Patterns in genomic methylation determined with long-read sequencing

Abstract

During the last two decades, whole-genome sequencing (WGS) quickly became a go-to tool to diagnose molecular diseases and disorders. A new technology for WGS, long-read sequencing, has the future potential to improve molecular diagnostic rates by identification of multiple types of variants using a single test, including epigenetic modifications such as DNA methylation. Methylation state across regulatory regions controls gene expression and abnormal methylation can lead to a loss of function as much as a detrimental change in sequence. Methylation patterns are complex and appear to have cumulative effect. We describe an aggregation computational algorithm that enables detection of methylation pattern changes in aligned and partially phased long-read sequence data, parent-of-origin phasing (using long-read sequencing or short-read sequencing parental samples), as well as establishing a panel-of-normals background and standard variance based on a set of 55 high-quality samples.

Biography

Sergey Batalov’s long-term research interests involve the development of algorithms and tools that elucidate molecular and biological gene function inference from sequence and genotype-phenotype association data. He has contributed to the development of multiple publicly available resources, ranging from well-known gene expression databases in human and mouse (and a gene annotation portal known as BioGPS.org), to a community consortium for functional annotation of genes (FANTOM), and to NGS analysis and epigenomics to empower precision medicine.