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 – 3 types of core promoter
TATA-box
Initiator (Inr)
Downstream promoter element
3 types of core promoter
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, ,
PREDICTED TRANSCRIPTION START SITES:
gi_59865_emb_X _HEHSV1SU Herpes simplex virus type 1 _HSV1_ short unique region DNA
Sequence length: # of bases: A=2286, C=4271, G=4078, T=2344
Predicted TSS
Forward strand
# of guesses = 5
Reverse complement strand
# of guesses = 2

19. Measurement Dragon 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 Main difference in new 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 Basic Model Sub-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 ANNs
Specific functional regions of a gene: promoter, coding-exon, intron
Represented as positional distributions of overlapping pentamers
ANNs

29. Sensors Pentamers : Positional weight matrices (PWM):
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
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
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 Pentamer promoter intron Exon A: ACGGT 0.13 0.20
0.67
B: CGATA
0.10
0.44
0.46
C: AUGCC
0.87
0.07
0.06
D: TAGTG
0.24
0.49
0.27

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} Example To From 1 2
1: promoter
2: intron
3: exon
Example To
From 1 2 3
4.1
4.4 8
2.6
7.1
3.2

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

51. Time Complexity
There are 9n2/2=O(n2) entries in the dynamic programming table
Filling each entry needs average n/2=O(n) time
The total time complexity is O(n3)

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

53. Conclusion
Significant achievement in promoter recognition technique & algorithms contributes to major advances in gene finding.
There is still room for improvement in promoter recognition.
A new algorithm is proposed for gene recognition.