An ANN-GA model based promoter prediction in Arabidopsis thaliana using tilling microarray data.

Abstract

Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to predict the promoter regions in eukaryotic genomes.

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Prediction model
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An ANN-GA model based promoter prediction in
Arabidopsis thaliana using tilling microarray data


Hrishikesh Mishra, Nitya Singh, Krishna Misra, Tapobrata Lahiri*


Division of Applied Sciences and Indo-Russian Centre for Biotechnology, Indian Institute of Information Technology, Allahabad, India; Tapobrata Lahiri - Email:
tlahiri@iiita.ac.in; Phone: 91-532-2922242; Fax: 91-532-2430006; *Corresponding author


Received February 10, 2011; Accepted May 09, 2011; Published June 06, 2011


Abstract:
Identification of promoter region is an important part of gene annotation. Identification of promoters in eukaryotes is important as promoters modulate various
metabolic functions and cellular stress responses. In this work, a novel approach utilizing intensity values of tilling microarray data for a model eukaryotic plant
Arabidopsis thaliana, was used to specify promoter region from non-promoter region. A feed-forward back propagation neural network model supported by
genetic algorithm was employed to predict the class of data with a window size of 41. A dataset comprising of 2992 data vectors representing both promoter and
non-promoter regions, chosen randomly from probe intensity vectors for whole genome of Arabidopsis thaliana generated through tilling microarray technique
was used. The classifier model shows prediction accuracy of 69.73% and 65.36% on training and validation sets, respectively. Further, a concept of distance based
class membership was used to validate reliability of classifier, which showed promising results. The study shows the usability of micro-array probe intensities to
predict the promoter regions in eukaryotic genomes.


Keywords: core promoter, TATA box, artificial neural network, class membership.


ISSN 0973-2063 (online) 0973-8894 (print)
Bioinformation 6(6): 240-243 (2011)
 

240 © 2011 Biomedical Informatics


Background:
With the continuously increasing number of various genome sequencing
projects, predicting promoters has become one of the prime focuses for gene
identification and annotation. Promoter-prediction is multi-informative, as
along with delineating one end of gene and being key to gene regulation; it also
gives a cue to functional aspect of gene [1]. In eukaryotes promoter guides the
cell development and differentiation, tissue morphogenesis and specificity,
hormonal communication, and cellular stress responses [2]. Moreover the
knowledge of promoter may lend clues for function of completely anonymous
proteins which cannot be retrieved from already predicted amino acid
sequences [3]. In eukaryotes promoters are classically defined as the start site
of transcription (TSS) [1]. According to a geneticist’s view promoters are cis-
acting elements deciding the site and rate of transcription, while according to
biochemist’s view; these are target sites of transcription factors [4]. Eukaryotic
promoters are of three types viz., core promoters, proximal promoters and
distal promoters that have different roles in gene regulation. Core promoters,
also known as minimal promoters are located ~80 to 100 base pairs (bp) around
TSS and are required for initiation of transcription. Proximal promoters located
~250 to 1000 upstream of core promoter, are position and species specific,
involve transcription factor binding site and initiate basal transcription. Distal
promoters located further upstream and also known as enhancers involve
additional regulatory element binding sites at distal regions from transcription
start site [5, 6]. For computational prediction, core promoter is more important
as it is the first essential site required for the initiation of transcription [1].
Eukaryotic core promoters have TATA box as an integral part which lies 25-30
bp upstream of TSS [7]. So for retrieving the locations of promoter sequences,
location of TATA box can be searched and region upstream to TSS with TATA
box of known genes can be targeted. Major challenges associated with
promoter prediction methods involve weak models used for promoter regions
and algorithmic constraints [5]. Also, it has been reported that using existing
promoter prediction methods miss 30-40% of true promoters and have a false
positive rate of 45-60% [8]. Complex architecture of promoter sequences
presents a computational problem yet to be solved satisfactorily [3]. Methods
for promoter prediction are in their infancy and level of accuracy achieved is
low [9].

The success of prediction depends upon biological model, type and quality of
training data utilized [10]. In general, most of the computational methods for
promoter prediction are based on models searching for organization of
promoters, promoter location [11] or for the hallmarks of promoters as CpG
islands [12], TATA boxes [13], CAAT boxes [14], specific transcription factor
binding sites (TFBSs) [13, 14], pentamer matrix [15] and oligonucleotides [16].
Various computational strategies applied for this purpose involve: neural
networks [12], linear and quadratic discriminant analyses [17], interpolated
Markov Model [14], independent component analysis (ICA) [18], and non-
negative matrix factorization (NMF) [19]. All the methods have their own
advantages and limitations. Selection of an appropriate combination of
biological features and computational approach for accurate promoter
prediction is still an open issue. In the present work we have dealt with this
problem by utilizing tilling microarray intensities for nucleosome rich DNA of
chromosome 1 of Arabidopsis thaliana. Although, microarray expression data
have been used to establish correlation between DNA sequence and promoter-
strength [20], but using it for predicting the promoter location is a novel
approach.

