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PyMotif-x

Biological sequence motif discovery in Python

Introdcution

PyMotif-x is a useable implementation the motif-x tool in the Python programming language. Motif-x is a software tool designed to extract overrepresented patterns from any sequence data set. The algorithm is an iterative strategy which builds successive motifs through comparison to a dynamic statistical background. For more information please refer to the motif-x resouce. The current implementation only supports sequences with a fixed length (i.e pre-aligned) and have a fixed central residue. For example, post-translational modification sites.

Installation

PyMotif-x can be directly installed from github

git clone https://github.com/dengzq1234/PyMotif-x.git

Usage

The script contains the function motifx is the main function. Before you start a run, you will need a foreground and background dataset of sequences. The sample dataset are extracted from another re-implementation, rmotifx. If you are a R user, please refer to rmotifx resource and its research.

The package has attached sample data for test run: fg-data-test.txt bg-data-test.txt

Here, the foreground data represents phosphorylation binding sites of Casein Kinase 2. The negative data represents 10,000 random serine-centered sites.

To start the progrom, run the following in bash command:

python motifx-functions.py <foreground_filename> <background_filename> <central_residue> <minimum_ocurrences> <p_value_cutoff> <verbose> <perl_minimum_activation>

running on example dataset:

python motifx-functions.py fg-data-test.txt bg-data-test.txt S 20 1e-6 False False

The resutls returned should have the following formats:

   motif_score      motif_regex  fg_matches  fg_size  bg_matches  bg_size  fold_increase
0    31.909180  .......SD.E....          57      399          23     6039      37.509317
1    26.679736  .......S..EE...          37      342          37     6016      17.590643
2    31.909180  .......SD.D....          39      305          12     5979      63.710656
3    23.062438  .......SE.E....          24      266          32     5967      16.824248
4    15.954590  .......S..E....          56      242         325     5935       4.225811
5    24.168438  .......SE.D....          21      186          26     5610      24.361042
6    10.915342  .......S..D....          30      165         233     5584       4.357394
7     9.371511  .......SD......          25      135         224     5351       4.423776
8     7.270142  .......S.E.....          25      110         342     5127       3.407097

For detailed explanations of all parameters and output, check out the documentation by typing man motifx. You can also refer to the original motif-x resource. If you want to check its usage in R, please check:

Wagih O, Sugiyama N, Ishihama Y, Beltrao P. (2015) Uncovering phosphorylation-based specificities through functional interaction networks (2015). Mol. Cell. Proteomics PUBMED

Further challenges

  • Allow motif discovery in non-centered k-mers
  • Support more background proteome data
  • Include statistic estimation

Feedback

If you have any feedback or suggestions, please left me a message at dengziqi1234(at)gmail.com) or open an issue on github.