1. Promoter Prediction in E.coli using ANN
A.Krishnamachari
Bioinformatics Centre, JNU

2. Definition of Bioinformatics
Systematic development and application of Computing and Computational solution techniques to biological data to investigate biological process and make novel observations

3. Research in Biology General approach Bioinformatics era Organism
Functions
Cell
Chromosome
DNA
Sequences

4. Genome Sequence intergenic TSS RBS CDS TF -35 -10
TF -> Transcription Factor Sites
TSS->Transcription Start Sites
RBS -> Ribosome Binding sites
CDS - > Coding Sequence (or) Gene

7. Statement of the problem
Given a set of known sequences pertaining to a specific biological feature , develop a computational method to search for new members or sequences

8. Computational Methods
Pattern Recognition
Pattern classification
Optimisation Methods

9. Sequence Analysis AND Prediction Methods
Consensus
Position Weight Matrix (or) Profiles
Machine Learning Methods
Neural Networks
Markov Models
Support Vector Machines
Decision Tree
Optimization Methods
Statistical Learning

10. Promoter- TATA BOX TAA T TA Consensus sequence 49%,54% and 58%
14 sites out of 291 sequences [Lisser and Margalitt]
Mismatches but which one?

11. Relative Entropy Plot - Promoters

12. Relative Entropy Plot - Random Sequences

13. Alignment

14. Describing features using frequency matrices
Goal: Describe a sequence feature (or motif) more quantitatively than possible using consensus sequences
Need to describe how often particular bases are found in particular positions in a sequence feature

15. Describing features using frequency matrices
Definition: For a feature of length m using an alphabet of n characters, a frequency matrix is an n by m matrix in which each element contains the frequency at which a given member of the alphabet is observed at a given position in an aligned set of sequences containing the feature

16. Frequency matrices (continued)
Three uses of frequency matrices
Describe a sequence feature
Calculate probability of occurrence of feature in a random sequence
Calculate degree of match between a new sequence and a feature

17. Frequency Matrices, PSSMs, and Profiles
A frequency matrix can be converted to a Position-Specific Scoring Matrix (PSSM) by converting frequencies to scores
PSSMs also called Position Weight Matrixes (PWMs) or Profiles

18. Methods for converting frequency matrices to PSSMs
Using log ratio of observed to expected
where m(j,i) is the frequency of character j observed at position i and f(j) is the overall frequency of character j (usually in some large set of sequences)
Using amino acid substitution matrix (Dayhoff similarity matrix) [see later]

19. Finding occurrences of a sequence feature using a Profile
As with finding occurrences of a consensus sequence, we consider all positions in the target sequence as candidate matches
For each position, we calculate a score by “looking up” the value corresponding to the base at that position

20. Alignment

21. Positions (Columns in alignment)
Nucleotides 1 2 3 4 5 A
x11
x21
x31
x41
x51 T
x12
x22
x32
x42
x52 G
x13
x23
x33
x43
x53 C
x14
x24
x34
x44
x54
x12 + x21 + x33 + x44 + x52 V1
TAGCT AGTGC
if is above a threshold it is a site V1

22. Building a PSSM PSSM builder Set of Aligned Sequence Features PSSM
Expected frequencies of each sequence element

23. Searching for sequences related to a family with a PSSM
Set of Aligned Sequence Features
PSSM builder
Expected frequencies of each sequence element
PSSM
Sequences that match above threshold
PSSM search
Threshold
Positions and scores of matches
Set of Sequences to search

24. Consensus sequences vs. frequency matrices
consensus sequence or a frequency matrix which one to use?
If all allowed characters at a given position are equally "good", use IUB codes to create consensus sequence
Example: Restriction enzyme recognition sites
If some allowed characters are "better" than others, use frequency matrix
Example: Promoter sequences

25. Consensus sequences vs. frequency matrices
Advantages of consensus sequences: smaller description, quicker comparison
Disadvantage: lose quantitative information on preferences at certain locations

26. Linear Classification Problems
Measure2
Measure1

27. Nonlinear Classification Problem
Measure 2
Measure 1

28. FEATURE EXTRACTION

29. FEATURE EXTRACTION

30. (Artificial) Neural Network

31. What Is A Neural Network ?
A computational construct based on biological neuron
. A neural network can:
Learn by adapting its synaptic weights to changes in the surrounding environments;
handle imprecise, fuzzy, noisy, and probabilistic information; and
generalize from known tasks or examples to unknown ones.
Artificial neural networks (ANNs) attempt to mimic some,or all of these characteristics.

32. Neural Network Characterised by:
- its pattern of connections between the neurons (Network Architecture)
- its method of determining the weights on the connections (training or learning algorithm)

33. Why Neural Network:Applications
-Little or no incomplete understanding of the problem to be solved (very little theory)
-Abundant data available

34. Neural Networks: Applications
Pattern classification
Speech synthesis and recognition
Image compression
Clustering
Medical Diagnosis
Manufacturing

35. Neural Network:Bioinformatics
Binding sites prediction
Protein Secondary Structure prediction
Protein folds
Micro array data clustering
Gene prediction

36. Neural Networks
Supervised Learning
Unsupervised Learning

37. Perceptron
Output
inputs
Layer 2 (output)
Layer 1 (input) 1
W1,3 3 2
W2,3
Direction of information flow

38. Perceptron
Summation Operation
xi * wij=x1*w1+x2*w2+x3w3….+xnWnj
Thresholding functions
Output = 0 if x*w < T
Output = 1 if x*w >T
Output 1
Output 1 1
Threshold=0 T

39. Logistic Transfer function 1
Perceptron
Output
Input
Logistic Transfer function 1
Output = -
1 + e
Weight updates
W(k+1) = w(k)+ µ[T9k) – w(k)x(k)]x(k) for 0 ≤ k≤ N-1

40. Learning Concepts Generally Stopping criterion
the target output is 1 for +ve
The target output is 0 for –ve
But practically (0.9, 0.1) combination
Stopping criterion
Based on certain epochs or cycles
Based on certain error estimates

41. Perceptron
Nucleic Acid
A T G C 1 2
Position
In a sequence
Of K nucleotides
K-1 K

42. Bit-Coding let the following binary values represent each base A="0001
then
G = 4
A or C = "0011 = 3
A,G or T = "1101 = 13
etc.

