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https://github.com/omarwagih/rmotifx
Discovery of biological sequence motifs in R
https://github.com/omarwagih/rmotifx
Last synced: 2 months ago
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Discovery of biological sequence motifs in R
- Host: GitHub
- URL: https://github.com/omarwagih/rmotifx
- Owner: omarwagih
- Created: 2014-11-09T01:10:34.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2018-12-20T07:08:25.000Z (about 6 years ago)
- Last Synced: 2024-08-02T13:31:48.290Z (6 months ago)
- Language: R
- Size: 619 KB
- Stars: 26
- Watchers: 3
- Forks: 11
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-proteomics - rnotifx - R - motif enrichment analyssis of PTMs on proteins, probably mostly used for phosphorylation - [paper](https://pubmed.ncbi.nlm.nih.gov/26572964/) (8. Kinase Motif/Activity Analysis / Table of Contents)
README
[](https://github.com/omarwagih/rmotifx)
## Introduction
This package contains a useable implementation the motif-x tool in the R programming language. motif-x (short for motif extractor) 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 original [motif-x resource](http://motif-x.med.harvard.edu/). Please note that the current implementation only supports sequences with a fixed length (i.e. pre-aligned) and have a fixed central residue. For example, phosphorylation sites.
## How to install?
The motif-x R package can be directly installed from github. First, ensure the `devotools` package is installed:
```splus
install.packages('devtools')
```
Then install rmotifx:
```splus
require(devtools)
install_github('omarwagih/rmotifx')
```
## How to use?
To get started, fire up the motif-x package:
```splus
require(rmotifx)
```
The package contains the function `motifx` which does everything. For a simple run, you will need a foreground and background set of sequences.
We can go ahead and use the sample data provided with the package:
```splus
# Get paths to sample files
fg.path = system.file("extdata", "fg-data-ck2.txt", package = "rmotifx")
bg.path = system.file("extdata", "bg-data-serine.txt", package = "rmotifx")
# Read in sequences
fg.seqs = readLines(fg.path)
bg.seqs = readLines(bg.path)
# You can take a look at the format of the sample data
head(fg.seqs)
head(bg.seqs)
```
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 program, run the following:
```splus
mot = motifx(fg.seqs, bg.seqs, central.res = 'S', min.seqs = 20, pval.cutoff = 1e-6)
print(mot)
```
The results returned should have the following format:
```
| motif | score | fg.matches | fg.size | bg.matches | bg.size | fold.increase |
|-----------------|------------------|------------|---------|------------|---------|------------------|
| .......SD.E.... | 615.305311137178 | 57 | 399 | 23 | 6039 | 37.5093167701863 |
| .......S..EE... | 318.377804126939 | 37 | 342 | 37 | 6016 | 17.5906432748538 |
| .......SD.D.... | 615.305311137178 | 39 | 305 | 12 | 5979 | 63.7106557377049 |
| .......SE.E.... | 314.760503514246 | 24 | 266 | 32 | 5967 | 16.8242481203008 |
| .......S..E.... | 307.652655568589 | 56 | 242 | 325 | 5935 | 4.22581055308328 |
| .......SE.D.... | 315.866504156853 | 21 | 186 | 26 | 5610 | 24.3610421836228 |
| .......S..D.... | 10.915342261675 | 30 | 165 | 233 | 5584 | 4.35739367928209 |
| .......SD...... | 9.3715112092424 | 25 | 135 | 224 | 5351 | 4.42377645502645 |
| .......S.E..... | 7.27014238663954 | 25 | 110 | 342 | 5127 | 3.40709728867624 |
```
It's that easy!
For detailed explanations of all parameters and output, check out the documentation by typing `?motifx`. You can also refer to the original motif-x [resource](http://motif-x.med.harvard.edu/motif-x.html) or [paper](http://motif-x.med.harvard.edu/publications/Chou_Schwartz_motif-x_CPBI_2011.pdf).
## Citation
If you use rmotifx please do cite the following paper:
Wagih O, Sugiyama N, Ishihama Y, Beltrao P. (2015) Uncovering phosphorylation-based specificities through functional interaction networks (2015). *Mol. Cell. Proteomics* [PUBMED](http://www.ncbi.nlm.nih.gov/pubmed/26572964)
## Todo
- Add support for degenerate motifs
- Add support for DNA sequences. Currently, only protein supported.
- Allow motif discovery in non-centered k-mers
## Feedback
If you have any feedback or suggestions, please drop me a line at (wagih(at)ebi.ac.uk) or open an issue on github.