
Abstract The assignment of variants across haplotypes, phasing, is crucial for predicting the consequences, interaction, and inheritance of mutations and is a key step in improving our understanding of phenotype and disease. However, phasing is limited by read length and stretches of homozygosity along the genome. To overcome this limitation, we designed MethPhaser, a method that utilizes methylation signals from Oxford Nanopore Technologies to extend Single Nucleotide Variation (SNV)-based phasing. We demonstrate that haplotype-specific methylations extensively exist in Human genomes and the advent of long-read technologies enabled direct report of methylation signals. For ONT R9 and R10 cell line data, we increase the phase length N50 by 78%-151% at a phasing accuracy of 83.4-98.7% To assess the impact of tissue purity and random methylation signals due to inactivation, we also applied MethPhaser on blood samples from 4 patients, still showing improvements over SNV-only phasing. MethPhaser further improves phasing across HLA and multiple other medically relevant genes, improving our understanding of how mutations interact across multiple phenotypes. The concept of MethPhaser can also be extended to non-human diploid genomes. MethPhaser is available at https://github.com/treangenlab/methphaser .
570, Medical Sciences, Science, 610, Genome informatics, Polymorphism, Single Nucleotide, Article, Biomedical Informatics, Cell Line, Medical Specialties, Medicine and Health Sciences, and Immunity, Humans, Polymorphism, Biological Phenomena, Genome, Genome, Human, Cell Phenomena, Q, Life Sciences, Genetics and Genomics, Single Nucleotide, DNA Methylation, Computational biology and bioinformatics, Haplotypes, Medical Molecular Biology, Mutation, Medical Genetics, Software, Human
570, Medical Sciences, Science, 610, Genome informatics, Polymorphism, Single Nucleotide, Article, Biomedical Informatics, Cell Line, Medical Specialties, Medicine and Health Sciences, and Immunity, Humans, Polymorphism, Biological Phenomena, Genome, Genome, Human, Cell Phenomena, Q, Life Sciences, Genetics and Genomics, Single Nucleotide, DNA Methylation, Computational biology and bioinformatics, Haplotypes, Medical Molecular Biology, Mutation, Medical Genetics, Software, Human
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 14 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