43. NETWORK Nucleic Acid A T G C 1 A 1 Wi,j 2 G 1 Position In a sequence1
Wi,j
2 G 1
Position
In a sequence
Of K nucleotides
K-1 G 1
K T 1

44. Test set Learning Model Positive Model Negative Model
TP + TN =100
Note: Training and Test sequences are fixed length

45. Learning Model
Training Set
Test Set
1 2 …………50
1 2 …………50
n=10
N=500

46. Learning Model Prediction Method Output TEST
TRAINING
C G T A G C T A T A G T G G G
T T T A A A C C C A A G A A T
T A T G G A A T T T G G A A G
T T T A G G A T A G C A C A G
G A T A A G G C C T A G A T A
T T T A T G C A T G A G A T G
Prediction
Method
Output
TEST
C C T G A A C T G A G A T G A T A T A T A A G T G A A A T T C C G
Input

47. Multilayer Perceptron
Input Layer -1
Hidden layer
Output Layer

51. Error
function A B
(local)
(global)
weight

52. Prediction NN Learning NN Recognition Example <x,f(x)> x h(x)
Disease A diagnosis:
x – gene expression data (vector of numbers)
f(x) – A positive / A negative (boolean 0/1)
<x,f(x)> - set of known values

53. Evaluation Mechanism Sensitivity = TP TP + FN Specificity = TN TN + FP
(TPxTN – FPxFN)
C =
(TP+FP)x(FP+TN)x(TN+FN)x(FN+TP)

54. Cross - Validation Benchmarking the Network Performance Step:1
Divide the training set into “N” partitions
Step: 2
Train the “N-1” partitions and
Test the Left out
Step: 3
Evaluate the Performance

55. References 1. Nucleic Acids Res. 1992 Aug 25;20(16):4331-8.
An assessment of neural network and statistical approaches for prediction of E. coli promoter sites. Horton PB, Kanehisa M.
2. Nucleic Acids Res Jun 11;22(11):
Analysis of E.coli promoter structures using neural networks. Mahadevan I, Ghosh I.

56. O’Neill MC
Training back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Res Jan 25;19(2):313-8.
Escherichia coli promoters: neural networks develop distinct descriptions in learning
to search for promoters of different spacing classes. Nucleic Acids Res Jul 11;20(13):
A general procedure for locating and analyzing protein-binding sequence motifs
in nucleic acids. Proc Natl Acad Sci U S A Sep 1;95(18):
Pedersen AG, Engelbrecht Investigations of Escherichia coli
promoter sequences with artificial neural networks: new signals discovered
upstream of the transcriptional startpoint. Proc Int Conf Intell Syst Mol Biol. 1995;3:292-9.

57. Siu-wai Leung, Chris Mellish and Dave Robertson
Basic Gene Grammars and DNA-ChartParser for language processing of Escherichia coli promoter DNA sequences
Siu-wai Leung, Chris Mellish and Dave Robertson
E.Coli PROMOTER DATA
R Hershberg, G Bejerano, A Santos-Zavaleta and H Margalit
PromEC: An updated database of Escherichia coli mRNA promoters with experimentally identified transcriptionalstart sites.
Nucleic Acids Research 2001, 29: 277

58. Earlier Studies Data set corresponding to 17 spacing class
Very high threshold values

59. Results - O'Neill (BPNN)
Network
Model
Performance
pBR322(CW)
pBR322(CCW)
17-1
34/35
-5, 339,477,1584,1657,1970,4130
125,805,1021,1226,4278
17-2
33/35 ….
17-3
17-4
32/35
Poll(4/4)
-5, 1584, 1970,4130
125,807,1226,4278

60. New
New
New
New

61. NEW

62. Disadvantages of MLP i) Slow convergence,
ii) Training relies heavily on the choice of the number of hidden layers and
iii) Mode of prediction is generally based on high threshold values.

63. Improvements in Prediction
Pre processing of the data based on DNA structure
Clean Model

64. Structural Atlas of E.coli
: J Mol Biol Jun 16;299(4):
                 A DNA structural atlas for Escherichia coli. Pedersen AG, Jensen LJ, Brunak S, Staerfeldt HH, Ussery DW.

65. Points to remember
1) The size of the positive data set may be increased by incorporating point mutations in non-sensitive positions
Negative data sets are generated by several ways i.e.
a) Shuffling or randomising the positive data set. This does not destroy
the the correlations between the bases completely.
b) Using random sequences with a biased composition,
c) Extracting the sequences from gene coding segments.
3) For the learning phase, the number of positive and negative input vectors are not generally proportionate and there is no standard prescription
4) Convergence factor and predictive ability depend on the size and the number of input vectors .

66. GRAIL

67. THANKS