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Python bindings for the TFM-Pvalue program.
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README.rst

pytfmpval

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This Python package serves as a wrapper for the incredibly useful TFM-Pvalue C++ program. It allows users to determine score thresholds for a given transcription factor position frequency matrix associated with a specific p-value. Naturally, it can also perform the reverse, quickly calculating an accurate p-value from a score for a given motif matrix.

pytfmpval allows this functionality to be easily utilized within a Python script, module, or package.

See full documentation and use examples here.

Installation

pytfmpval is on PyPI, so you can install via pip easily:

pip install pytfmpval

A Simple Example

JASPAR is a very highly-touted transcription factor motif database from which motif count matrices can be downloaded for a large variety of organisms and transcription factors. There exist numerous other motif databases as well (TRANSFAC, CIS-BP, MEME, HOMER, WORMBASE, etc), most of which use a relatively similar format for their motifs. Typically, a motif file consists of four rows or columns with each position in a given row or column corresponding to a base within the motif. Sometimes there is a comment line started with >. The row or column order is always A, C, G, T. In this example, the motif consists of four rows corresponding to the 16 positions of the motif with counts for each base at each position.

>>> from pytfmpval import tfmp
>>> m = tfmp.create_matrix("MA0045.pfm")
>>> tfmp.score2pval(m, 8.7737)
9.992625564336777e-06
>>> tfmp.pval2score(m, 0.00001)
8.773708000000001

This could also be done by creating a string for the matrix by concatenating the rows (or columns) and using the read_matrix() function. This method is usually easier, as allows the user to parse the motif file as necessary to ensure a proper input. It's also more fitting for high-throughput.

>>> from pytfmpval import tfmp
>>> mat = (" 3  7  9  3 11 11 11  3  4  3  8  8  9  9 11  2"
...        " 5  0  1  6  0  0  0  3  1  4  5  1  0  5  0  7"
...        " 4  3  1  4  3  2  2  2  8  6  1  4  2  0  3  0"
...        " 2  4  3  1  0  1  1  6  1  1  0  1  3  0  0  5"
...       )
>>> m = tfmp.read_matrix(mat)
>>> tfmp.pval2score(m, 0.00001)
8.773708000000001
>>> tfmp.score2pval(m, 8.7737)
9.992625564336777e-06

Contribute

Any and all contributions are welcome. Bug reporting via the Issue Tracker is much appeciated. Here's how to contribute:

  1. Fork the pytfmpval repository on github (see forking help).
  2. Make your changes/fixes/improvements locally.
  3. Optional, but much-appreciated: write some tests for your changes. (Don't worry about integrating your tests into the test framework - writing some in your commit comments or providing a test script is fine. I will integrate them later.)
  4. Send a pull request (see pull request help).

Reference

Efficient and accurate P-value computation for Position Weight Matrices
H. Touzet and J.S. Varré
Algorithms for Molecular Biology 2007, 2:15

License

This project is licensed under the GPL3 license. You are free to use, modify, and distribute it as you see fit. The program is provided as is, with no guarantees.

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