hribo 1.5.0

HRIBO (High-throughput annotation by Ribo-seq), a workflow to enable reproducible and high-throughput analysis of bacterial Ribo-seq data.

High-throughput annotation by Ribo-seq

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.

HRIBO can be installed with all dependencies via conda. 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 [e-mail]:gelhausr/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. 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 Snakefile --configfile HRIBO/config.yaml --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 Snakefile --configfile HRIBO/config.yaml --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 <email>" --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