Long reads capture simultaneous enhancer–promoter methylation status for cell-type deconvolution

Author:

Margalit Sapir12,Abramson Yotam12,Sharim Hila12,Manber Zohar23,Bhattacharya Surajit4,Chen Yi-Wen45,Vilain Eric45,Barseghyan Hayk45,Elkon Ran23,Sharan Roded26,Ebenstein Yuval12

Affiliation:

1. Department of Physical Chemistry, Tel Aviv University, Tel Aviv 6997801, Israel

2. Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv 6997801, Israel

3. Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel

4. Center for Genetic Medicine Research, Children’s National Hospital, Washington, DC 20010, USA

5. Department of Genomics and Precision Medicine, George Washington University, Washington, DC 20052, USA

6. School of Computer Science, Tel-Aviv University, Tel-Aviv 6997801, Israel

Abstract

Abstract Motivation While promoter methylation is associated with reinforcing fundamental tissue identities, the methylation status of distant enhancers was shown by genome-wide association studies to be a powerful determinant of cell-state and cancer. With recent availability of long reads that report on the methylation status of enhancer–promoter pairs on the same molecule, we hypothesized that probing these pairs on the single-molecule level may serve the basis for detection of rare cancerous transformations in a given cell population. We explore various analysis approaches for deconvolving cell-type mixtures based on their genome-wide enhancer–promoter methylation profiles. Results To evaluate our hypothesis we examine long-read optical methylome data for the GM12878 cell line and myoblast cell lines from two donors. We identified over 100 000 enhancer–promoter pairs that co-exist on at least 30 individual DNA molecules. We developed a detailed methodology for mixture deconvolution and applied it to estimate the proportional cell compositions in synthetic mixtures. Analysis of promoter methylation, as well as enhancer–promoter pairwise methylation, resulted in very accurate estimates. In addition, we show that pairwise methylation analysis can be generalized from deconvolving different cell types to subtle scenarios where one wishes to resolve different cell populations of the same cell-type. Availability and implementation The code used in this work to analyze single-molecule Bionano Genomics optical maps is available via the GitHub repository https://github.com/ebensteinLab/Single_molecule_methylation_in_EP.

Funder

European Research Council Consolidator

NIH

NIH National Human Genome Research Institute

Israel Science Foundation

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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