the 5’ ends of the respective gene more precisely • Localize the regions that contain numerous different transcription control components • Developing a perfect predictive model of PR is challenging

  13. Main Approach to PR • Pattern-driven strategy • Collect a set of real binding sites to build characteristics definition, representation, pattern or profile from them • Recognition of individual potential binding sites by using their characteristic profiles • Assembling the candidates’ binding sites following some descriptions and rules about how these arrangements should be done.

  14. Problem: • Given a collection of known binding sites, how to develop a representation of those sites, which is useful to search for them in new sequence? • Consensus sequences • Positional Weight Matrices (PWM) • Hidden Markov profiles • Multilayer neural networks and so on

  15. Promoter Recognition Program • Statistical approach + artificial intelligence techniques - • Dragon Promoter Finder (DPF) • PromoterInspector • Promoter 2.0

  16. Accuracy Metric for PR A common measure of prediction accuracy Sensitivity Specificity TP TN SE = ——— SP = ——— TP + FN TN + FP • Evaluation largely influenced by training set and test sets

  17. Prediction of Promoter 2 x 2 contingency table

  18. Example of Prediction - DPF Promoter positions - exact positions of the TSS 2360, 2585, 4125, 5026, 5734, 7090,8567, 10641, -2700, -12561, -12855 PREDICTED TRANSCRIPTION START SITES: gi_59865_emb_X02138.1_HEHSV1SU Herpes simplex virus type 1 _HSV1_ short unique region DNA Sequence length: 12979 # of bases: A=2286, C=4271, G=4078, T=2344 Predicted TSS Forward strand 4125 5733 7093 8567 10641 # of guesses = 5 Reverse complement strand -12561 -2698 # of guesses = 2

  19. MeasurementDragon Promoter Finder, BIC-KRDL Singapore SE = 7/11 = 0.64 SP = 6479/6479 = 1

  20. Outline of Presentation • Biological Background • Gene Finding • Promoter Recognition • Dragon Promoter Finder • Open Problem and Future Research • New Algorithm • Conclusion

  21. Dragon Promoter Finder -Introduction • Dragon Promoter Finder( DPF) • locates RNA polymerase II promoters in DNA sequences of vertebrates • predicts Transcription Start Site (TSS) positions. • strand specific • Components: • nonlinear promoter recognition models • signal procession • artificial neural networks (ANNs ) • sensors.

  22. Introduction (cont) • The latest version • Dragon Promoter Finder Ver. 1.3 • Main difference in new version • models are now specialized for C+G-rich and for C+G-poor sequences.

  23. Structure • Overall Model • comprises a collection of a number of basic models • Basic Model • made up of two sub-models, A and B • trained for different ranges of system sensitivity • trained separately for the best performance. • Sub-Model

  24. Overall Model

  25. Basic Model • A composite collection of basic models • Possess identical structure • Trained for narrow specificity range. • Data procession in each model is analogous.

  26. Basic Model

  27. Sub-model

  28. Sub-model • Three Sensors • Specific functional regions of a gene: promoter, coding-exon, intron • Represented as positional distributions of overlapping pentamers • ANNs

  29. Sensors • Pentamers : • All sequences of 5 consecutive nucleotides. • AAAAA,AAAAC,AAAAG…… 4^5=1024 pentamers • Selected the most significant 256 pentamers from 1024 pentamers according to statistical relevance • Positional weight matrices (PWM): • The positional distribution of selected pentamers • Generate PWMs for each of the 3 functional groups, promoter, exon & intron, by counting the frequencies of all selected pentamers at each position.

