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https://github.com/csoneson/armor
Light-weight Snakemake workflow for preprocessing and statistical analysis of RNA-seq data
https://github.com/csoneson/armor
rna-seq workflow
Last synced: 4 days ago
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Light-weight Snakemake workflow for preprocessing and statistical analysis of RNA-seq data
- Host: GitHub
- URL: https://github.com/csoneson/armor
- Owner: csoneson
- License: mit
- Created: 2017-09-15T10:02:41.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-12-06T10:42:10.000Z (about 2 months ago)
- Last Synced: 2024-12-13T18:10:32.268Z (about 1 month ago)
- Topics: rna-seq, workflow
- Language: R
- Homepage:
- Size: 28.9 MB
- Stars: 161
- Watchers: 16
- Forks: 32
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS
- License: LICENSE
Awesome Lists containing this project
README
# ARMOR workflow
[![snakemake-run](https://github.com/csoneson/ARMOR/actions/workflows/snakemake-run.yaml/badge.svg)](https://github.com/csoneson/ARMOR/actions)**ARMOR** (**A**utomated **R**eproducible **MO**dular **R**NA-seq) is a [Snakemake workflow](https://snakemake.readthedocs.io/en/stable/index.html), aimed at performing a typical RNA-seq workflow in a reproducible, automated, and partially contained manner. It is implemented such that alternative or similar analysis can be added or removed.
ARMOR consists of a `Snakefile`, a [`conda`](https://conda.io/docs/) environment file (`envs/environment.yaml`) a configuration file (`config.yaml`) and a set of `R` scripts, to perform quality control, preprocessing and differential expression analysis of RNA-seq data. The output can be combined with the [`iSEE`](https://bioconductor.org/packages/iSEE/) `R` package to generate a `shiny` application for browsing and sharing the results.
By default, the pipeline performs all the steps shown in the [diagram](img/dag_nice3.png) below. However, you can turn off any combination of the light-colored steps (e.g `STAR` alignment or `DRIMSeq` analysis) in the `config.yaml` file.
*Advanced use*: If you prefer other software to run one of the outlined steps (e.g. `DESeq2` over `edgeR`, or `kallisto` over `Salmon`), you can use the software of your preference provided you have your own script(s), and change some lines within the `Snakefile`. If you think your "custom rule" might be of use to a broader audience, let us know by opening an issue.
## Using the ARMOR workflow
Assuming that snakemake and conda are installed (and your system has the necessary libraries to compile R packages), you can use the following commands on a test dataset:
```
git clone https://github.com/csoneson/ARMOR.git
cd ARMOR && snakemake --use-conda
```To use the ARMOR workflow on your own data, follow the steps outlined in the [wiki](https://github.com/csoneson/ARMOR/wiki).
## Workflow graph
![DAG](img/dag_nice5.png)
Blue circles are rules run in `R`, orange circles from software called as shell commands. Dashed lines and light-colored circles are optional rules, controlled in `config.yaml`## Contributors
Current contributors include:- [Ruizhu Huang](https://github.com/fionarhuang)
- [Katharina Hembach](https://github.com/khembach)
- [Stephany Orjuela](https://github.com/sorjuela)
- [Mark D. Robinson](https://github.com/markrobinsonuzh)
- [Charlotte Soneson](https://github.com/csoneson)