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https://github.com/guigolab/ggsashimi
Command-line tool for the visualization of splicing events across multiple samples
https://github.com/guigolab/ggsashimi
Last synced: 4 months ago
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Command-line tool for the visualization of splicing events across multiple samples
- Host: GitHub
- URL: https://github.com/guigolab/ggsashimi
- Owner: guigolab
- License: mit
- Created: 2017-06-14T10:28:59.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2023-08-22T18:37:48.000Z (11 months ago)
- Last Synced: 2024-01-17T00:55:14.409Z (6 months ago)
- Language: Python
- Size: 10.7 MB
- Stars: 107
- Watchers: 10
- Forks: 38
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- repo-5916-awesome-genome-visualization - GGsashimi - genome-visualization/ggsashimi.png) (Static)
- awesome-genome-visualization - GGsashimi
README
# ggsashimi
[](https://github.com/guigolab/ggsashimi/actions)
Command-line tool for the visualization of splicing events across multiple samples
**[Installation](#installation)**
**[Dependencies](#dependencies)**
**[Download docker image](#download-docker-image)**
**[Build docker image](#build-docker-image)**
**[Use docker image](#use-docker-image)**
**[Usage](#usage)**
**[Galaxy](#galaxy)**
**[Cite ggsashimi](#cite-ggsashimi)**
The `ggsashimi` script can be directly downloaded from this repository:
```shell
wget https://raw.githubusercontent.com/guigolab/ggsashimi/master/ggsashimi.py
```
Change the execution permissions:
```shell
chmod u+x ggsashimi.py
```
Provided all dependencies are already installed (see below), you can directly execute the script:
```shell
./ggsashimi.py --help
```
To download the entire repository, which includes the dockerfile and example files:
```shell
git clone https://github.com/guigolab/ggsashimi.git
```
In order to run `ggsashimi` the following software components and packages are required:
- python (2.7 or 3)
- pysam (>=0.10.0)
- R (>=3.3)
- ggplot2 (>=2.2.1)
- data.table (>=1.10.4)
- gridExtra (>=2.2.1)
Additional required R packages `grid` and `gtable` should be automatically installed when installing R and `ggplot2`, respectively. Package `svglite` (>=1.2.1) is also required when generating output images in SVG format.
To avoid dependencies issues, the script is also available through a docker image.
A public `ggsashimi` Docker image is available in the [Docker Hub](https://hub.docker.com/r/guigolab/ggsashimi/) and can be downloaded as follows:
```shell
docker pull guigolab/ggsashimi
```
__Alternatively__, we provide the Dockerfile if you want to build your local docker image, although most users will not need it.
### Build docker image (optional)
After downloading the repository, move inside the repository folder:
```shell
cd ggsashimi
```
To build the docker image run the following command:
```shell
docker build -f docker/Dockerfile -t guigolab/ggsashimi .
```
This can take several minutes.
Once the image is downloaded or built, to execute ggsashimi with docker:
```shell
docker run guigolab/ggsashimi --help
```
Because the image is used in a docker container which has its own file system, to use the program with local files, a host data volume needs to be mounted.
As an example, you can run this command from the main repository folder:
```shell
docker run -w $PWD -v $PWD:$PWD guigolab/ggsashimi -b examples/input_bams.tsv -c chr10:27040584-27048100
```
The '-w' option sets the working directory inside the container to the current directory.
The '-v' option mounts the current working directory and all child folders inside the container to the same path (host_path:container_path).
If your files are in another folder, for example the annotation file is stored in a different folder then the one containing the bam file, you can mount extra folders like this:
```shell
f="$DIR/annotation.gtf"
docker run -w $PWD -v $PWD:$PWD -v $DIR:$DIR guigolab/ggsashimi -b examples/input_bams.tsv -c chr10:27040584-27048100 -g $f
```
You can even mount a single file:
```shell
docker run -w $PWD -v $PWD:$PWD -v $f:$f guigolab/ggsashimi -b examples/input_bams.tsv -c chr10:27040584-27048100 -g $f
```
Execute the script with `--help` option for a complete list of options.
Sample data and usage examples can be found at `examples`
### Debug mode
Debug mode allows to run `ggsashimi` without producing any graphical output. It creates an `Rscript` file in the current working folder, containing all the R commands to generate the plot. It can be useful when reporting bugs or trying to debug the program behavior. Debug mode can be enabled by setting the environment variable `GGSASHIMI_DEBUG` when running the script, e.g.:
```
# export the environment variable
$ export GGSASHIMI_DEBUG=yes
$ ggsashimi.py -b ...
```
or
```
# set the environment variable inline
$ GGSASHIMI_DEBUG=yes ggsashimi.py -b ...
```
Thanks to [ARTbio](https://github.com/ARTbio), now a [Galaxy](https://galaxyproject.org) wrapper for `ggsashimi` is available at the [Galaxy ToolShed](https://toolshed.g2.bx.psu.edu/repository?repository_id=397283a49b821a79&changeset_revision=64aa67b5099f).
If you find `ggsashimi` useful in your research please cite the related publication:
[Garrido-Martín, D., Palumbo, E., Guigó, R., & Breschi, A. (2018). ggsashimi: Sashimi plot revised for browser-and annotation-independent splicing visualization. _PLoS computational biology, 14_(8), e1006360.](https://doi.org/10.1371/journal.pcbi.1006360)