Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/98363
Title: Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post transcriptional Gene Silencing
Researcher: Murali, Reena
Guide(s): Dr. David Peter S
Keywords: Gene Silencing
Mechanism of RNAi
siRNA Prediction
University: Cochin University of Science and Technology
Completed Date: 15-02-2015
Abstract: Post-transcriptional gene silencing by RNA interference is newlinemediated by small interfering RNA called siRNA. This gene newlinesilencing mechanism can be exploited therapeutically to a wide newlinevariety of disease-associated targets, especially in AIDS, newlineneurodegenerative diseases, cholesterol and cancer on mice with the newlinehope of extending these approaches to treat humans. Over the recent newlinepast, a significant amount of work has been undertaken to understand newlinethe gene silencing mediated by exogenous siRNA. The design of newlineefficient exogenous siRNA sequences is challenging because of many newlineissues related to siRNA. While designing efficient siRNA, target newlinemRNAs must be selected such that their corresponding siRNAs are newlinelikely to be efficient against that target and unlikely to accidentally newlinesilence other transcripts due to sequence similarity. So before doing newlinegene silencing by siRNAs, it is essential to analyze their off-target newlineeffects in addition to their inhibition efficiency against a particular newlinetarget. Hence designing exogenous siRNA with good knock-down newlineefficiency and target specificity is an area of concern to be addressed. newlineSome methods have been developed already by considering both newlineinhibition efficiency and off-target possibility of siRNA against a gene. Out of these methods, only a few have achieved good inhibition newlineefficiency, specificity and sensitivity.The main focus of this thesis is to develop computational newlinemethods to optimize the efficiency of siRNA in terms of inhibition newlinecapacity and off-target possibility against target mRNAs with newlineimproved efficacy, which may be useful in the area of gene silencing newlineand drug design for tumor development. This study aims to newlineinvestigate the currently available siRNA prediction approaches and newlineto devise a better computational approach to tackle the problem of newlinesiRNA efficacy by inhibition capacity and off-target possibility. The newlinestrength and limitations of the available approaches are investigated newlineand taken into consideration for making improved solution. Thus the newlineapproaches proposed in this stumodel, named siRNA Designer, is used for optimizing the inhibition newlineefficiency of siRNA against target genes. The second ANN model, newlinenamed Optimized siRNA Designer, OpsiD, produces efficient newlinesiRNAs with high inhibition efficiency to degrade target genes with newlineimproved sensitivity-specificity, and identifies the off-target knockdown newlinepossibility of siRNA against non-target genes. The models are newlinetrained and tested against a large data set of siRNA sequences. The newlinevalidations are conducted using Pearson Correlation Coefficient, newlineMathews Correlation Coefficient, Receiver Operating Characteristic newlineanalysis, Accuracy of prediction, Sensitivity and Specificity.
Pagination: p: 234
URI: http://hdl.handle.net/10603/98363
Appears in Departments:Department of Computer Science

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01_ title .pdfAttached File118.36 kBAdobe PDFView/Open
02_certificate.pdf143.21 kBAdobe PDFView/Open
03_ declaration.pdf113 kBAdobe PDFView/Open
04_ acknowledgements.pdf133.53 kBAdobe PDFView/Open
05_ abstract.pdf119.12 kBAdobe PDFView/Open
06_ contents.pdf136.72 kBAdobe PDFView/Open
07-list of tables.pdf123.51 kBAdobe PDFView/Open
08 list of figures.pdf132.7 kBAdobe PDFView/Open
09_ list of abbreviations .pdf76.9 kBAdobe PDFView/Open
10_ chapter 1.pdf232.24 kBAdobe PDFView/Open
11_ chapter 2.pdf373.88 kBAdobe PDFView/Open
12- chapter 3.pdf329.54 kBAdobe PDFView/Open
13_ chapter 4.pdf293.94 kBAdobe PDFView/Open
14_ chapter 5 .pdf215.25 kBAdobe PDFView/Open
14_ chapter 6.pdf532.26 kBAdobe PDFView/Open
15_ chapter 7.pdf318.08 kBAdobe PDFView/Open
16_chapter 8 .pdf3.61 MBAdobe PDFView/Open
17_ chapter 9 .pdf212.47 kBAdobe PDFView/Open
18_ references .pdf251.3 kBAdobe PDFView/Open
20_ appendix 1.pdf558.69 kBAdobe PDFView/Open
21_ appendix 2 .pdf203.08 kBAdobe PDFView/Open
22_ appendix 3 .pdf147.52 kBAdobe PDFView/Open
23_ appendix 4 .pdf185.37 kBAdobe PDFView/Open
24_ appendix 5 .pdf192.25 kBAdobe PDFView/Open


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