README.md

Rhea pipeline

Snakemake workflow for general purpose RNA-Seq library annotation developed by the Zavolan lab.

Reads are processed, aligned, quantified and analyzed with state-of-the-art tools to give meaningful initial insights into various aspects of an RNA-Seq library while cutting down on hands-on time for bioinformaticians.

Below is a schematic representation of the individual workflow steps ("pe" refers to "paired-end"):

For a more detailed description of each step, please refer to the pipeline documentation.

Requirements

Currently the workflow is only available for Linux distributions. It was tested on the following distributions:

• CentOS 7.5
• Debian 10
• Ubuntu 16.04, 18.04

Installation

Cloning the repository

Traverse to the desired path on your file system, then clone the repository and move into it with:

git clone ssh://git@git.scicore.unibas.ch:2222/zavolan_group/pipelines/rhea.git
cd rhea

Installing Conda

Workflow dependencies can be conveniently installed with the Conda package manager. We recommend that you install Miniconda for your system (Linux). Be sure to select Python 3 option. The workflow was built and tested with miniconda 4.7.12. Other versions are not guaranteed to work as expected.

Installing dependencies

For improved reproducibility and reusability of the workflow, each individual step of the workflow runs in its own Singularity container. As a consequence, running this workflow has very few individual dependencies. It does, however, require Singularity to be installed on the system running the workflow. As the functional installation of Singularity requires root privileges, and Conda currently only provides Singularity for Linux architectures, the installation instructions are slightly different depending on your system/setup:

For most users

If you do not have root privileges on the machine you want to run the workflow on or if you do not have a Linux machine, please install Singularity separately and in privileged mode, depending on your system. You may have to ask an authorized person (e.g., a systems administrator) to do that. This will almost certainly be required if you want to run the workflow on a high-performance computing (HPC) cluster. We have successfully tested the workflow with the following Singularity versions:

• v2.4.5
• v2.6.2
• v3.5.2

After installing Singularity, install the remaining dependencies with:

conda env create -f install/environment.yml

As root user on Linux

If you have a Linux machine, as well as root privileges, (e.g., if you plan to run the workflow on your own computer), you can execute the following command to include Singularity in the Conda environment:

conda env create -f install/environment.root.yml

Activate environment

Activate the Conda environment with:

conda activate rhea

Installing non-essential dependencies

Most tests have additional dependencies. If you are planning to run tests, you will need to install these by executing the following command in your active Conda environment:

conda env update -f install/environment.dev.yml

Testing the installation

We have prepared several tests to check the integrity of the workflow, its components and non-essential processing scripts. These can be found in subdirectories of the tests/ directory. The most critical of these tests lets you execute the entire workflow on a small set of example input files. Note that for this and other tests to complete without issues, additional dependencies need to be installed.

Run workflow on local machine

Execute the following command to run the test workflow on your local machine:

bash tests/test_integration_workflow/test.local.sh

Run workflow via Slurm

Execute the following command to run the test workflow on a Slurm-managed high-performance computing (HPC) cluster:

bash tests/test_integration_workflow/test.slurm.sh

NOTE: Depending on the configuration of your Slurm installation or if using a different workflow manager, you may need to adapt file cluster.json and the arguments to options --config and --cores in file test.slurm.sh, both located in directory tests/test_integration_workflow. Consult the manual of your workload manager as well as the section of the Snakemake manual dealing with cluster execution.

Running the workflow on your own samples

1. Assuming that you are currently inside the repository's root directory, create a directory for your workflow run and traverse inside it with:

   mkdir config/my_run
   cd config/my_run

2. Create empty sample table, workflow configuration and, if necessary, cluster configuration files:

   touch samples.tsv
   touch config.yaml
   touch cluster.json

3. Use your editor of choice to manually populate these files with appropriate values. Have a look at the examples in the tests/ directory to see what the files should look like, specifically:

   • samples.tsv
   • config.yaml
   • cluster.json

4. Create a runner script. Pick one of the following choices for either local or cluster execution. Before execution of the respective command, you must replace the data directory placeholders in the argument to the --singularity-args option with a comma-separated list of all directories containing input data files (samples and any annoation files etc) required for your run.

