David E Frankhouser1, Mark Murphy1, James S Blachly1, Jincheol Park2, Mike W Zoller1, Javkhlan-Ochir Ganbat1, John Curfman1, John C Byrd1, Shili Lin1, Guido Marcucci1, Pearlly Yan1, Ralf Bundschuh3. 1. College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA. 2. College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA. 3. College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA College of Medicine, Biomedical Sciences Graduate Program, Department of Internal Medicine, Division of Hematology, Department of Statistics, Mathematical Biosciences Institute, Department of Physics, Department of Chemistry & Biochemistry and Center for RNA Biology, The Ohio State University, Columbus, OH 43210, USA.
Abstract
MOTIVATION: DNA methylation is an epigenetic change occurring in genomic CpG sequences that contribute to the regulation of gene transcription both in normal and malignant cells. Next-generation sequencing has been used to characterize DNA methylation status at the genome scale, but suffers from high sequencing cost in the case of whole-genome bisulfite sequencing, or from reduced resolution (inability to precisely define which of the CpGs are methylated) with capture-based techniques. RESULTS: Here we present a computational method that computes nucleotide-resolution methylation values from capture-based data by incorporating fragment length profiles into a model of methylation analysis. We demonstrate that it compares favorably with nucleotide-resolution bisulfite sequencing and has better predictive power with respect to a reference than window-based methods, often used for enrichment data. The described method was used to produce the methylation data used in tandem with gene expression to produce a novel and clinically significant gene signature in acute myeloid leukemia. In addition, we introduce a complementary statistical method that uses this nucleotide-resolution methylation data for detection of differentially methylated features.
MOTIVATION: DNA methylation is an epigenetic change occurring in genomic CpG sequences that contribute to the regulation of gene transcription both in normal and malignant cells. Next-generation sequencing has been used to characterize DNA methylation status at the genome scale, but suffers from high sequencing cost in the case of whole-genome bisulfite sequencing, or from reduced resolution (inability to precisely define which of the CpGs are methylated) with capture-based techniques. RESULTS: Here we present a computational method that computes nucleotide-resolution methylation values from capture-based data by incorporating fragment length profiles into a model of methylation analysis. We demonstrate that it compares favorably with nucleotide-resolution bisulfite sequencing and has better predictive power with respect to a reference than window-based methods, often used for enrichment data. The described method was used to produce the methylation data used in tandem with gene expression to produce a novel and clinically significant gene signature in acute myeloid leukemia. In addition, we introduce a complementary statistical method that uses this nucleotide-resolution methylation data for detection of differentially methylated features.
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