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Abstract:
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Post-transcriptional gene silencing by RNA interference is
mediated by small interfering RNA called siRNA. This gene
silencing mechanism can be exploited therapeutically to a wide
variety of disease-associated targets, especially in AIDS,
neurodegenerative diseases, cholesterol and cancer on mice with the
hope of extending these approaches to treat humans. Over the recent
past, a significant amount of work has been undertaken to understand
the gene silencing mediated by exogenous siRNA. The design of
efficient exogenous siRNA sequences is challenging because of many
issues related to siRNA. While designing efficient siRNA, target
mRNAs must be selected such that their corresponding siRNAs are
likely to be efficient against that target and unlikely to accidentally
silence other transcripts due to sequence similarity. So before doing
gene silencing by siRNAs, it is essential to analyze their off-target
effects in addition to their inhibition efficiency against a particular
target. Hence designing exogenous siRNA with good knock-down
efficiency and target specificity is an area of concern to be addressed.
Some methods have been developed already by considering both
inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition
efficiency, specificity and sensitivity.
The main focus of this thesis is to develop computational
methods to optimize the efficiency of siRNA in terms of “inhibition
capacity and off-target possibility” against target mRNAs with
improved efficacy, which may be useful in the area of gene silencing
and drug design for tumor development. This study aims to
investigate the currently available siRNA prediction approaches and
to devise a better computational approach to tackle the problem of
siRNA efficacy by inhibition capacity and off-target possibility. The
strength and limitations of the available approaches are investigated
and taken into consideration for making improved solution. Thus the
approaches proposed in this study extend some of the good scoring
previous state of the art techniques by incorporating machine learning
and statistical approaches and thermodynamic features like whole
stacking energy to improve the prediction accuracy, inhibition
efficiency, sensitivity and specificity. Here, we propose one Support
Vector Machine (SVM) model, and two Artificial Neural Network
(ANN) models for siRNA efficiency prediction. In SVM model, the
classification property is used to classify whether the siRNA is
efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition
efficiency of siRNA against target genes. The second ANN model,
named Optimized siRNA Designer, OpsiD, produces efficient
siRNAs with high inhibition efficiency to degrade target genes with
improved sensitivity-specificity, and identifies the off-target knockdown
possibility of siRNA against non-target genes. The models are
trained and tested against a large data set of siRNA sequences. The
validations are conducted using Pearson Correlation Coefficient,
Mathews Correlation Coefficient, Receiver Operating Characteristic
analysis, Accuracy of prediction, Sensitivity and Specificity.
It is found that the approach, OpsiD, is capable of predicting
the inhibition capacity of siRNA against a target mRNA with
improved results over the state of the art techniques. Also we are able
to understand the influence of whole stacking energy on efficiency of
siRNA. The model is further improved by including the ability to
identify the “off-target possibility” of predicted siRNA on non-target
genes. Thus the proposed model, OpsiD, can predict optimized
siRNA by considering both “inhibition efficiency on target genes and
off-target possibility on non-target genes”, with improved inhibition
efficiency, specificity and sensitivity. Since we have taken efforts to
optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while
doing gene silencing in various bioinformatics fields can be
overcome to a great extent. These findings may provide new insights
into cancer diagnosis, prognosis and therapy by gene silencing. The
approach may be found useful for designing exogenous siRNA for
therapeutic applications and gene silencing techniques in different
areas of bioinformatics. |