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It has been shown that nucleosome is located at ~ +40±15 bp of TSS i.e.,
mostly downstream from typical TATA box position. It can be inferred that
TATA box is situated within 5’ half of nucleosomal DNA or right upstream of
it. Further, nucleosomal distribution around the TSS has a strong correlation
with promoter region [21]. Therefore, microarray experiment was designed to
retrieve nucleosomal rich DNA regions to ensure the incorporation of promoter
regions. Further, as analyzed from various biological features adopted for
promoter prediction, length and composition of DNA sequence i.e., GC content
plays an important role in distinguishing promoter region from non-promoter
[22]. Difference in melting temperature due to different composition of
promoter sequence may result into different pattern in hybridization intensity
which can be detected. Our work explored conglomerative machine learning
approach to utilize data obtained from high throughput and high resolution
tilling microarray data for detection of promoter region.

Methodology:
Dataset:
Intensity values for mononucleosomal DNA regions were obtained from tilling
microarray data downloaded from gene expression Omnibus (GEO) at NCBI
having GEO accession:
http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse25553. Also whole
genomic DNA tilling microarray intensity values generated under the same
experimental conditions were obtained from same geo data series, which were
used for normalization of mononucleosomal DNA intensity data. Intensity data
for mononucleosomal regions was vectorized to consider the context intensity
values of neighboring probes as a window around centrally positioned intensity
value. Several window sizes were tested and finally size of 41 was chosen by
trial and error to include 41 consecutive probes, considering the average range
of number of probes comprising different known promoter regions in training
data.

Each window comprised the intensity for the probe in question (P) positioned
at the centre of the window i.e., 21st position so that P was represented with 20
intensity values on both sides. A probe in question (P) refers to the tilling array
position against which the nucleosome was to be detected. For extremities zero
padding of required size was done. Thus finally the input feature for 779303
data yields 779303 row vectors each consisting of 41intensity values. Promoter
locations for fifty genes from chromosome 1 of A. thaliana were identified
considering the presence of TATA box in the proximity (17-25 base pairs
upstream) of start codon for the genes concerned. Intensity vectors
corresponding to these promoter regions were retrieved to prepare the initial
data set. All the 1492 vectors representing 50 promoter regions and 1500
vectors representing non-promoter regions were retrieved. Thus we had 2992
data each comprising a vector of 41 consecutive probe intensity values.

Training set and Validation set:
Data was divided with a ratio of 30:20 for training (897 and 900 data for
promoters and non-promoters respectively) and validation (595 and 600 for
promoters and non-promoters respectively) data set respectively. Thus training
and validation sets consisted of 1797 and 1195 vectors respectively. Mean was
calculated for data of each column of training set. Training data was
normalized by subtracting the column wise mean from each element of column
concerned. The mean calculated from training data was used for normalizing
the validation set also.

Model development using Neuro-GA approach:
The classification of data was done using a feed-forward backpropagation
network model, where normalized probe intensity vectors were fed as input.
Several architectures for neural network were tried for training. Finally a three
layer neural network giving maximum efficiency was chosen as classification
model. While the input layer comprised forty one inputs, first and second
hidden layers consist of twenty and ten nodes respectively. The output layer
contains two nodes to represent two classes of our interest i.e., promoter and
non-promoter. Target output vectors were created as [-1 1] for promoter region
and [1 -1] for non promoter regions. Tan sigmoid transfer function was used in
both hidden and output layers. To enhance the speed of learning Levenberg-
Marquardt algorithm was applied. Mean square error of decisions was used as
performance function for the network. The weights and biases of trained ANN
were arranged into a vector comprising 1072 variables and optimized using a
strategy following a published protocol using Genetic Algorithm [23]. Initial
population was created by randomly adding or subtracting uniformly
distributed random numbers between 0 to 10% of the value of each element of
the combined vector obtained by the combination of weight-matrices and bias-
vectors. Fitness scaling was done using the rank of each individual. Scattered
GEO25553, available at
crossover and uniform mutation with rate of 0.01 was used to generate new
generations of population. Percentage efficiency in correctly classifying
validation data using current weights and biases was used as fitness function.
Optimized weights and biases were used for promoter-prediction.

