Study

RRBS sequencing of 7 tumour regions and a normal sample from a single TRACERx patient.

Study ID Alternative Stable ID Type
EGAS00001002484 Other

Study Description

Background: Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that the study of DNA methylation (DNAm) patterns along the genome - so-called `epialleles' - offers greater insight into epigenetic dynamics than conventional analyses which examine DNAm marks individually. Results: We have developed a Bayesian model to infer which epialleles are present in multiple regions of the same tumour. We apply our method to reduced representation bisulfite sequencing (RRBS) data from multiple regions of one lung cancer tumour and a matched normal sample. The model borrows information from all tumour regions to leverage greater statistical power. The total number of epialleles, the epiallele DNAm patterns, and a noise hyperparameter are all automatically inferred from the data. Uncertainty as to which epiallele an observed sequencing read originated from is explicitly incorporated by marginalising over the appropriate posterior densities. The degree to which tumour samples are ... (Show More)

Study Datasets 1 dataset.

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Dataset ID Description Technology Samples
EGAD00001003404
RRBS sequencing of 7 tumour regions and a normal sample from a single TRACERx patient.
Illumina HiSeq 2500 8

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