junyu-boston/dicerna-rnaseq

Dicerna-RNA SEQ pipeline based on nf-core

Introduction

nf-core/rnaseq is a bioinformatics analysis pipeline used for 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.

On release, automated continuous integration tests run the pipeline on a full-sized dataset obtained from the ENCODE Project Consortium 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.

Pipeline summary

1. Download FastQ files via SRA, ENA or GEO ids and auto-create input samplesheet (ENA FTP; if required)
2. Merge re-sequenced FastQ files (cat)
3. Read QC (FastQC)
4. UMI extraction (UMI-tools)
5. Adapter and quality trimming (Trim Galore!)
6. Removal of ribosomal RNA (SortMeRNA)
7. Choice of multiple alignment and quantification routes:
   1. STAR -> Salmon
   2. STAR -> RSEM
   3. HiSAT2 -> NO QUANTIFICATION
8. Sort and index alignments (SAMtools)
9. UMI-based deduplication (UMI-tools)
10. Duplicate read marking (picard MarkDuplicates)
11. Transcript assembly and quantification (StringTie)
12. Create bigWig coverage files (BEDTools, bedGraphToBigWig)
13. Extensive quality control:
    1. RSeQC
    2. Qualimap
    3. dupRadar
    4. Preseq
    5. DESeq2
14. Pseudo-alignment and quantification (Salmon; optional)
15. Present QC for raw read, alignment, gene biotype, sample similarity, and strand-specificity checks (MultiQC, R)

NB: Quantification isn't performed if using --aligner hisat2 due to the lack of an appropriate option to calculate accurate expression estimates from HISAT2 derived genomic alignments. However, you can use this route if you have a preference for the alignment, QC and other types of downstream analysis compatible with the output of HISAT2.
NB: The --aligner star_rsem option will require STAR indices built from version 2.7.6a or later. However, in order to support legacy usage of genomes hosted on AWS iGenomes the --aligner star_salmon option requires indices built with STAR 2.6.1d or earlier. Please refer to this issue for further details.

Quick Start

1. Install nextflow

2. Install any of Docker, Singularity or Podman for full pipeline reproducibility (please only use Conda as a last resort; see docs). Note: This pipeline does not currently support running with Conda on macOS because the latest version of the SortMeRNA package is not available for this platform.

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

   nextflow run nf-core/rnaseq -profile test,<docker/singularity/podman/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.
   • 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. It is also highly recommended to use the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir settings to store the images in 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!

   • Typical command for RNA-seq analysis:

     nextflow run nf-core/rnaseq \
         --input samplesheet.csv \
         --genome GRCh37 \
         -profile <docker/singularity/podman/conda/institute>

   • Typical command for downloading public data:

     nextflow run nf-core/rnaseq \
         --public_data_ids ids.txt \
         -profile <docker/singularity/podman/conda/institute>

   NB: The commands to obtain public data and to run the main arm of the pipeline are completely independent. This is intentional because it allows you to download all of the raw data in an initial pipeline run (results/public_data/) and then to curate the auto-created samplesheet based on the available sample metadata before you run the pipeline again properly.

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

Documentation

The nf-core/rnaseq pipeline comes with documentation about the pipeline: usage and output.

Credits

These scripts were originally written for use at the National Genomics Infrastructure, part of SciLifeLab in Stockholm, Sweden, by Phil Ewels (@ewels) and Rickard Hammarén (@Hammarn).

The pipeline was re-written in Nextflow DSL2 by Harshil Patel (@drpatelh) from The Bioinformatics & Biostatistics Group at The Francis Crick Institute, London.

Many thanks to other who have helped out along the way too, including (but not limited to): @Galithil, @pditommaso, @orzechoj, @apeltzer, @colindaven, @lpantano, @olgabot, @jburos.

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 #rnaseq channel (you can join with this invite).

Citations

If you use nf-core/rnaseq for your analysis, please cite it using the following doi: 10.5281/zenodo.1400710

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.