Cross validation using efficiency measures and distance based
membership:
Sensitivity, specificity and accuracy were used as measures of efficiency of
classification as the classification of probe intensity values into promoter and
non-promoter classes is a binary classification [24]. Further a new statistic
‘distance based membership’ of correctly predicted class by neural network in
the original class was calculated. Euclidian distances were measured between
the predicted output and original target output as given in Supplementary
material.

Discussion:
Accuracy, sensitivity and specificity of trained network on training data set
were found to be 69.73%, 73.91% and 65.56% respectively. Accuracy,
sensitivity and specificity of trained network on validation data set were found
to be 65.36%, 71.26% and 59.50% respectively, as tabularized in Table 1 (see
Supplementary material). Our method has given false positive rate of 40.50%
on validation data set as compared to existing methods which have a false
positive rate of 45-60% as discussed previously. Similarly false negative rate
was found to be 28.74% which is lower than currently available methods
known to miss 30-40% of true promoters. Results obtained from this pilot
study in the direction of a new approach for predicting eukaryotic promoters
appear to be promising. Nucleosomal DNA experiment intensity values were
calibrated by using whole genomic DNA intensities generated by hybridizing
MNase treated genomic DNA (~150 bp) to similar Arabidopsis tilling array
chip. So finally the calibrated data had intensity values for nucleosome rich
regions along with embedded genomic DNA sequence intensities. This
facilitated statistical analysis which used the concept of context to incorporate
the effect of genomic DNA intensity values. This might have helped neural
network classifier to detect the hidden pattern in data for promoter regions.
Moreover the concept of distance based class membership was introduced that
gives an idea about the confidence with which the neural network predicted the
class for input data. Values obtained for distance based class membership were
found to be 64.14% and 64.94% for training and validation sets respectively. It
indicates that the classification decision obtained through our classifier was
reliable. The study paves a way to utilize high throughput microarray data for
fast prediction of promoter locations. None the less, the study is also helpful to
establish a link between promoter location, gene expression and nucleosomal
dynamics in the eukaryotic genome. Study of nucleosomal positioning and
dynamics in genome is of great importance and interest as it governs the gene
expression or suppression events which are at the heart of cell metabolism.

Conclusion:
The results obtained from our study which involves robust classification of
tilling microarray data provide new insights for the promoter prediction
problem. The error involved in this method is possibly because of low
resolution in the tilling microarray system. Moreover, the apparent error may
be due to the identification of unknown promoter sites for further investigation.
The distance based class membership statistics used to validate the classifier
accuracy makes the results more reliable and may be used by the machine
learning community to validate the outcome of their classifier.

Acknowledgement:
We gratefully acknowledge the helpful discussion about microarray data used
in this work by research team of Dr Sameer Sawant, National Botanical
Research Institute (NBRI), Lucknow, India. We are also grateful to Ministry of
Human Resource and Development (MHRD) and Department of Science and
Technology (DST) for their joint financial support to Division of Applied
Sciences and Indo-Russian Centre for Biotechnology at IIIT to continue this
work.

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242 © 2011 Biomedical Informatics
 
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Edited by P Kangueane
Citation: Mishra et al. Bioinformation 6(6): 240-243 (2011)
License statement: This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes,
provided the original author and source are credited. 

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ISSN 0973-2063 (online) 0973-8894 (print)
Bioinformation 6(6): 240-243 (2011)
 

243 © 2011 Biomedical Informatics
 
Supplementary material:

Euclidian distances:

d=√((e1-o1)2 + (e2-o2)2)

where e1, e2 are expected outputs and o1, o2 are predicted outputs of two output nodes respectively. For example, maximum possible distance between the predicted
and original outputs for the promoter can be considered when the predicted outputs is (1 -1) instead of (-1 1) and vice versa for a non-promoter. Therefore, value of
maximum possible distance (maxd) between predicted and expected output comes out to be 2√2. Class membership was thus defined as: m=1-d/maxd. Using this
distance their actual membership for the class assigned to them was calculated. Mean value obtained for class memberships for both the classes was used as
measure of robustness of classification.

Table 1: Detailed results of classification on training and validation sets (TP= number of true positives, TN= number of true negatives, FP= number of false
positives, FN=number of false negatives)
Size of data TP TN FP FN Sensitivity Specificity Accuracy
Training Set
Validation Set
1797
1195
663
424
590
357
310
243
234
171
73.91%
71.26%
65.56%
59.50%
69.73%
65.36%