gimmemotifs
Suite of motif tools, including a motif prediction pipeline for ChIP-seq experiments. See full GimmeMotifs documentation for detailed installation instructions and usage examples.
Science Score: 77.0%
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Low similarity (14.4%) to scientific vocabulary
Repository
Suite of motif tools, including a motif prediction pipeline for ChIP-seq experiments. See full GimmeMotifs documentation for detailed installation instructions and usage examples.
Basic Info
- Host: GitHub
- Owner: vanheeringen-lab
- License: mit
- Language: Python
- Default Branch: master
- Homepage: https://gimmemotifs.readthedocs.io/en/master
- Size: 382 MB
Statistics
- Stars: 120
- Watchers: 5
- Forks: 35
- Open Issues: 62
- Releases: 35
Metadata Files
README.md
GimmeMotifs
Suite of motif tools, including a motif prediction pipeline for ChIP-seq experiments.
See full GimmeMotifs documentation for detailed installation instructions and usage examples.
For documentation on the development version see here.
The manuscript describing this latest release is available on biorRxiv as a preprint and can be cited as:
GimmeMotifs: an analysis framework for transcription factor motif analysis
Niklas Bruse, Simon J. van Heeringen
bioRxiv (2018) DOI: 10.1101/474403
You can interactively try out the Python API in a Jupyter notebook using binder: 
We need your help!
GimmeMotifs was originally developed for our own needs but we would really like it to be useful to the wider community. However, this also depends on your input. Let us know what you think! What features are missing? Which tutorial would you like to see? What part of the documentation is unclear? Have great ideas for future developments? Maybe you even want to join in developing this software?
Easy installation
The most straightforward way to install GimmeMotifs is via conda using the bioconda channel.
If you have not used bioconda before, first set up the necessary channels (in this order!). You only have to do this once.
$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forge
You can now install GimmeMotifs with one command:
```
Create an environment called gimme with all dependencies
$ conda create -n gimme python=3 gimmemotifs
Activate the environment
$ conda activate gimme ```
Python 3 is the required, from version 0.13.0 on GimmeMotifs no longer supports Python 2.
Don't forget to activate the environment with conda activate gimme whenever you want to use GimmeMotifs.
Quick start
Predict some de novo motifs:
$ gimme motifs my_peaks.bed my_motifs -g /data/genomes/hg38/hg38.fa --denovo
Download a genome
The example above assumes that you have the hg38 genome in
/data/genomes/hg38/hg38.fa.
GimmeMotifs can also use genomes installed by
genomepy.
You can configure the directory where genomepy stores genomes by editing
~/.config/genomepy/genomepy.yaml
genome_dir: /data/genomes
To download a genome from UCSC:
$ genomepy install hg38 --annotation # genomepy >=0.9.0
Now you can specify this genome for GimmeMotifs by name.
$ gimme motifs my_peaks.bed -g hg38 -n my_motifs
Help
- Full documentation: http://gimmemotifs.readthedocs.io/.
- Check the FAQ for common issues.
- The preferred way to get support is through the Github issues page
- Finally, you can reach me by mail or Twitter.
Owner
- Name: vanheeringen-lab
- Login: vanheeringen-lab
- Kind: organization
- Email: s.vanheeringen@science.ru.nl
- Location: Nijmegen, the Netherlands
- Repositories: 23
- Profile: https://github.com/vanheeringen-lab
Repository of the group of Simon van Heeringen @ Radboud University
Citation (CITATION.cff)
# YAML 1.2
---
message: "If you use this software, please cite it using these metadata."
cff-version: "1.1.0"
abstract: |
"Background: Transcription factors (TFs) bind to specific DNA sequences, TF motifs, in cis-regulatory sequences and control the expression of the diverse transcriptional programs encoded in the genome. The concerted action of TFs within the chromatin context enables precise temporal and spatial expression patterns. To understand how TFs control gene expression it is essential to model TF binding. TF motif information can help to interpret the exact role of individual regulatory elements, for instance to predict the functional impact of non-coding variants.
