R script for fast and streamlined Radiality Calculation from GPSeq data. GPSeq-RadiCal aims to provide the same functionalities as the gpseqc Python3 package, but with much lower memory usage and faster computation times.
- Tested on R v3.6.3.
- Required packages: argparser, data.table, logging, outliers, pbapply, rtracklayer.
The script checks automatically for the required packages.
When packages are missing, it provides the code to install the missing ones, one by one.
First, prepare a tabulation-separated metadata file with four columns:
exid: sequencing run ID.cond: condition description (e.g., time and/or concentration).libid: library ID.fpath: full absolute path to bed file. Bed files can be gzipped (recommended).
The bed files should be reported in order of condition strength, with the top rows being weaker than bottom ones. Also, the first line should contain the column headers. An example metadata file can be found here.
Then, to run with default parameters, execute the following command:
./gpseq-radical.R example_meta.tsv output_folder
Replacing example_meta.tsv with your metadata file path, and output_folder with the path where the script should write the output. Note that the specified output folder must not already exist.
To access the script help page, run the following:
./gpseq-radical.R -h
- Allows for normalization in a chromosome-wise fashion too, instead of only library-wise.
- Normalization factors are calculated after outlier removal, not before.
- Masking is performed before binning, but after the normalization factors are calculated. In this way, reads from masked regions still count for normalization purposes, while they are masked out from chromosome-wide analysis.
- Re-running on the same output folder to populate with additional resolutions.
- Additional centrality estimates (currently calculates only the one selected in the GPSeq study).
We welcome any contributions to GPSeq-RadiCal. Please, refer to the contribution guidelines if this is your first time contributing! Also, check out our code of conduct.
MIT License
Copyright (c) 2020 Gabriele Girelli
- Girelli, Gabriele, et al. "GPSeq reveals the radial organization of chromatin in the cell nucleus." Nature Biotechnology (2020): 1-10. Genomic loci Positioning by Sequencing (GPSeq) original paper.