nf-core/smrnaseq

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Introduction

nf-core/smrnaseq is a bioinformatics best-practice analysis pipeline for Small RNA-Seq Best Practice Analysis Pipeline.

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. Where possible, these processes have been submitted to and installed from nf-core/modules in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!

On release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the nf-core website.

Online videos

A short talk about the history, current status and functionality on offer in this pipeline was given by Lorena Pantano (@lpantano) on 9th November 2021 as part of the nf-core/bytesize series.

You can find numerous talks on the nf-core events page from various topics including writing pipelines/modules in Nextflow DSL2, using nf-core tooling, running nf-core pipelines as well as more generic content like contributing to Github. Please check them out!

Pipeline summary

1. Raw read QC (FastQC)
2. Adapter trimming (Trim Galore!)
   1. Insert Size calculation
   2. Collapse reads (seqcluster)
3. Contamination filtering (Bowtie2)
4. Alignment against miRBase mature miRNA (Bowtie1)
5. Alignment against miRBase hairpin
   1. Unaligned reads from step 3 (Bowtie1)
   2. Collapsed reads from step 2.2 (Bowtie1)
6. Post-alignment processing of miRBase hairpin
   1. Basic statistics from step 3 and step 4.1 (SAMtools)
   2. Analysis on miRBase, or MirGeneDB hairpin counts (edgeR)
      • TMM normalization and a table of top expression hairpin
      • MDS plot clustering samples
      • Heatmap of sample similarities
   3. miRNA and isomiR annotation from step 4.1 (mirtop)
7. Alignment against host reference genome (Bowtie1)
   1. Post-alignment processing of alignment against host reference genome (SAMtools)
8. Novel miRNAs and known miRNAs discovery (MiRDeep2)
   1. Mapping against reference genome with the mapper module
   2. Known and novel miRNA discovery with the mirdeep2 module
9. miRNA quality control (mirtrace)
10. Present QC for raw read, alignment, and expression results (MultiQC)

Quick Start

1. Install Nextflow (>=22.10.1)

2. Install any of Docker, Singularity (you can follow this tutorial), Podman, Shifter or Charliecloud for full pipeline reproducibility (you can use Conda both to install Nextflow itself and also to manage software within pipelines. Please only use it within pipelines as a last resort; see docs).

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

   nextflow run nf-core/smrnaseq -profile test,YOURPROFILE --outdir <OUTDIR>

   Note that some form of configuration will be needed so that Nextflow knows how to fetch the required software. This is usually done in the form of a config profile (YOURPROFILE in the example command above). You can chain multiple config profiles in a comma-separated string.

   • The pipeline comes with config profiles called docker, singularity, podman, shifter, charliecloud and conda which instruct the pipeline to use the named tool for software management. For example, -profile test,docker.
   • Please check nf-core/configs to see if a custom config file to run nf-core pipelines already exists for your Institute. If so, you can simply use -profile <institute> in your command. This will enable either docker or singularity and set the appropriate execution settings for your local compute environment.
   • If you are using singularity, please use the nf-core download command to download images first, before running the pipeline. Setting the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options enables you 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!

   nextflow run nf-core/smrnaseq --input samplesheet.csv --outdir <OUTDIR> --genome GRCh37 -profile <docker/singularity/podman/shifter/charliecloud/conda/institute>

Documentation

The nf-core/smrnaseq pipeline comes with documentation about the pipeline usage, parameters and output.

Credits

nf-core/smrnaseq was originally written for use at the National Genomics Infrastructure at SciLifeLab in Stockholm, Sweden, by Phil Ewels (@ewels), Chuan Wang (@chuan-wang) and Rickard Hammarén (@Hammarn).

Lorena Pantano (@lpantano) from MIT updated the pipeline to Nextflow DSL2.

Contributions and Support

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

For further information or help, don’t hesitate to get in touch on the Slack #smrnaseq channel (you can join with this invite).

Citations

If you use nf-core/smrnaseq for your analysis, please cite it using the following doi: 10.5281/zenodo.3456879

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.