RiboFlow is a Nextflow based pipeline for processing ribosome profiling data. As output, it generates ribo files that can be analyzed using RiboR or RiboPy. RiboFlow belongs to a software ecosystem desgined to work with ribosome profiling data.
- Installation
- Test Run
- Output
- RiboFlow on Your Data
- UMIs
- A Note on References
- Advanced Features
- Frequently Asked Questions
- Release Notes
First, follow the instructions in Nextflow website and install Nextflow.
Install Docker. Here is a tutorial for Ubuntu.
All remaining dependencies come in the Docker image hakanozadam/riboflow. This image is automatically pulled by RiboFlow when run with Docker (see test runs below).
This option has been tested on Linux systems only.
Install Conda.
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
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
nextflow RiboFlow.groovy -params-file project.yaml -profile docker_local
Note that we provided the argument -profile docker_local to Nextflow to indicate that RiboFlow will be run via Docker containers. In other words, the steps of RiboFlow will be executed inside Docker containers by Nextflow.
Hence, no locally installed software (other than Java and Nextflow) is needed by RiboFlow.
In Conda option, the steps of RiboFlow are run locally. So, we need to install the dependencies first. This can easily be done via conda. The default profile directs RiboFlow to run locally, so we can simply skip the -profile argument. Also note that the conda environment has to be activated before running RiboFlow.
Before running the commands below, make sure that you have created the conda environment, called ribo, using the instructions above.
# List the environments to make sure that ribo environment exists
conda env list
# Activate the ribo environment
conda activate ribo
# Get RiboFlow repository
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 run RiboFlow
nextflow RiboFlow.groovy -params-file project.yaml
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 called all.ribo.
ls output/ribo/all.ribo
You can use RiboR or RiboPy to work with ribo files.
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.
-
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
-
Prepare a custom
project.yamlfile. You can use the sample fileproject.yaml, provided in this repository, as template. -
In
project.yaml, provide RiboFlow parameters such asclip_arguments, alignment arguments etc. You can simply modify the arguments in the sample fileproject.yamlin this repository. -
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, seeconfigs/local.config. -
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.
- 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 Features below.
- 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
Unique Molecular Identifiers (UMIs) can be ligated to either side of the molecules and they allow labeling molecules uniqely. This way UMIs can be used to deduplicate mapped reads for more accurate quantification.
If there are UMIs in your ribosome profiling data, Riboflow can trim them and deduplicate reads based on UMIs.
RiboFlow extracts UMIs and stores them in the Fastq headers and uses the UMIs in deduplication (instead of position based read collapsing). For this purpose RiboFlow uses umi_tools.
Here we explain the related parts of the project file to be able to use UMIs feature of Riboflow.
Also, we provide a working example of project file in this repository: project_umi.yaml.
The following parameter must be set:
dedup_method: "umi_tools"
Also, users must set the following two parameters: umi_tools_extract_arguments and umi_tools_dedup_arguments.
For example:
umi_tools_extract_arguments: "-p \"^(?P<umi_1>.{12})(?P<discard_1>.{4}).+$\" --extract-method=regex"
umi_tools_dedup_arguments: "--read-length"
The above example takes the first 12 nucleotides from the 5' end, discards the 4 nucleotides downstream and writes the 12 nt UMI sequence to the header. The second parameter tells umi_tools to use read lengths IN ADDITION to UMI sequencing in collapsing reads. Note that these two arguments are directly provided to umi_tools. So users are encouraged to familirize themselves with umi_tools.
We provide a mini dataset, with two samples, to try Riboflow with sequencing reads having UMIs.
In this sample dataset, the first 12 nucleotides on the 5' end of the reads are UMIs.
Four nucleotides following the UMIs need to be discarded.
On the 3' end of the reads, there are adapters having the sequence AAAAAAAAAACAAAAAAAAAA.
The parameters of this sample run are provided in the file project_umi.yaml.
Below are the steps to process this data.
# List the environments to make sure that ribo environment exists
conda env list
# Activate the ribo environment
conda activate ribo
# Get RiboFlow repository
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 run RiboFlow
nextflow RiboFlow.groovy -params-file project_umi.yaml
# At the end of the run
# checkk the ribo file
ribopy info output_umi/ribo/all.ribo
In the current version, UMIs are supported for ribosome profiling data only. So RNA-Seq libraries can either be used without deduplication or the reads can be collapsed based on position.
RiboFlow is designed to work with transcriptomic references. RiboFlow does NOT work with genomic references. The users need to provide a transcriptome reference and annotation to run this software. There is a curated set of RiboFlow references, that users can download and use, in this GitHub repository
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 the output ribo file.
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.
@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}
}
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