https://github.com/kchu25/motifs.jl
DNA Motif discovery that includes the discovery of flexible (long or gapped) motifs.
https://github.com/kchu25/motifs.jl
bioinformatics dna-sequences machine-learning-algorithms motifs representation-learning transcription-factor-binding-site
Last synced: 8 months ago
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DNA Motif discovery that includes the discovery of flexible (long or gapped) motifs.
- Host: GitHub
- URL: https://github.com/kchu25/motifs.jl
- Owner: kchu25
- License: mit
- Created: 2023-03-27T18:22:25.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-18T20:23:04.000Z (over 2 years ago)
- Last Synced: 2025-10-21T12:51:45.638Z (8 months ago)
- Topics: bioinformatics, dna-sequences, machine-learning-algorithms, motifs, representation-learning, transcription-factor-binding-site
- Language: Julia
- Homepage: https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email
- Size: 2.4 MB
- Stars: 8
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Finding Motifs Using DNA Images Derived From Sparse Representations
General purpose motif discovery package that includes the discovery of flexible (long or gapped) motifs.
This code repository corresponds to the paper [Finding Motifs Using DNA Images Derived From Sparse Representations](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email), which has been published in Oxford Bioinformatics.
## Table of contents
- [Motivation](#Motivation)
- [Installation](#Installation)
- [Usage](#Usage)
- [Software requirements](#Software-requirements)
- [Hardware requirements](#Hardware-requirements)
- [Adjustable Hyperparameters](#Adjustable-Hyperparameters)
- [Interpret the results](#Interpret-the-results)
- [Cite this work](#Cite-this-work)
- [Contact](#Contact)
## Motivation
Traditional methods such as [STREME](https://meme-suite.org/meme/doc/streme.html) and [HOMER](http://homer.ucsd.edu/homer/motif/) excel at efficiently finding the primary motifs of a transcription factor. This raises the question: why do we require an additional motif discovery method?
Because there may be more patterns in the datasets that aren't fully captured. This is especially evident for context-dependent binding sites, such as C2H2 zinc finger, and cooperative binding patterns observed in in-vivo datasets from ChIP-Seq.
Our work reveals that over half of the ChIP-Seq datasets selected from the [JASPAR 2022](https://jaspar.genereg.net/) database contain transposable elements that overlap the primary binding sites. For instance, see [NFE2L2](https://en.wikipedia.org/wiki/NFE2L2), [YY1](https://en.wikipedia.org/wiki/YY1), [STAT1](https://en.wikipedia.org/wiki/STAT1), [SRF](https://en.wikipedia.org/wiki/Serum_response_factor), [AR](https://en.wikipedia.org/wiki/Androgen_receptor) ([Manuscript Figure 4](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email)):
These long patterns present challenges for traditional k-mer-based methods due to their exponential time and space complexity.
Furthermore, many datasets exhibit a large presence of gapped motifs. For example, we found that ChIP-Seq datasets from both [JASPAR](https://jaspar.genereg.net/) and [Factorbook](https://www.factorbook.org/) often contains gapped motifs ([Manuscript Figure 6](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email)):
and the spacers that characterized the gapped motifs [can be widely varied (Supplementary Material Figure 2)](./imgs/gaps.png).
Last, there are cooperative binding patterns, e.g., ([Manuscript Figure 5](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email)):
for which we see consecutive occurrences of [Oct4](https://en.wikipedia.org/wiki/Oct-4) and cooccurrence of [Oct4](https://en.wikipedia.org/wiki/Oct-4) and [Zic3](https://en.wikipedia.org/wiki/ZIC3), in addition to the Oct4-Sox2 motif. The presence of gapped motifs and cooperative binding patterns presents challenges for k-mer-based methods as well, as these methods are primarily designed to detect ungapped motifs.
## Installation
To install MOTIFs.jl use Julia's package manager:
```
pkg> add MOTIFs
```
## Usage
In Julia:
````julia
using MOTIFs
# Do motif discovery on a set of DNA sequences in a fasta file,
# where the `` and `` are the
# absolute filepaths as strings.
discover_motifs(, )
# for example
discover_motifs("home/shane/mydata/fasta.fa",
"home/shane/mydata/out/")
````
## Software requirements
This package currectly requires [Weblogo](http://weblogo.threeplusone.com/manual.html#download) for PWM plotting. Install Weblogo by running the following command with python3 and pip3:
```bash
pip install weblogo
```
## Hardware requirements
Currently, a GPU is required for this package as it utilizes [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) to accelerate certain computations. However, I plan to implement a CPU extension in the future.
## Adjustable Hyperparameters
````julia
# The user can adjust the number of epochs for training the network.
discover_motifs(, ; num_epochs=10)
````
## Interpret the results
### Summary page
Once the motif discovery process is complete, a summary.html page is generated in the output folder, providing a comprehensive overview of the results.
For instance, here is an example result page showcasing data from the [SP1 transcription factor from JASPAR](https://jaspar.genereg.net/matrix/MA0079.3/):
> 
The top of the result page has
- **Number of sequences**: The total number of DNA sequences in the dataset.
- **Label**: A label assigned for each discovered motifs.
* Each label is hyperlinked to a text file in TRANSFAC format that can be parsed.
- **P-value**: The satistical significance of the discovered motif using Fisher exact test ([Manuscript section 2.7.2](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email)).
- **\# instances**: An estimate of the number of occurrences in the dataset ([Manuscript section 2.7.3](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email)).
- **Logo**: Position weight matricies.
* Press the `Reverse complement` button to view the logo in alternative orientation.
Note that in in-vivo datasets, especially for zinc-finger proteins, a large number of motifs can be observed, often characterized by variable spacings in their binding sites.
> 
### Statistically insignificant motifs
Some of the motifs shown here have their p-values in grey, indicating that they have a relatively high p-value (p > 0.01, Fisher exact test). This statistical result simply suggests that these motifs are not significantly enriched relative to the shuffled DNA strings ([Manuscript section 2.7.2](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btad378/7192989?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email)); it does not imply that these motifs do not exist in the dataset.
> 
## Cite this work
You can cite this work using the following BibTex entry:
```
@article{chu2023finding,
title={Finding Motifs Using DNA Images Derived From Sparse Representations},
author={Chu, Shane K and Stormo, Gary D},
journal={Bioinformatics},
pages={btad378},
year={2023},
publisher={Oxford University Press}
}
```
## Contact
If you have any questions or suggestions regarding the usage or source code, please feel free to reach out to me at .