A new multiple classifiers soft decisions fusion approach for exons prediction in dna sequences

2013 IEEE International Conference on Signal and Image Processing Applications (ICSIPA 2013)

A New Multiple Classifiers Soft Decisions Fusion
Approach
for Exons Prediction in DNA Sequences
Ismail M. El-Badawy, Ashraf M. Aziz, Senior Member, IEEE, Safa Gasser and Mohamed E. Khedr

Department of Electronics & Communications Engineering
Arab Academy for Science, Technology and Maritime Transport, Egypt

Presented by

Ismail M. El-Badawy


Outline
 Introduction
 DNA Structure
 Predicting Exons Locations
 Exons Prediction using DFT
 Proposed Soft Decisions Fusion Approach
 Performance Evaluation
 Conclusion


Introduction
 Digital Signal Processing has proved its success in different fields,

and bioinformatics is one of these fields.

 Identification of protein coding regions in DNA sequences is one of

the important topics in biosignal processing and bioinformatics
area.

 With the significant growth of sequenced genomic data, it has

become important to come up with computarized methods for
predicting these important protein coding regions (exons) in DNA
sequences.


DNA Structure
 DNA, or deoxyribonucleic acid, is the hereditary material in humans

and almost all other organisms.


DNA Structure
 Organisms can be categorized into

prokaryotes (e.g bacteria) and
eukaryotes (e.g human).

 In both categories, DNA consists

of genes separated by intergenic
regions.

 In eukaryotes, genes are further

divided into protein-coding
regions (exons) and noncoding regions (introns).


DNA Structure
 DNA is made up of nucleotides.
 Nucleotides are identified by the

four nitrogen bases.
 Nitrogen bases pair up with each

other forming a double helix.
Adenine (A)
Thymine (T)
Cytosine (C)
Guanine (G)
 The

two DNA strands are
complementary to each other.


DNA Structure
 DNA = Chain of nucleotides {A, C, G and T}.

 This DNA chain (Exons and introns) can symbolically be

represented by a character string of four alphabet letters.

………TCCGATCGATCGATCTCTCTAGCGTCTACGCTAT
CATCGCTCTCTATTATCGCGCGATCGTCGATCGCGCG
AGAGTATGCTACGTCGATCGAATTG …………………………


DNA Structure
 Protein-Coding regions (Exons) are the portions in DNA that

contain the information for producing proteins.


Predicting Exons Locations
 Accurate prediction of the exons locations in DNA sequences is

an important issue for biologists since they are considered as
information bearing parts.

TATTCCGATCGATCGATCT
CTCTAGCGTCTACGCTATC
ATCGCTCTCTATTATCGCG
CG ……

Exons
finder

Exons Locations


 The order of the nucleotides

stored in the Exons spell out a
code for protein synthesis.

 Triplets of nucleotides (codons)

in the exonic segments of DNA
specify each type of amino acid
based on a genetic code.

 Each amino acid is encoded by one

or more codons (many to one
mapping).


 It was shown in previous publications that exonic parts exhibit a

period-3 property due to the codon structure and the nonuniform usage of codons in exonic regions.
 This periodicity is absent outside the exonic segments.
……… ACGTATTCCGATCGA …………… GACTCTAGCGTCTAC ………


 Three main steps to predict exons locations using digital signal

processing (DSP) tool.
…TATTCCGATCGATCGATCTCTCTAGCGTCTAC
GCTATCATCGCTCTCTATTATCGCGCG ……

Symbolic to
Numeric Mapping

Track the strength
of the period-3
component using
DSP tool

Decision Making

Exons Locations


Exons Prediction using DFT
 Sliding window DFT is one of various DSP methods previously

proposed in the filed of exons prediction based on DNA spectral
analysis.

Numerical
Mapping

X[n]

Sliding
Window
DFT

S[L/3]

Exons Locations


 Calculating the power spectrum of a windowed DNA

numerical sequence at k=L/3 is sufficient as it is expected to be
large value in exonic regions and small value outside.

Numerical
Mapping

X[n]

Sliding
Window
DFT

S[L/3]


 A hard decision for each nucleotide (exonic or intronic

nucleotide) is made according to the corresponding S[L/3] value,
whether it is above or below a decision threshold.

S[L/3]

Exons Locations


 In our work, we selected two symbolic-to-numeric mapping

schemes from different schemes that previously showed a
reasonable performance.

