ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

Author:

Nikolaienko Oleksii1ORCID,Lønning Per Eystein12,Knappskog Stian12

Affiliation:

1. K. G. Jebsen Center for Genome-Directed Cancer Therapy, Department of Clinical Science, University of Bergen, Bergen, Norway

2. Department of Oncology, Haukeland University Hospital, Bergen, Norway

Abstract

Abstract Motivation With recent advances in the field of epigenetics, the focus is widening from large and frequent disease- or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyze such events. Results We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of ramr and confirmed its applicability for analysis of large public datasets. Using ramr we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages. Availability and implementation The R package is freely available at https://github.com/BBCG/ramr and https://bioconductor.org/packages/ramr. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Mohn Cancer Research Laboratory and was supported by funding from the K.G. Jebsen foundation

Norwegian Research Council

The Norwegian Cancer Society

Norwegian Health Region West

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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