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.
|Qualification||Doctor of Philosophy|
|Award date||14 Feb 2020|
|Publication status||Unpublished - 2020|