First name
Kamal
Last name
Kishore
Year of Study
Research Center
Thesis Title
Development of Computational Tools to Study the Patterning of DNA and RNA Methylation in Healthy and Disease States
Thesis Abstract
Epigenetics can be defined as the set of sequence independent processes
that produces heritable changes in cellular information. These chromatinbased
events such as covalent modification of DNA and histone tails are laid
down by the co-ordinated action of chromatin modifying enzymes, thus
altering the organisation of chromatin and its accessibility to the
transcriptional machinery. Our understanding of epigenetic intricacies has
considerably increased over the last decade owing to rapid development of
genomic and proteomic technologies. This has resulted in huge surge in the
generation of epigenomics data. Integrative analysis of these epigenomics
datasets provides holistic view on the interplay of various epigenetic
components and possible aberration in patterns in specific biological or
disease states. Although, there are numerous computational tools available
catering individually to each epigenomic datatype, a comprehensive
computational framework for integrated exploratory analysis of these
datasets was missing. We developed a suite of R packages methylPipe and
compEpiTools that can efficiently handle whole genome base-resolution DNA
methylation datasets and effortlessly integrate them with other epigenomics
data. We applied these methods to the study of epigenomics landscape in Bcell
lymphoma identifying a putative set of tumor suppressor genes.
Moreover, we also applied these methods to explore possible associations
between m6A RNA methylation, epigenetic marks and regulatory proteins.
that produces heritable changes in cellular information. These chromatinbased
events such as covalent modification of DNA and histone tails are laid
down by the co-ordinated action of chromatin modifying enzymes, thus
altering the organisation of chromatin and its accessibility to the
transcriptional machinery. Our understanding of epigenetic intricacies has
considerably increased over the last decade owing to rapid development of
genomic and proteomic technologies. This has resulted in huge surge in the
generation of epigenomics data. Integrative analysis of these epigenomics
datasets provides holistic view on the interplay of various epigenetic
components and possible aberration in patterns in specific biological or
disease states. Although, there are numerous computational tools available
catering individually to each epigenomic datatype, a comprehensive
computational framework for integrated exploratory analysis of these
datasets was missing. We developed a suite of R packages methylPipe and
compEpiTools that can efficiently handle whole genome base-resolution DNA
methylation datasets and effortlessly integrate them with other epigenomics
data. We applied these methods to the study of epigenomics landscape in Bcell
lymphoma identifying a putative set of tumor suppressor genes.
Moreover, we also applied these methods to explore possible associations
between m6A RNA methylation, epigenetic marks and regulatory proteins.
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