  30. How to analyze the content of a data window: • Sequence W=n1n2…nL-1nL, ni belongs to{A, C, G, T} • Sequence P of successive overlapping pentamers pj:P = p1p2… pL–5pL–4. S = score for each data window The higher the s, the more likely the data window represents the respective functional region. These scores are input to nonlinear signal processing block (SPB) Output from SPB is then input to ANN : The jth pentamer at position i : The frequency of the jth pentamer at position i

  31. ANNs • Inputs: scores (outputs of sensors) • A multi-sensor integration. • Trained by the Bayesian regularization method to separate promoter regions from the non-promoter regions. • The threshold that best separated promoters from non promoter was selected • ANN output > threshold promoter region + TSS at a position 50bp before the data window’s end

  32. Evaluation • Successfully recognize both CpG island-related and CpG island-nonrelated promoters. • Its performance on several large sets(A,B,and human chromosome 22) is reasonably consistent • On the average, its expected maximum sensitivities is approximately 66 percent. • In general, the DPF produces many times fewer FP predictions than comparative systems at the same sensitivity level.

  33. Comparison

  34. Outline of Presentation • Biological Background • Gene Finding • Promoter Recognition • Dragon Promoter Finder • Open Problem and Future Research • New Algorithm • Conclusion

  35. Open Problem & Future Research • Open problem: • Lack of biological information on transcription process • Characteristics of promoter -> low ratio of accuracy • Future research work: • Designing specific algorithm for either classes of promoters or species-specific promoters • Comparative sequence analysis • Combinatorial approach • Data mining tools

  36. Outline of Presentation • Biological Background • Gene Finding • Promoter Recognition • Dragon Promoter Finder • Open Problem and Future Research • New Algorithm • Conclusion

  37. Gene Recognition Algorithm Using Dynamic Programming Approach Presented by: Yin Jun

  38. Dynamic Programming Algorithm Existing Dynamic Programming Algorithm for Gene Finding • Snyder and Stormo’s method • GeneParser • Solovyev et al’s method • FGENEH • MORGAN’s DP algorithm

  39. Goal of those Algorithm • Divide DNA sequence into alternate intron and exon regions. • Define a score for each kind of division. Try to find a kind of division which has the maximum score. The higher the score, the better the division.

  40. Advantage and Disadvantage of Snyder and Stormo’s algorithm • Advantage • the donor and the acceptor site • HMM hidden status • Disadvantage • Cannot recognize promoter • 3-mer based

  41. Our Algorithm • Combine the ideas of “Dragon Promoter Finder” and “Snyder and Stormo’s algorithm” • Can deal with promoters • Use pentamer instead of 3-mer, more efficient • Dynamic Programming

  42. Training Phase • Pentamer – 5 consecutive bases • For example: “ACGGT” • There are 45=1024 different kind of pentamers • Divide a DNA sequence into pentamers • From training data, we can obtain the probability for each kind of pentamer to become a promoter, an intron or an exon

  43. Probability Table

  44. Principle of Division (1) • Good (red: promoter; green: intron; blue: exon) • Bad (low sum of probability) C C A B B C B A D D D C C A B B C B A D D D

  45. Principle of Division (2) • Good (red: promoter; green: intron; blue: exon) • Bad (too frequent mutation) C C A B B C B A D D D C C A B B C B A D D D

  46. Mutation Penalty • M(x, x) should be 0, x∈ {1, 2, 3} • 1: promoter • 2: intron • 3: exon • Example

  47. Notation • P(p, r) – Probability for pentamer p belongs region r • Obtain from training data • M(s, t) – Mutation penalty • Parameters to specify • pi (1≤i≤n) – The i th pentamer in the DNA sequence • Input data (testing data) • a(pi) – Region assignment result; a(pi)∈{1, 2, 3} • Output data

  48. Score Function • For division assignment a, its score is • We use dynamic programming algorithm to find the best division assignment, whose score is the highest

  49. Bases • Let F(i, j, s, t) be the optimal score for the consecutive segment of pentamers from i th to j th, where i th pentamer is assigned region s, j th pentamer is assigned region t • Bases

  50. Recursive Definition • Recursive Definition • Finally, we get F(1, n, s, t) where s, t ∈{1, 2, 3} • Pick up the highest score from the 9 scores

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