A Software Ecosystem for Ribosome Profiling Data

Ribo Ecosystem is a set of software tools, built around the ribo file format, to facilitate analyzing ribosome profiling data at ribosome protected footprint length resolution. It consists of a pipeline, RiboFlow, and two interfaces RiboR and RiboPy.

Steps for Data Analysis

Please follow these steps to process your ribosome profiling experiment(s):

1. Have Your Data Ready: Have your sequencing data ready in gzipped fastq format.

2. Prepare Your Parameters File: RiboFlow requires a parameters file. We provide a version that works with human ribosome profiling data here. Please modify according to your needs.

3. Generate Ribo Files: Using RiboFlow, you can process your data and generate a ribo file. The structure of the ribo file format is available in RiboPy documentation.

4. Analyze Your Data: There are two alternatives to analyze ribo files. R users can use RiboR. Python or command line users can use RiboPy.

RiboFlow

RiboFlow is a pipeline that processes ribosome profiling data and compiles the results into a ribo file.

• Source code of RiboFlow is available on Github.
• Documentation is available on Github.

Installation and Requirements

You need the following software to run RiboFlow.

• NextFlow
• Docker

If you don’t have Docker, you can use conda to install the required RiboFlow dependencies. See the Github page for instructions.

Quick Start

Make sure that you have NextFlow and Docker installed.

# 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
# Finally, you can run the pipeline.
nextflow RiboFlow.groovy -params-file project.yaml -profile docker_local
# Nextflow will print running times when finished.

The output files, e.g. alignment statistics and the ribo file, are created inside the folder named output.

ls output
ls output/ribo/all.ribo

You can also run RiboFlow without using Docker. See instructions on Github.

RiboPy

RiboPy is a Python and command line interface to read and write ribo files.

• Source code is available on Github.
• Documentation is available on readthedocs.

RiboPy can be installed via pip:

pip install ribopy

RiboPy comes with an application programming interface (API) and a command line interface (CLI). A walk-through of the API is available here. For the CLI, a separate walk-through is available in this link.

RiboR

RiboR is an R interface to read ribo files.

• Source code is available on Github.

RiboR can be installed via devtools. Make sure your R version is 3.6 or higher.

# Run this in an R Environment.

install.packages("devtools")
library("devtools")
install_github("ribosomeprofiling/ribor")

You can continue your analysis by following this walkthrough of RiboR

Citing

RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution, H. Ozadam, M. Geng, C. Cenik Bioinformatics 36 (9), 2929-2931

@article{ozadam2020riboflow,
  title={RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution},
  author={Ozadam, Hakan and Geng, Michael and Cenik, Can},
  journal={Bioinformatics},
  volume={36},
  number={9},
  pages={2929--2931},
  year={2020},
  publisher={Oxford University Press}
}