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afrubin authored Apr 15, 2017
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25 changes: 25 additions & 0 deletions README.md
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simdms
======

simdms is a script for simulating deep mutational scanning data. These datasets are intended to be analyzed using [Enrich2](https://github.com/FowlerLab/Enrich2/). For more information or to cite simdms, please refer to [Enrich2: a statistical framework for analyzing deep mutational scanning data](http://biorxiv.org).

Dependencies
------------

simdms runs on Python 2.7 and requires the following packages:

* [NumPy](http://www.numpy.org/) version 1.10.4 or higher
* [SciPy](http://www.scipy.org/) version 0.16.0 or higher
* [pandas](http://pandas.pydata.org/) version 0.18.0 or higher
* [PyTables](http://www.pytables.org/) version 3.2.0 or higher

We recommend using a scientific Python distribution such as [Anaconda](https://store.continuum.io/cshop/anaconda/) or [Enthought Canopy](https://www.enthought.com/products/canopy/) to install and manage dependencies. PyTables may not be installed when using the default settings for your distribution. If you encounter errors, check that the `tables` module is present.

To use simdms, git clone or download the repository and run `python simdms.py config.json` from its root directory. simdms takes a json-formatted configuration file as the command line argument.

Questions?
----------

Please use the [GitHub Issue Tracker](https://github.com/FowlerLab/simdms/issues) to file bug reports or request features.

simdms was written by [Alan F Rubin](mailto:alan.rubin@wehi.edu.au) and Hannah Gelman.
42 changes: 42 additions & 0 deletions config.json
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{
"binding": {
"max effect": 1.0,
"min effect": 0.05,
"sigma": 0.1,
"wt effect": 0.5,
"wt pctile": 75
},
"experiment": {
"depths": [
200
],
"replicates": 5,
"rounds": 6
},
"growth": {
"max effect": 5.0,
"min effect": 0.05,
"sigma": 0.1,
"wt doublings": 2,
"wt effect": 1.0,
"wt pctile": 75
},
"noise": {
"jackpot": {
"mult high": 50,
"mult low": 50,
"pct high": 5,
"pct low": 5
},
"reperror": {
"freq": 0.1
}
},
"outdir": ".",
"population": {
"size": 10000000,
"starting counts sigma": 0.4,
"starting wt freq": 0.01,
"variants": 10000
}
}
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