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nf-core/smrnaseq

GitHub Actions CI Status GitHub Actions Linting Status Nextflow

install with bioconda Docker Get help on Slack

DOI

Introduction

nf-core/smrnaseq is a bioinformatics best-practice analysis pipeline used for small RNA sequencing data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It comes with docker containers making installation trivial and results highly reproducible.

Quick Start

  1. Install nextflow (>=20.04.0)

  2. Install any of Docker, Singularity, Podman, Shifter or Charliecloud for full pipeline reproducibility (please only use Conda 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,<docker/singularity/podman/shifter/charliecloud/conda/institute>

    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.

  4. Start running your own analysis!

    nextflow run nf-core/smrnaseq -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --input '*_R{1,2}.fastq.gz' --genome GRCh37

See usage docs for all of the available options when running the pipeline.

Pipeline summary

  1. Raw read QC (FastQC)
  2. Adapter trimming (Trim Galore!)
    1. Insert Size calculation
    2. Collapse reads (seqcluster)
  3. Alignment against miRBase mature miRNA (Bowtie1)
  4. Alignment against miRBase hairpin
    1. Unaligned reads from step 3 (Bowtie1)
    2. Collapsed reads from step 2.2 (Bowtie1)
  5. Post-alignment processing of miRBase hairpin
    1. Basic statistics from step 3 and step 4.1 (SAMtools)
    2. Analysis on miRBase 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)
  6. Alignment against host reference genome (Bowtie1)
    1. Post-alignment processing of alignment against host reference genome (SAMtools)
  7. 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
  8. miRNA quality control (mirtrace)
  9. Present QC for raw read, alignment, and expression results (MultiQC)

Documentation

The nf-core/smrnaseq pipeline comes with documentation about the pipeline: usage 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). Updated by Lorena Pantano (@lpantano) from MIT.

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

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.

In addition, references of tools and data used in this pipeline are as follows: