Development of new computational methods to identify differential methylation in genomic data

Akanksha Srivastava

doi:10.26182/5e6830e36c613

Development of new computational methods to identify differential methylation in genomic data

Akanksha Srivastava

School of Molecular Sciences

Research output: Thesis › Doctoral Thesis

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Abstract

The current differentially methylated region (DMR) identification methods suffer from limitations such as inaccurate DMR boundary detection, a high volume of spurious DMRs, and an inability to identify DMRs in time series data. Additionally, the accuracy of DMR identification is often limited by missing methylation values in the samples. This thesis addresses the above limitations with the development of two new computational tools capable of sensitive and accurate identification of DMRs among two or more groups of samples. The new tool has been applied to identify novel and biologically significant DMRs during Intestinal stem cell aging and plant germination.

Original language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Thesis sponsors	Australian Government Research Training Program
Award date	14 Feb 2020
DOIs	https://doi.org/10.26182/5e6830e36c613
Publication status	Unpublished - 2020

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Srivastava, A. (2020). Development of new computational methods to identify differential methylation in genomic data. https://doi.org/10.26182/5e6830e36c613

@phdthesis{9bb492b4104848b9965a8e7010e088a1,

title = "Development of new computational methods to identify differential methylation in genomic data",

abstract = "The current differentially methylated region (DMR) identification methods suffer from limitations such as inaccurate DMR boundary detection, a high volume of spurious DMRs, and an inability to identify DMRs in time series data. Additionally, the accuracy of DMR identification is often limited by missing methylation values in the samples. This thesis addresses the above limitations with the development of two new computational tools capable of sensitive and accurate identification of DMRs among two or more groups of samples. The new tool has been applied to identify novel and biologically significant DMRs during Intestinal stem cell aging and plant germination.",

keywords = "Differentially methylated regions, Support vector machine, convolutional neural network, whole genome bisulfite sequencing, Machine learning, Deep Learning",

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language = "English",

school = "The University of Western Australia",

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AU - Srivastava, Akanksha

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Y1 - 2020

N2 - The current differentially methylated region (DMR) identification methods suffer from limitations such as inaccurate DMR boundary detection, a high volume of spurious DMRs, and an inability to identify DMRs in time series data. Additionally, the accuracy of DMR identification is often limited by missing methylation values in the samples. This thesis addresses the above limitations with the development of two new computational tools capable of sensitive and accurate identification of DMRs among two or more groups of samples. The new tool has been applied to identify novel and biologically significant DMRs during Intestinal stem cell aging and plant germination.

AB - The current differentially methylated region (DMR) identification methods suffer from limitations such as inaccurate DMR boundary detection, a high volume of spurious DMRs, and an inability to identify DMRs in time series data. Additionally, the accuracy of DMR identification is often limited by missing methylation values in the samples. This thesis addresses the above limitations with the development of two new computational tools capable of sensitive and accurate identification of DMRs among two or more groups of samples. The new tool has been applied to identify novel and biologically significant DMRs during Intestinal stem cell aging and plant germination.

KW - Differentially methylated regions

KW - Support vector machine

KW - convolutional neural network

KW - whole genome bisulfite sequencing

KW - Machine learning

KW - Deep Learning

U2 - 10.26182/5e6830e36c613

DO - 10.26182/5e6830e36c613

M3 - Doctoral Thesis

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Access to Document

10.26182/5e6830e36c613

TH20 32 THESIS - DOCTOR OF PHILOSOPHY - SRIVASTAVA Akanksha - 2020
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