Emanuele Raineri

ASMS detects allele-specific methylation without phasing by analyzing methylation data directly, offering a faster approach for studying gene regulation and imprinting.

The paper reanalyzes exome sequencing data from rare disease cases to identify previously undetected copy number variants, resulting in diagnoses for 51 families and partial explanations for 34 individuals.

The paper introduces a method to identify heterogeneously methylated genomic regions using Nanopore reads by analyzing methylation patterns across multiple loci.

The paper introduces two tools for analyzing chromatin networks using epigenomic data in hematopoietic cells.

The paper predicts the first eigenvector of the Hi-C matrix from whole genome bisulfite sequencing data, showing that methylation variability better predicts chromatin compartments than methylation levels.

The paper derives the conditional Site Frequency Spectrum for a genomic region linked to a focal mutation and presents formulas for neutrality tests in non-recombining regions.

This study compares the epigenomes of neutrophils and monocytes to identify regulatory elements and chromatin states, revealing differences in enhancer activity and DNA methylation.

This study benchmarks somatic mutation calling in cancer genomes, identifying inconsistencies and proposing solutions to improve accuracy.