Motivation: Genome-wide profiling of transcription factor binding and chromatin states is a widely-used approach for mechanistic understanding of gene regulation. Recent technology development has enabled such profiling at single-cell resolution. However, an end-to-end computational pipeline for analyzing such data is still lacking.
Results: Here, we have developed a flexible pipeline for analysis and visualization of single-cell CUT&Tag and CUT&RUN data, which provides functions for sequence alignment, quality control, dimensionality reduction, cell clustering, data aggregation and visualization. Furthermore, it is also seamlessly integrated with the functions in original CUT&RUNTools for population-level analyses. As such, this provides a valuable toolbox for the community.
Availability and implementation: https://github.com/fl-yu/CUT-RUNTools-2.0.
Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author(s) 2021. Published by Oxford University Press.