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

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)

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 ofthe important topics in biosignal processing and bioinformaticsarea.

With the significant growth of sequenced genomic data, it hasbecome important to come up with computarized methods forpredicting these important protein coding regions (exons) in DNAsequences.


DNA Structure

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

and almost all other organisms.


DNA Structure Organisms can be categorized intoprokaryotes (e.g bacteria) andeukaryotes (e.g human).

In both categories, DNA consistsof genes separated by intergenicregions.

In eukaryotes, genes are furtherdivided into protein-codingregions (exons) and non-coding 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.



Accurate prediction of the exons locations in DNA sequences is

an important issue for biologists since they are considered as

information bearing parts.


Exons

finder

TATTCCGATCGATCGATCT

CTCTAGCGTCTACGCTATC

ATCGCTCTCTATTATCGCG

CG ……

Exons Locations

Predicting Exons Locations The order of the nucleotides

stored in the Exons spell out acode for protein synthesis.

Triplets of nucleotides (codons)in the exonic segments of DNAspecify each type of amino acidbased on a genetic code.

Each amino acid is encoded by oneor more codons (many to onemapping).



It was shown in previous publications that exonic parts exhibit a

period-3 property due to the codon structure and the non-

uniform 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.


Symbolic to

Numeric Mapping

Track the strength

of the period-3

component using

DSP tool

Decision Making

…TATTCCGATCGATCGATCTCTCTAGCGTCTAC

GCTATCATCGCTCTCTATTATCGCGCG ……

Exons Locations


Sliding window DFT is one of various DSP methods previously

proposed in the filed of exons prediction based on DNA spectral

analysis.


Numerical

Mapping

Sliding

Window

DFT



Exons Locations

X[n] S[L/3]


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

Sliding

Window

DFT



X[n] 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.


Exons Locations

S[L/3]


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

schemes from different schemes that previously showed a

reasonable performance.


Numerical

Mapping



Nucleotide EIIP CIS

Adenine (A) 0.1260 1

Cytosine (C) 0.1340 -j

Guanine (G) 0.0806 -1

Thymine (T) 0.1335 j

X[n]



EIIP

Mapping

Sliding

Window

DFT

…TATTCCGATCGATCGAT…CTCTC…TAGCGTCT

ACGCTATCATCGCTCTCT…ATTATCGCGCG ……

CIS

Mapping

Sliding

Window

DFT

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1

Nucleotide Positions

0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1


X[n]

X[n]

S[L/3]

S[L/3]

Gene F56F11.4 contains

five exons


Each mapping scheme is ablepronounce the peaks insome exonic segments than theother scheme.

The peaks in the exonicsegments are not alwaysconsistently large whilethose in the intronic segmentsare not always consistently low.


0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1


0 1000 2000 3000 4000 5000 6000 7000 80000

0.5

1


Gene F56F11.4 contains

five exons



EIIP

Mapping

Sliding

Window

DFT



CIS

Mapping

Sliding

Window

DFT

X[n]

X[n]

S[L/3]

S[L/3]

Soft Decisions



Hard Decision (0 or 1)

Soft Decision (0 to 1)


Each nucleotide belongs to exonic regions with a partial

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

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

L/

3]



EIIP

Mapping

Sliding

Window

DFT



CIS

Mapping

Sliding

Window

DFT

X[n]

X[n]

S[L/3]

S[L/3]

DFC

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

helps in making a more

reliable decision as compared to making

a hard decision depending on only one classifier.


DFC

Exons Locations

Soft Decisions

Performance Evaluation Metrics


Prediction

Decision

PositiveTrue

False

NegativeTrue

False


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



F_measureVs Decision threshold is also a good indicator.



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 Multiple

ClassifierEIIP CIS

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

% of exonic nucleotides detected as true

positives

at 10% FPR at 20% FPR at 30% FPR

EIIP 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 Multiple

ClassifierEIIP CIS

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 twoclassifiers instead of one helps in making more reliable decisions.

The prediction accuracy is enhanced at the expense of increasingcomputational time and complexity.

Although the analysis of the proposed approach has been investigated incase of only two classifiers for simplicity, it can be easily be extended tomore than two classifiers.


Thank You

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

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