REMP-package: Repetitive Element Methylation Prediction

In REMP: Repetitive Element Methylation Prediction

Description

Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure directly using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE.

Overview - standard procedure

Step 1

Start out generating required dataset for prediction using initREMP. The datasets include RE information, RE-CpG (i.e. CpGs located in RE region) information, and gene annotation, which are maintained in a REMParcel object. It is recommended to save these generated data to the working directory so they can be used in the future.

Step 2

Clean Illumina methylation dataset using grooMethy. This function can help identify and fix abnormal values and automatically impute missing values, which are essential for downstream prediction.

Step 3

Run remp to predict genome-wide locus specific RE methylation.

Step 4

Use the built-in accessors and utilities in REMProduct object to get or refine the prediction results.

Author(s)

Yinan Zheng [e-mail], Lei Liu [e-mail], Wei Zhang [e-mail], Warren Kibbe [e-mail], Lifang Hou [e-mail]

Maintainer: Yinan Zheng [e-mail]

References

Zheng Y, Joyce BT, Liu L, Zhang Z, Kibbe WA, Zhang W, Hou L. Prediction of genome-wide DNA methylation in repetitive elements. Nucleic Acids Res. 2017;45(15):8697-711. PubMed PMID: 28911103; PMCID: PMC5587781. http://dx.doi.org/10.1093/nar/gkx587.

REMP documentation built on Nov. 8, 2020, 8:05 p.m.