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https://github.com/RickGelhausen/HRIBO

We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing steps and quality control. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary prokaryotic-focused tools, and integrates them with additional computed features. This facilitates both the rapid discovery of ORFs and their prioritization for functional characterization.
https://github.com/RickGelhausen/HRIBO

bioinformatics prokaryotes ribosome-profiling snakemake

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We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing steps and quality control. Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary prokaryotic-focused tools, and integrates them with additional computed features. This facilitates both the rapid discovery of ORFs and their prioritization for functional characterization.

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# High-throughput annotation by Ribo-seq



[![GitHub](https://img.shields.io/github/tag/RickGelhausen/HRIBO.svg)](https://github.com/RickGelhausen/HRIBO)

[![Snakemake](https://img.shields.io/badge/snakemake-≥5.10.0-brightgreen.svg)](https://snakemake.bitbucket.io)

[![Documentation Status](https://readthedocs.org/projects/hribo/badge/?version=latest)](http://hribo.readthedocs.io/?badge=latest)

[![PyPI Latest Release](https://img.shields.io/pypi/v/hribo.svg)](https://pypi.org/project/hribo/)



We present HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data. The workflow performs all required pre-processing steps and quality control.  Importantly, HRIBO outputs annotation-independent ORF predictions based on two complementary prokaryotic-focused tools, and integrates them with additional computed features. This facilitates both the rapid discovery of ORFs and their prioritization for functional characterization.



For a detailed description of this workflow, the installation, usage and examples, please refer to the [ReadTheDocs documentation](http://hribo.readthedocs.io/?badge=latest).



HRIBO installs all dependencies via [conda](https://conda.io/docs/install/quick.html). Once you have conda installed simply type:



         conda create -c bioconda -c conda-forge -n snakemake snakemake



         source activate snakemake



### Basic usage



The retrieval of input files and running the workflow locally and on a server cluster via a queuing system is

working as follows. Create a project directory and change into it:



         mkdir project

         cd project



Retrieve the HRIBO from GitHub:



         git clone [email protected]:RickGelhausen/HRIBO.git



The workflow requires a genome sequence (fasta), an annotation file (gtf) and the sequencing results files (fastq).

We recommend retrieving both the genome and the annotation files from [Ensembl Genomes](http://ensemblgenomes.org/).

Copy the genome and the annotation file into the project folder, decompress them and name them genome.fa and annotation.gtf.



Create a folder fastq and copy your compressed fastq.gz files into the fastq folder.



Please copy the template of the sample sheet and the config file into the HRIBO folder.



         cp HRIBO/templates/config.yaml HRIBO/

         cp HRIBO/templates/samples.tsv HRIBO/



Customize the config.yaml with the used adapter sequence and optionally with the path to a precomputed

STAR genome index. For correct removal of reads mapping to ribosomal genes please specify the taxonomic group of

the used organism (Eukarya, Bacteria, Archea).

Now edit the sample sheet corresponding to your project, using one line per sequencing result, stating the used

method (RIBO for ribosome profiling, RNA for RNA-seq), the applied condition (e.g. A, B, CTRL, TREAT), the replicate (e.g. 1, 2,..) and the filename. Following is an example:



|method|	condition |replicate|	fastqFile                 |

|------|-----------|---------|--------------------------------|

|RIBO  |	A         |        1|"fastq/FP-ctrl-1-2.fastq.gz"    |

|RIBO  |	B         |        1|"fastq/FP-treat-1-2.fastq.gz"   |

|RNA   |	A         |        1|"fastq/Total-ctrl-1-2.fastq.gz" |

|RNA   |	B         |        1|"fastq/Total-treat-1-2.fastq.gz"|



Now you can start your workflow.



Run Snakemake locally:



         snakemake --use-conda -s HRIBO/Snakefile --directory ${PWD} -j 20 --latency-wait 60



Run Snakemake on the cluster:



Edit cluster.yaml according to your queuing system and cluster hardware. The following example works for Grid Engine:



       snakemake --use-conda -s HRIBO/Snakefile --directory ${PWD} -j 20 --cluster-config HRIBO/cluster.yaml --cluster "qsub -N {cluster.jobname} -cwd -q {cluster.qname} -pe {cluster.parallelenvironment} -l {cluster.memory} -o {cluster.logoutputdir} -e {cluster.erroroutputdir} -j {cluster.joinlogs} -M " --latency-wait 60



Once the workflow has finished you can request a automatically generated report.html file with the following command:



       snakemake --report report.html