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Abstract
Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data.
DNA methylation from cell-free DNA (cfDNA) can be profiled using whole genome bisulfite sequencing (WGBS). Here, the authors develop a computational method, FinaleMe, that predicts DNA methylation and tissues of-origin in cfDNA and validate its performance using paired deep and shallow-coverage whole-genome sequencing (WGS) and WGBS data.
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1 Northwestern University, Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Chicago, USA (GRID:grid.16753.36) (ISNI:0000 0001 2299 3507); Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, USA (GRID:grid.16753.36) (ISNI:0000 0004 0619 6876); Cincinnati Children’s Hospital Medical Center, Division of Human Genetics, Cincinnati, USA (GRID:grid.239573.9) (ISNI:0000 0000 9025 8099); Cincinnati Children’s Hospital Medical Center, Division of Biomedical Informatics, Cincinnati, USA (GRID:grid.239573.9) (ISNI:0000 0000 9025 8099); University of Cincinnati College of Medicine, Department of Pediatrics, Cincinnati, USA (GRID:grid.24827.3b) (ISNI:0000 0001 2179 9593); University of Cincinnati Center for Environmental Genetics, Cincinnati, USA (GRID:grid.24827.3b) (ISNI:0000 0001 2179 9593); University of Cincinnati Cancer Center, Cincinnati, USA (GRID:grid.24827.3b) (ISNI:0000 0001 2179 9593); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)
2 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); Vanderbilt University School of Medicine, Medical Scientist Training Program, Nashville, USA (GRID:grid.152326.1) (ISNI:0000 0001 2264 7217)
3 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623)
4 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); Dana-Farber Cancer Institute, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910)
5 Dana-Farber Cancer Institute, Boston, USA (GRID:grid.65499.37) (ISNI:0000 0001 2106 9910)
6 Northwestern University, Department of Biochemistry and Molecular Genetics, Feinberg School of Medicine, Chicago, USA (GRID:grid.16753.36) (ISNI:0000 0001 2299 3507); Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, USA (GRID:grid.16753.36) (ISNI:0000 0004 0619 6876); Cincinnati Children’s Hospital Medical Center, Division of Human Genetics, Cincinnati, USA (GRID:grid.239573.9) (ISNI:0000 0000 9025 8099)
7 Cincinnati Children’s Hospital Medical Center, Division of Human Genetics, Cincinnati, USA (GRID:grid.239573.9) (ISNI:0000 0000 9025 8099)
8 Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623); Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786)