This repository is related to:
Impact of chromatin context on Cas9-induced DNA double-strand break repair pathway balance (bioRxiv 2020)
https://www.biorxiv.org/content/10.1101/2020.05.05.078436v1
It has also under revision at Molecular Cell.
Data availability
Raw sequencing data: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA686952
Processed data and markdowns: https://osf.io/cywxd/
Summary
DNA double-strand break (DSB) repair is mediated by multiple pathways. It is thought that the local chromatin context affects the pathway choice, but the underlying principles are poorly understood. Using a newly developed multiplexed reporter assay in combination with Cas9 cutting, we systematically measured the relative activities of three DSB repair pathways as a function of chromatin context in >1,000 genomic locations. This revealed that non-homologous end-joining (NHEJ) is broadly biased towards euchromatin, while the contribution of microhomology-mediated end-joining (MMEJ) is higher in specific heterochromatin contexts. In H3K27me3-marked heterochromatin, inhibition of the H3K27 methyltransferase EZH2 reverts the balance towards NHEJ. Single-stranded template repair (SSTR), often used for precise CRISPR editing, competes with MMEJ, and is moderately linked to chromatin context. These results provide insight into the impact of chromatin on DSB repair pathway balance, and guidance for the design of Cas9-mediated genome editing experiments.
Conda environment
1. Raw data processing
2. Indel & mapping (iPCR) data from the IPRs
It contains three main scripts that process the data that was created by the DSB_trip_snakamake.
- Parsing_QC
- Indel_processing_data
- Chromatin_processing_data
The data that was used for these scripts are available in the processed sequencing data folder on https://osf.io/cywxd/.
These scripts produce multiple RDS output files that are avaible in the R data output for analysis folder on https://osf.io/cywxd/.
3. Mapping IPRs with Tagmeppr
This script uses the tagmeppr package to map the IPRs to hg38. It was recently added because it was part of a mapping effort of multiple other clones. This has been streamlined for the publication. It uses raw demultiplexed sequencing data and puts out a table with the integrations and figures of the mapping (not in the paper).
4. Rearrangement detection with tagmentation
5. ChIP
6. Timeseries
7. Figures
Most figures (except figure 5 and S5 & S2K-M) have been generated using the associated scripts from this git. They require input files generated by the scripts above.