2for1separator

2for1 separator is an algorithm to deconvolve CUT&Tag2for1 data. It uses differences in the fragment length distributions of the two targets and the proximity of chromatin cuts to estimate the probability for each cut to originate from one target or the other. The result is a set of cut density tracks that represent the estimated number of cuts induced by the two antibodies used in the CUT&Tag2for1 experiment.

Installation

Using conda

TBA

Using pip

Please make sure python points to a Python 3.9+ interpreter and libcurl is installed. We highly recommed to install install scikit-sparse to significantly reduce memory demand and runtime. Finally, inslatt 2for1separator with

pip install sep241

From source

To install from source you can run:

git clone https://github.com/settylab/2for1separator
cd 2for1separator
pip install .

Usage

Before the deconvolution, the data has to be split up into manageable chunks:

sep241prep [bed files] --out [jobdata pkl file] --memory [max memory target in GB]

It is recommended to use approximately 20 GB or more for --memory. This specifies the targeted memory demand during the subsequent deconvolution.

The output of the function reports the number of separate work chunks and suggests subsequent calls for the deconvolution. The number of slurm jobs is also stored in an additional output file named with [jobdata pkl file].njobs. Note that if memory resources are exhausted, deconvolution jobs may be cancled by slurm or the operating system and subsequent results will be missing. The downstream scripts will look for missing results and report a comma sperated list of respective work chunk numbers. If you are using slurm you can rerun the slurm jobs of only the specified jobs by passing the list with the --array= parameter of the sbatch command.

Exporting Results

If not specified otherwise through the --out argument, all outputs are placed into the same directory with the [jobdata pkl file]. Most outputs contain c1 or c2 in their file name, which stand for channel 1 or channel 2, and represent the two constituent parts of the data that were induced by the two different targets and that were reconstructed through the deconvolution.

To produce bigwig files from the deconvolution results run

sep241mkbw [jobdata pkl file] [chrom sizes file]

The chomosome sizes file needs to have two columns with chromosome name and size in bases (see bigWIG format).

The produced bigWIG files may be used for downstream analysis such as peak calling. To use the 2for1seperator cut-likelihood-based peak calling with overlap identification run:

sep241peakcalling [jobdata pkl file]

Note, that sep241peakcalling does not require the prior conversion to bigWIG but instead uses the raw deconvolution output.

To write out the target specific likelihoods of each genomic cut you can run

sep241events [jobdata pkl file]

For more information pass --help to the respective commands.

Visualization

Output files are bigwigs and bed-files. These can be visualised with software tools such as the IGV Browser or JBrowser 2. Visualization of intermediate results are currently only possible if intermediate function calls within the supplied scripts are reproduced in a python environment.

Citation

Janssens, D.H., Otto, D.J., Meers, M.P. et al. CUT&Tag2for1: a modified method for simultaneous profiling of the accessible and silenced regulome in single cells. Genome Biol 23, 81 (2022). https://doi.org/10.1186/s13059-022-02642-w