SpikyClip/llrnaseq

Introduction

SpikyClip/llrnaseq is a simple RNA-seq pipeline adapted to the Latrobe Institute of Molecular Science (LIMS) High Performance Computing Cluster (HPCC).

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.

As the new cluster now has singularity support, the best profile to run the pipeline is now -profile lims,singularity.

Pipeline summary

1. Read QC (FastQC 0.11.9)
2. Present QC for raw reads (MultiQC 1.9)
3. Trim reads (Trim Galore 0.6.3)
4. Index genome (Hisat2.1.0)
5. Align reads (Hisat2.1.0)
6. Sort and index alignments (Samtools 1.9)
7. Read quantification (featureCounts 1.6, StringTie 1.3.5)

Quick Start

1. Install Nextflow (>=21.04.3) (see installation.md for more information)

2. If executing the pipeline on a computer that can support it, install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda as a last resort; see docs). If executing the pipeline on the LIMS-HPCC, ignore this step.

3. Download the pipeline and test it on a minimal dataset with a single command:

   1. If running on the LIMS-HPCC:

      nextflow run SpikyClip/llrnaseq -profile test,lims,singularity
      

   2. If running on a Docker/Singularity capable machine:

      nextflow run SpikyClip/llrnaseq -profile test,<docker/singularity/podman/shifter/charliecloud/conda/institute>
      

   • If you are using singularity then the pipeline will auto-detect this and attempt to download the Singularity images directly as opposed to performing a conversion from Docker images. If you are persistently observing issues downloading Singularity images directly due to timeout or network issues then please use the --singularity_pull_docker_container parameter to pull and convert the Docker image instead. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able to store and re-use the images from a central location for future pipeline runs.
   • If you are using conda, it is highly recommended to use the NXF_CONDA_CACHEDIR or conda.cacheDir settings to store the environments in a central location for future pipeline runs.

4. Start running your own analysis!

   1. You will first need to create a samplesheet with information about the samples you would like to analyse before running the pipeline.

   2. The pipeline can pull some common genome references used for alignment from Illumina iGenomes. Check out igenomes.config to see the full list of iGenomes this pipeline recognises.

      nextflow run llrnaseq \
          -profile lims,singularity \
          --input <samplesheet>.csv \
          --genome GRCh37
      

   3. Alternatively, you can specify genome.fa and genome.gtf explicitly:

      nextflow run llrnaseq \
          -profile lims,singularity \
          --input <samplesheet>.csv \
          --fasta <genome>.fa> \
          --gtf <annotation>.gtf
      

   4. If running a job on the LIMS-HPCC, wrap the nextflow run command in a shell script (e.g. run_pipeline.sh) and submit it using slurm:

      sbatch run_pipeline.sh

      Consider specifying the estimated time needed in the script if the job may take more than 8 hours using #SBATCH --time=<HH>:<MM>:<SS>. This is to avoid the pipeline ending prematurely. However, if the job is interrupted, it may be resumed with the nextflow -resume flag. See the usage docs for more information on the -resume flag.

Documentation

The SpikyClip/llrnaseq pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

SpikyClip/llrnaseq was originally written by Vikesh Ajith.

We thank the following people for their extensive assistance in the development of this pipeline:

Contributions and Support

This applied research project was supervised by Dr. Mathew Lewsey and Dr. Bhavna Hurgobin from lewseylab.

If you would like to contribute to this pipeline, please see the contributing guidelines.

Citations

An extensive list of references for the tools used by the pipeline can be found in the CITATIONS.md file.

You can cite the nf-core publication as follows:

The nf-core framework for community-curated bioinformatics pipelines.

Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.

Nat Biotechnol. 2020 Feb 13. doi: 10.1038/s41587-020-0439-x.