Biology subjects

Detinis Zur, T.

Publications and source records attributed to Detinis Zur, T..

3 recordsLinked to original sources

bioRxiv2023

Design of optimal labeling patterns for optical genome mapping via information theory

Optical genome mapping (OGM) is a technique that extracts partial genomic information from optically imaged and linearized DNA fragments containing fluorescently labeled short sequence patterns. This information can be used for various genomic analyses and applications, such as the detection of structural variations and copy-number variations, epigenomic profiling, and microbial species identification. Currently, the choice of labeled patterns is based on the available bio-chemical methods, and is not necessarily optimized for the application. In this work, we develop a model of OGM based on information theory, which enables the design of optimal labeling patterns for specific applications and target organism genomes. We validated the model through experimental OGM on human DNA and simulations on bacterial DNA. Our model predicts up to 10-fold improved accuracy by optimal choice of labeling patterns, which may guide future development of OGM bio-chemical labeling methods and significantly improve its accuracy and yield for applications such as epigenomic profiling and cultivation-free pathogen identification in clinical samples.

bioinformatics↗

bioRxiv2022

DeepOM: Single-molecule optical genome mapping via deep learning

Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule fragment is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optical genome mapping utilizing Deep Learning is presented, termed DeepOM. Utilization of a Convolutional Neural Network (CNN), trained on simulated images of labeled DNA molecules, improves the success rate in alignment of DNA images to genomic references. The method is evaluated on acquired images of human DNA molecules stretched in nano-channels. The accuracy of the method is benchmarked against state-of-the-art commercial software Bionano Solve. The results show a significant advantage in alignment success rate for molecules shorter than 50 kb. DeepOM improves yield, sensitivity and throughput of optical genome mapping experiments in applications of human genomics and microbiology.

bioinformatics↗

bioRxiv2022

Optical Genome and Epigenome Mapping of Clear Cell Renal Cell Carcinoma

Cancer cells display complex genomic aberrations that include large-scale genetic rearrangements and epigenetic modulation that are not easily characterized by short-read sequencing. We present a method for simultaneous profiling of long-range genetic/epigenetic changes in matched cancer samples. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. Most ccRCC cases demonstrate somatic genomic alterations involving the short arm of chromosome 3 (3p), most often targeting the von Hippel-Lindau (VHL) gene. Aiming to identify somatic alterations that characterize early stage ccRCC, we performed comprehensive genetic, cytogenetic and epigenetic analyses comparing ccRCC tumor to adjacent non-tumorous tissue. Optical genome mapping identified genomic aberrations such as structural and copy number variations, complementing exome-sequencing results. Single-molecule methylome and hydroxymethylome mapping revealed multiple differential regions, some of them known to be associated with ccRCC pathogenesis. Among them, metabolic pathways were significantly enriched. Moreover, significant global epigenetic differences were detected between the tumor and the adjacent non-tumorous tissue, and a correlation between epigenetic signals and gene expression was found. This is the first reported comparison of a human tumor and a matched tissue by optical genome/epigenome mapping, revealing well-established and novel somatic aberrations.

genomics↗