Harmonizing single cell 3D genome data with STARK and scNucleome

Single-cell three-dimensional genome sequencing (sc3DG-seq) is advancing our understanding of genome regulation and cellular heterogeneity in diverse biological processes. Despite significant technological advancements, a universal tool capable of processing sc3DG-seq data and benchmarking the performance of various techniques in resolving 3D chromatin structures is absent. To fill this gap, we present STARK, a versatile toolkit designed for the preprocessing, quality control and analysis of all spectrum of sc3DG-seq data. Utilizing STARK, we systematically processed 11 sc3DG-seq technologies data, enabling a quantitative benchmarking of each technologys strengths and limitations. Additionally, we developed the EmptyCells algorithm to distinguish high quality from empty barcodes, and introduced the Spatial Structure Capture Efficiency (SSCE) metric to assess the ability of single cells to capture chromatin structures. Furthermore, we established scNucleome, an extensive repository of uniformly processed sc3DG-seq datasets, offering a foundational resource to catalyze further exploration and discovery in the 3D genome research.