   Runner script for local execution:

   cat << "EOF" > run.sh
   #!/bin/bash
   snakemake \
       --snakefile="../../snakemake/Snakefile" \
       --configfile="config.yaml" \
       --cores=4 \
       --printshellcmds \
       --rerun-incomplete \
       --use-singularity \
       --singularity-args="--bind <data_dir_1>,<data_dir_2>,<data_dir_n>"
   EOF

   OR

   Runner script for Slurm cluster exection (note that you may need to modify the arguments to --cluster and --cores depending on your HPC and workload manager configuration):

   cat << "EOF" > run.sh
   #!/bin/bash
   mkdir -p logs/cluster_log
   snakemake \
       --snakefile="../../snakemake/Snakefile" \
       --configfile="config.yaml" \
       --cluster-config="cluster.json" \
       --cluster="sbatch --cpus-per-task={cluster.threads} --mem={cluster.mem} --qos={cluster.queue} --time={cluster.time} --job-name={cluster.name} -o {cluster.out} -p scicore" \
       --cores=256 \
       --printshellcmds \
       --rerun-incomplete \
       --use-singularity \
       --singularity-args="--bind <data_dir_1>,<data_dir_2>,<data_dir_n>"
   EOF

5. Start your workflow run:

   bash run.sh

Configuring workflow runs via LabKey tables

Our lab stores metadata for sequencing samples in a locally deployed LabKey instance. This repository provides two scripts that give programmatic access to the LabKey data table and convert it to the corresponding workflow inputs (samples.tsv and config.yaml), respectively. As such, these scripts largely automate step 3. of the above instructions. However, as these scripts were specifically for the needs of our lab, they are likely not portable or, at least, will require considerable modification for other setups (e.g., different LabKey table structure).

NOTE: All of the below steps assume that your current working directory is the repository's root directory.

1. The scripts have additional dependencies that can be installed with:

   pip install -r scripts/requirements.txt

2. In order to gain programmatic access to LabKey via its API, a credential file is required. Create it with the following command after replacing the placeholder values with your real credentials (talk to your LabKey manager if you do not have these):

   cat << EOF | ( umask 0377; cat >> ${HOME}/.netrc; )
   machine <remote-instance-of-labkey-server>  
   login <user-email>
   password <user-password>  
   EOF

3. Generate the workflow configuration with the following command, after replacing the placeholders with the appropriate values (check out the help screen with option '--help' for further options and information):

   python scripts/labkey_to_snakemake.py \
       --input_dict="scripts/labkey_to_snakemake.dict.tsv" \
       --config_file="config/my_run/config.yaml" \
       --samples_table="config/my_run/samples.tsv" \
       --remote \
       --project-name="project_name" \
       --table-name="table_name" \
       <path_to_annotation_files>

Additional information

The metadata field names in the LabKey instance and those in the parameters in the Snakemake workflow have different names. A mapping between LabKey field identifiers and Snakemake parameters is listed below:

Labkey	Snakemake
Entry date	entry_date
Path to FASTQ file(s)	fastq_path
Condition name	condition
Replicate name	replicate_name
End type (PAIRED or SINGLE)	seqmode
Name of Mate1 FASTQ file	fq1
Name of Mate2 FASTQ file	fq2
Direction of Mate1 (SENSE, ANTISENSE or RANDOM)	mate1_direction
Direction of Mate2 (SENSE, ANTISENSE or RANDOM)	mate2_direction
5' adapter of Mate1	fq1_5p
3' adapter of Mate1	fq1_3p
5' adapter of Mate2	fq2_5p
3' adapter of Mate2	fq2_3p
Fragment length mean	mean
Fragment length SD	sd
Quality control flag (PASSED or FAILED)	quality_control_flag
Checksum of raw Mate1 FASTQ file	mate1_checksum
Checksum of raw Mate2 FASTQ file	mate2_checksum
Name of metadata file	metadata
Name of quality control file for Mate1	mate1_quality
Name of quality control file for Mate2	mate2_quality
Organism	organism
Taxon ID	taxon_id
Name of Strain / Isolate / Breed / Ecotype	strain_name
Strain / Isolate / Breed / Ecotype ID	strain_id
Biomaterial provider	biomaterial_provider
Source / tissue name	source_name
Tissue code	tissue_code
Additional tissue description	tissue_description
Genotype short name	genotype_name
Genotype description	genotype_description
Disease short name	disease_name
Disease description	disease_description
Abbreviation for treatment	treatment
Treatment description	treatment_description
Gender	gender
Age	age
Developmental stage	development_stage
Passage number	passage_number
Sample preparation date (YYYY-MM-DD)	sample_prep_date
Prepared by	prepared_by
Documentation	documentation
Name of protocol file	protocol_file
Sequencing date (YYYY-MM-DD)	seq_date
Sequencing instrument	seq_instrument
Library preparation kit	library_kit
Cycles	cycles
Molecule	molecule
Contaminant sequences	contaminant_seqs