Integrating Single-Molecule Sequencing and Deep Learning to Predict Haplotype-Specific 3D Chromatin Organization in a Mendelian Condition

Abstract

The three-dimensional (3D) architecture of the genome plays a crucial role in gene regulation and various human diseases. Short-read sequencing methods for measuring 3D genome organization are powerful, but they lack the ability to resolve individual human haplotypes or structurally complex regions. To address this, we present FiberFold, a deep learning model that combines convolutional neural networks and transformer architectures to accurately predict cell-type-specific and haplotype-specific 3D genome organization using multi-omic data from a single, long-read sequencing assay, Fiber-seq. By applying FiberFold to a cell line with allelic X-inactivation, we show that Topologically Associated Domains (TADs) are attenuated on the inactive chrX. Furthermore, FiberFold predicts significant changes to TADs surrounding a 13;X balanced translocation in a patient with a rare Mendelian disease. FiberFold showcases the power of integrating long-read epigenomic sequencing with deep learning tools to investigate fundamental chromatin biology as well as the molecular basis of human disease.