Integrating whole genome sequencing, methylation, gene expression, topological associated domain information in regulatory mutation prediction: A study of follicular lymphoma.

ABSTRACT: A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In a first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In a second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains), and enriched biological pathways. Resulting mutation block

SUBMITTER: Farooq A 

PROVIDER: S-EPMC9024376 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature