FiberBrowser: a unified framework for single-molecule genomics

Abstract

Single-molecule footprinting methods are rapidly transforming chromatin biology. They resolve nucleosome positioning, regulatory-factor occupancy, and accessibility on individual DNA fibers, revealing combinatorial heterogeneity that bulk measurements average away. Emerging footprinting methods leverage diverse enzyme systems and sequencing platforms, necessitating tools that can accommodate these systems. We present FiberBrowser, a unified platform that converts these diverse datasets into a common per-molecule representation of nucleosomes, accessible DNA, and subnucleosomal footprints for interactive and scripted analyses. Its inference engine, FiberHMM, weighs each observation against the assay's informative sites and sequence-specific modification probabilities, separating missing information from protection in four assays. FiberBrowser clusters, sorts and compares these calls across thousands of molecules in seconds, linking chromatin architecture to haplotype, CpG methylation and non-reference structural variants on the same molecules. Because the representation is independent of chemistry, models also transfer between assays. A classifier trained on Fiber-seq footprints identifies 12 scDAF-seq cells as lymphoblastoid without retraining, whereas models trained on raw modification patterns fail to transfer. FiberBrowser thus provides a unified, expandable analytical framework for single-molecule chromatin footprinting.