Nucleotide

Numerical
Mapping

CIS

Adenine (A)

0.1260

1

Cytosine (C)

X[n]

EIIP

0.1340

-j

Guanine (G)

0.0806

-1

Thymine (T)

0.1335

j


…TATTCCGATCGATCGAT…CTCTC…TAGCGTCT
ACGCTATCATCGCTCTCT…ATTATCGCGCG ……

Gene F56F11.4 contains
five exons
1

EIIP
Mapping

X[n]

Sliding
Window
DFT

0.5

S[L/3]
0

0

1000

2000

3000
4000
5000
Nucleotide Positions

6000

7000

8000

0

1000

2000

3000
4000
5000

6000

7000

8000

1

CIS
Mapping

X[n]

Sliding
Window
DFT

0.5

S[L/3]
0


Gene F56F11.4 contains
five exons

 Each mapping scheme is able

pronounce the peaks in
some exonic segments than the
other scheme.

 The

peaks in the exonic
segments are not always
consistently large while
those in the intronic segments
are not always consistently low.

1

0.5

0

0

1000

2000

3000
4000
5000

6000

7000

8000

0

1000

2000

3000
4000
5000

6000

7000

8000

1

0.5

0


Proposed Soft Decisions Fusion Approach

EIIP
Mapping

X[n]

Sliding
Window
DFT

S[L/3]

Soft Decisions
CIS
Mapping

X[n]

Sliding
Window
DFT

S[L/3]


Hard Decision (0 or 1)

Soft Decision (0 to 1)


 Each nucleotide belongs to exonic regions with a partial

S[L/3]

membership value (i.e possibility of being an exonic nucleotide).



EIIP
Mapping

X[n]

Sliding
Window
DFT

S[L/3]
DFC

CIS
Mapping

X[n]

Sliding
Window
DFT

S[L/3]

Exons Locations


 The DFC averages the two local soft decisions.
 If the average exceeds 0.5 (i.e the average possibility of being

an exonic nucleotide exceeds 50% ),
the final decision is ‘1’,
otherwise ‘0’.
 The combined decision
Soft Decisions
helps in making a more
reliable decision as compared to making
a hard decision depending on only one classifier.

DFC

Exons Locations


Performance Evaluation Metrics

True
Prediction
Decision

Positive
Negative

False

True
False


 Area under the ROC curve (AUC) is a good indicator.


 F_measure Vs Decision threshold is also a good indicator.


Performance Evaluation
 MATLAB Simulation is conducted on real data (HMR195

dataset) which is available online.
 It contains 195 mammalian sequences consisting of 43 single-

exon and 152 multi-exon genes.
 Traditional and proposed approaches are simulated using

different window shapes with a constant length (L=351) as
reported in previous publications.


 AUC values for HMR195 dataset and ROC curves plotted in

case of using Bartlett window.
Window
Shape

Single Classifier
EIIP

CIS

Multiple
Classifier

Rectangular

0.7280

0.7398

0.7862

Nutall

0.7264

0.7439

0.7972

Parzen

0.7281

0.7457

0.7989

Bohman

0.7314

0.7490

0.8021

Blackman

0.7331

0.7504

0.8035

Hanning

0.7387

0.7553

0.8079

Hamming

0.7425

0.7580

0.8106

Bartlett

0.7438

0.7589

0.8115


Numerical
Scheme
used by the
classifier

Number of
Classifiers

EIIP

% of exonic nucleotides detected as true
positives
at 10% FPR

at 20% FPR

at 30% FPR

1

43.5

56.9

66.4

CIS

1

46.8

59.9

68.7

Both

2

54.1

67.3

76.0

At 10% FPR:
by 24.4 % over single classifier
using EIIP
using CIS


 Maximum

F_measures achieved and corresponding
decision thresholds for HMR195 dataset.
Single Classifier

EIIP

CIS

Multiple
Classifier

Maximum
F_measure

0.4287

0.4562

0.5086

Decision
Threshold

0.029

0.048

0.037

using EIIP
using CIS


Conclusion
 In our work, a new multiple DFT-based classifiers approach for exons

prediction has been proposed.

 Making soft decisions instead of hard decisions and depending on two

classifiers instead of one helps in making more reliable decisions.

 The prediction accuracy is enhanced at the expense of increasing

computational time and complexity.

 Although the analysis of the proposed approach has been investigated in

case of only two classifiers for simplicity, it can be easily be extended to
more than two classifiers.

A new multiple classifiers soft decisions fusion approach for exons prediction in dna sequences

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