Findings: Here we present GimmeMotifs, a comprehensive computational framework for TF motif analysis. Compared to the previously published version, this release adds a whole range of new functionality and analysis methods. It now includes tools for de novo motif discovery, motif scanning and sequence analysis, motif clustering, calculation of performance metrics and visualization. Included with GimmeMotifs is a non-redundant database of clustered motifs. Compared to other motif databases, this collection of motifs shows competitive performance in discriminating bound from unbound sequences. Using our de novo motif discovery pipeline we find large differences in performance between de novo motif finders on ChIP-seq data. Using an ensemble method such as implemented in GimmeMotifs will generally result in improved motif identification compared to a single motif finder. Finally, we demonstrate maelstrom, a new ensemble method that enables comparative analysis of TF motifs between multiple high-throughput sequencing experiments, such as ChIP-seq or ATAC-seq. Using a collection of ~200 H3K27ac ChIP-seq data sets we identify TFs that play a role in hematopoietic differentiation and lineage commitment.
Conclusion: GimmeMotifs is a fully-featured and flexible framework for TF motif analysis. It contains both command-line tools as well as a Python API and is freely available at: https://github.com/vanheeringen-lab/gimmemotifs."
authors:
-
family-names: Bruse
given-names: Niklas
affiliation: "Radboud University, Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, 6500 HB Nijmegen, The Netherlands "
-
family-names: "van Heeringen"
given-names: "Simon Jan"
orcid: "https://orcid.org/0000-0002-0411-3219"
affiliation: "Radboud University, Faculty of Science, Department of Molecular Developmental Biology, Radboud Institute for Molecular Life Sciences, 6500 HB Nijmegen, The Netherlands "
date-released: 2019-11-18
doi: "10.1101/474403"
license: MIT
repository-code: "https://github.org/vanheeringen-lab/gimmemotifs/"
title: "GimmeMotifs: an analysis framework for transcription factor motif analysis"
version: "0.17.2"
...
GitHub Events
Total
- Issues event: 2
- Watch event: 12
- Pull request event: 1
- Fork event: 1
Last Year
- Issues event: 2
- Watch event: 12
- Pull request event: 1
- Fork event: 1
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Simon van Heeringen | s****n@g****m | 1,581 |
| siebrenf | s****f@g****m | 103 |
| Maarten-vd-Sande | m****e@h****m | 56 |
| Simon van Heeringen | s****n@n****l | 8 |
| Uri Laserson | u****n@g****m | 4 |
| JGASmits | j****3@h****m | 3 |
| Aaron Statham | a****m@g****m | 3 |
| Øyvind Almelid | o****d@i****k | 2 |
| akmorrow13 | a****w@b****u | 1 |
| jsmits | j****s@m****l | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 78
- Total pull requests: 39
- Average time to close issues: over 1 year
- Average time to close pull requests: 25 days
- Total issue authors: 44
- Total pull request authors: 8
- Average comments per issue: 2.78
- Average comments per pull request: 0.36
- Merged pull requests: 30
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 2
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- siebrenf (12)
- JGASmits (5)
- fgualdr (4)
- shaunmahony (3)
- Mitmischer (3)
- apposada (2)
- irzhegalova (2)
- shangguandong1996 (2)
- laserson (2)
- plachta11b (2)
- gfudenberg (2)
- Francis3209 (2)
- oalmelid (2)
- lynnyummy (2)
- roskamsh (2)
Pull Request Authors
- siebrenf (22)
- Maarten-vd-Sande (7)
- simonvh (3)
- JGASmits (1)
- oalmelid (1)
- cameronraysmith (1)
- BuysDB (1)
- VonAlphaBisZulu (1)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 441 last-month
- Total dependent packages: 3
- Total dependent repositories: 6
- Total versions: 26
- Total maintainers: 2
pypi.org: gimmemotifs
GimmeMotifs is a motif prediction pipeline.
- Homepage: https://github.com/vanheeringen-lab/gimmemotifs/
- Documentation: https://gimmemotifs.readthedocs.io/
- License: MIT
-
Latest release: 0.18.0
published about 3 years ago
Rankings
Dependencies
- genomepy
- gimmemotifs >=0.13.0
- python 3.*
- biofluff *
- configparser *
- copied *
- diskcache *
- feather-format *
- genomepy *
- ipywidgets *
- iteround *
- jinja2 *
- logomaker *
- loguru *
- matplotlib *
- numpy *
- pandas *
- pyarrow *
- pybedtools *
- pysam *
- python *
- qnorm *
- scikit-learn *
- scipy *
- seaborn *
- setuptools *
- statsmodels *
- tqdm *
- xdg *
- xgboost *
- xxhash *