Metadata-Version: 2.1
Name: ggr-cwl-ipynb-gen
Version: 0.5.0
Summary: IPython notebook generator for GGR CWL processing pipelines of genomic data
Home-page: https://github.com/ReddyLab/ggr-cwl-ipynb-gen
Author: Alejandro Barrera
Author-email: alejandro.barrera@duke.edu
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/ReddyLab/ggr-cwl-ipynb-gen/issues
Project-URL: Source, https://github.com/ReddyLab/ggr-cwl-ipynb-gen/
Keywords: cwl,bioinformatics,development
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.1
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: jinja2 (>=2.8)
Requires-Dist: nbformat (>=4.0.1)
Requires-Dist: numpy (>=1.10.4)
Requires-Dist: pandas (>=0.17.1)
Requires-Dist: xlrd (>=1.0.0)
Requires-Dist: ruamel.yaml (>=0.11.11)
Requires-Dist: pymongo (>=3.4.0)

# ggr-cwl-ipynb-gen
Jupyter notebook generator to download and execute the processing files for GGR related datasets.
At this point, is not intented to cover all use cases, but to serve as a quick generator of all
related files and scripts to pre-process genomic data generated at the [Duke-GCB Sequencing Core](https://genome.duke.edu/cores-and-services/sequencing-and-genomic-technologies) in [HARDAC](https://genome.duke.edu/cores-and-services/computational-solutions/compute-environments-genomics).

Example of usage:
```
$ python ggr_cwl_ipynb_gen.py \
  --root-dir /path/to/rootdir \
  --metadata examples/Hong_3979_170316B1.xlsx \
  --out /path/to/output_dir \
  --force
```
The information in the example metadata and configuration file should reveal what is needed to download and pre-process the samples.

For a full list of options:
```
$ python ggr_cwl_ipynb_gen.py -h
usage: Generator of Jupyter notebooks to execute CWL pre-processing pipelines [-h] -o OUT -m METADATA [-f] [-n] [--metadata-sep SEP] [--project-name PROJECT_NAME]
                                                                              [--data-from {sftp,miseq,other,dukeds,local}] [-c CONF_FILE] [-u USER] [-e USER_DUKE_EMAIL] [-r ROOT_DIR]

optional arguments:
  -h, --help            show this help message and exit
  -o OUT, --out OUT     Jupyter notebook output file name
  -m METADATA, --metadata METADATA
                        Metadata file with samples information
  -f, --force           Force to overwrite output file
  -n, --no-upload       Avoids uploading generated data to database when specified
  --metadata-sep SEP    Separator for metadata file (when different than Excel spread sheet)
  --project-name PROJECT_NAME
                        Project name (by default, basename of metadata file name)
  --data-from {sftp,miseq,other,dukeds,local}
                        Choices: sftp, miseq, other, dukeds, local
  -c CONF_FILE, --conf-file CONF_FILE
                        YAML configuration file (see examples)
  -u USER, --user USER  HARDAC User used in SLURM (default: ${USER})
  -e USER_DUKE_EMAIL, --user-duke-email USER_DUKE_EMAIL
                        Email(s) notified when execution is finished (default: ${USER}@duke.edu)
  -r ROOT_DIR, --root-dir ROOT_DIR
                        Root directory where all subfolders and files will be created (semi-required: either defined here or in conf-file)
```


