Welcome to the GitHub repository for our work on V(D)J recombination! This repository contains the entire project history as well as curated scripts to ensure computational reproducibility. The following sections will describe this repository in detail. If anything remains unclear, please open an issue and we'll respond as quickly as we can.
This repository contains three unique branches -- master, gh-pages, and
publication (where you are now). The master branch is the primary branch of
the project and contains the entire history of our thought process including
exploratory data analysis and modeling attempts. The gh-pages branch contains
all files that constitute the paper website,
including all of the interactive figures. Finally, the publication branch
contains a curated list of all of the processed data files, final versions of
the figure-generating and analysis Python scripts, the Python software module
vdj, and the MATLAB module tpm. The vdj module is necessary to reproduce
the work in this paper and can be installed locally.
To reproduce this work, you must install the vdj python module -- a homegrown
software package written explicitly for this work. The various components are
described in README.md files within the vdj folder. To install this package
locally, the requirements can be installed via the command line:
pip install -r requirements.txt
The vdj module itself can be installed locally by executing the following
command in the root directory (this directory).
pip install -e ./
Both of these commands can be executed at once by running the install.sh
script in this repostiory,
sh install.sh
in the root directory. When installed, a new folder vdj.egg-info will appear.
Do not delete this folder as it is necessary to access the vdj software.
This repository is broken up into several directories and subdirectories. Please see each directory individually for important notes. Below, we summarize the contents of each folder.
This folder contains all of the exectued Python scripts used to reproduce the findings in this work. These files are broken up further into several subdirectories which separate the scripts by their function.
analysis| Contains all Python scripts which perform data analysis procedures on pre-processed data (i.e., not raw image data). This includes scripts to perform bootstrapping of looping frequencies, generate posterior distributions for the cleavage probability, and parameter inference.figures| Contains all Python scripts which perform data visualization procedures. This includes the generation of all main and supplementary figures.processing| Contains a single scriptcompile_data.pywhich reads through the pre-processed data files, stored as.mat, and extracts the important quantities.interactives| Contains all Python and JavaScript files to generate the interactive figures on the paper website.stan| Contains the inferential model used to estimate the paired-complex leaving rate. This is written in the Stan probabilistic programming language and is not directly executable.
This folder contains the processed data files needed to reproduce the figures in
this work. The pre-processed data for each mutant in each condition can be
downloaded as .mat files from the CaltechDATA
research data repository under DOI: 10.22002/D1.1288. These files, as is described in
code/processing/README.md, are generated by processing the raw image files
using the tpm MATLAB module. The raw image files are very large (~ 10 TB) and
are on cold storage. Image fils are available upon request.
This folder contains .pdf files of all main and supplementary text figures.
The interactive figures are saved here as .html and are stand alone, meaning
that all data is encoded within the file. No code exists in this directory.
This is heart-and-soul of the repository. It contains a series of Python files which define the myriad functions used in the processing, analysis, and visualization of the data associated with this work. Please navigate to the directory to see a description of its contents.
This contains all of the MATLAB .m files used to process the raw image files.
This code was used and described previously in Lovely et al. PNAS 112 (14)
2015 and in the supplementary
material of Johnson et al. Nucleic Acids Research 40 (16)
2012..
All creative works (writing, figures, etc) are licensed under a Creative Commons CC-BY 4.0 license. All software is distributed under the standard MIT license as follows:
Copyright 2019 The Authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.