initialized RiboFlow

.gitignore

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+*.class
+
+# Mobile Tools for Java (J2ME)
+.mtj.tmp/
+
+# Package Files #
+*.jar
+*.war
+*.ear
+
+# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml
+hs_err_pid*
+
+# NextFlow Specific
+.nextflow.log*
+.nextflow
+
+# Docker Specific
+*apt.list
+*conda.list

.zenodo.json

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+
+{
+    "creators": [ 
+        {
+            "name": "Ozadam, Hakan", 
+            "affiliation": "UT, Austin, TX, USA"
+        },
+        {
+            "name": "Cenik, Can",
+            "affiliation": "UT, Austin, TX, USA"
+        }
+    ], 
+    "keywords": [
+        "bioinformatics",
+        "genomics",
+        "ribosome",
+        "ribo-seq",
+        "Python"
+    ],
+    "description": "<p>RiboFlow is a NextFlow based pipeline for processing ribosome profiling data.</p>",
+    "access_right": "open",
+    "license": "MIT",
+    "upload_type": "software"
+}

README.md

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+# RiboFlow
+
+RiboFlow is a [Nextflow](https://www.nextflow.io/) based pipeline 
+for processing ribosome profiling data.
+
+## Installation
+
+### Requirements
+
+* [Nextflow](https://www.nextflow.io/)
+* [Docker](https://docs.docker.com/install/) (Optional) 
+* [Conda](https://conda.io/en/latest/miniconda.html) (Optional)
+
+First, follow the instructions in [Nextflow website](https://www.nextflow.io/) and install Nextflow. 
+
+The easiest way of using RiboFLow is using Docker.
+If using Docker is not an option, you can install the dependencies using Conda
+and run RiboFlow without Docker. 
+
+### Docker Option
+
+Install [Docker](https://docs.docker.com/install/). 
+Here is a [tutorial for Ubuntu.](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-on-ubuntu-18-04)
+
+All remaining dependencies come in the Docker image [ceniklab/riboflow](https://hub.docker.com/r/ceniklab/riboflow).
+This image is automatically pulled by RiboFlow when run with Docker (see test runs below). 
+
+### Conda Option
+
+This option has been tested on Linux systems only.
+
+Install  [Conda](https://conda.io/en/latest/miniconda.html). 
+
+All other dependencies can be installed using the environment file,
+environment.yaml, in this repository.
+```
+git clone https://github.com/ribosomeprofiling/riboflow.git
+conda env create -f riboflow/environment.yaml
+```
+
+The above command will create a conda environment called _ribo_
+and install dependencies in it.
+To start using RiboFlow, you need to activate the _ribo_ environment.
+
+`conda activate ribo`
+
+## Test Run
+
+For fresh installations, before running RiboFlow on actual data,
+it is recommended to do a test run.
+
+Clone this repository in a new folder and change your working directory to the RiboFlow folder. 
+```
+mkdir rf_test_run && cd rf_test_run
+git clone https://github.com/ribosomeprofiling/riboflow.git
+cd riboflow
+```
+
+Obtain a copy of the sample data in the working directory.
+```
+git clone https://github.com/ribosomeprofiling/rf_sample_data.git
+```
+
+### Run Using Docker
+
+Provide the argument `-profile docker_local` to Nextflow to indicate Docker use. 
+
+`nextflow RiboFlow.groovy -params-file project.yaml -profile docker_local`
+
+### Run Using Conda Environment
+
+Make sure that you have created the conda environment, called _ribo_,
+using the instructions above. Then activate the conda environment.
+
+`conda activate ribo` 
+
+If the above command fails to activate the ribo environment, try
+`source activate ribo`
+
+Now RiboFlow is ready to run.
+
+`nextflow RiboFlow.groovy -params-file project.yaml`
+
+## Output
+
+Pipeline run may take several minutes.
+When finished, the resulting files are in the `./output` folder.
+
+Mapping statistics are compiled in a csv file called `stats.csv` 
+
+```
+ls output/stats/stats.csv
+```
+
+Ribosome occupancy data is in a single 
+[ribo file](https://ribopy.readthedocs.io/en/latest/ribo_file_format.html) called `all.ribo`.
+
+`ls output/ribo/all.ribo`
+
+You can use 
+[RiboR](https://github.com/ribosomeprofiling/ribor) or
+[RiboPy](https://github.com/ribosomeprofiling/ribopy) to work with ribo files.
+
+
+## Actual Run
+
+For running RiboFlow on actual data, files must be organized and a parameters file must be prepared.
+You can examine the sample run above to see an example.
+
+1. Organize your data. The following files are required for RiboFlow
+* **Ribosome profiling sequencing data:** in gzipped fastq files 
+* **Transcriptome Reference:** Bowtie2 index files
+* **Filter Reference:** Bowtie2 index files (typically for rRNA sequences)
+* **Annotation:** A bed file defining CDS, UTR5 and UTR3 regions.
+* **Transcript Lengths:** A two column tsv file containing transcript lengths
+
+2. Prepare a custom `project.yaml` file. 
+You can use the sample file `project.yaml`, provided in this repository,
+as template.
+
+3. In `project.yaml`, provide RiboFlow parameters such as `clip_arguments`, alignment arguments etc.
+You can simply modify the arguments in the sample file `project.yaml` in this repository.
+
+4. You can adjust the hardware and computing environment settings in Nextflow configuration file(s).
+For Docker option, see `configs/docker_local.config`. If you are not using Docker,
+see `configs/local.config`.
+
+5. RNA-Seq data is optional for RiboFlow. So, if you do NOT have RNA-Seq data, in the project file, set
+
+`do_rnaseq: false`
+
+If you have RNA-Seq data to be paired with ribosome profiling data, see the __Advanced Features__ below.
+
+
+6. Metadata is optional for RiboFlow.. If you do NOT have metadata, in the project file, set
+
+`do_metadata: false`
+
+If you have metadata, see Advanced feature below.
+
+7. Run RiboFlow using the new parameters file `project.yaml`.
+
+Using Docker:
+
+`nextflow RiboFlow.groovy -params-file project.yaml -profile docker_local`
+
+Without Docker:
+
+`nextflow RiboFlow.groovy -params-file project.yaml`
+
+## Advanced Features
+
+### RNA-Seq Data
+
+If you have RNA-Seq data that you want to pair with ribosome profiling experiments,
+provide the paths of the RNA-Seq (gzipped) fastq files  in the configuration file in
+_input -> metadata_. See the file `project.yaml` in this repository for an example.
+Note that the names in defining RNA-Seq files must match the names in definig ribosome profiling data.
+Also turn set the do_rnaseq flag to true, in the project file:
+
+`do_rnaseq: true`
+
+Transcript abundance data will be stored in 
+
+### Metadata
+
+If you have metadata files for the ribosome profiling experiments,
+provide the paths of the metadata files (in yaml format) in the configuration file in
+_input -> metadata_. See the file `project.yaml` in this repository for an example.
+Note that the names in defining metadata files must match the names in definig ribosome profiling data.
+Also turn set the metadata flag to true, in the project file:
+
+`do_metadata: true`
+
+Metadata will be stored in the output ribo file.
+