Data analysis and creation of epigenetics database

• Main Author: Desai, Akshay A.
• Other Authors: Liu, Xiaowen
• Language: en_US
• Published: 2014
• Subjects: database,epigenetics,data analysis; DNA > Methylation > Research > Methodology; DNA > Methylation > Statistical methods; DNA > Methylation > Electronic information resources; Epigenesis > Databases; Medical informatics > Methodology > Analysis; Adenocarcinoma > Genetic aspects; Lungs > Cancer > Databases; Molecular biology > Research > Databases; Biological systems > Analysis; Genomics > Data processing; Browsers (Computer programs); Genomics > Mathematical models; Bioinformatics > Research > Methodology > Databases > Analysis; Computational biology > Databases
• Online Access: http://hdl.handle.net/1805/4452

Indiana University-Purdue University Indianapolis (IUPUI) === This thesis is aimed at creating a pipeline for analyzing DNA methylation epigenetics data and creating a data model structured well enough to store the analysis results of the pipeline. In addition to storing the results, the model is also designed to hold information which will help researchers to decipher a meaningful epigenetics sense from the results made available. Current major epigenetics resources such as PubMeth, MethyCancer, MethDB and NCBI’s Epigenomics database fail to provide holistic view of epigenetics. They provide datasets produced from different analysis techniques which raises an important issue of data integration. The resources also fail to include numerous factors defining the epigenetic nature of a gene. Some of the resources are also struggling to keep the data stored in their databases up-to-date. This has diminished their validity and coverage of epigenetics data. In this thesis we have tackled a major branch of epigenetics: DNA methylation. As a case study to prove the effectiveness of our pipeline, we have used stage-wise DNA methylation and expression raw data for Lung adenocarcinoma (LUAD) from TCGA data repository. The pipeline helped us to identify progressive methylation patterns across different stages of LUAD. It also identified some key targets which have a potential for being a drug target. Along with the results from methylation data analysis pipeline we combined data from various online data reserves such as KEGG database, GO database, UCSC database and BioGRID database which helped us to overcome the shortcomings of existing data collections and present a resource as complete solution for studying DNA methylation epigenetics data.