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dicerna-rnaseq

Dicerna-RNA SEQ pipeline based on nf-core

https://github.com/junyu-boston/dicerna-rnaseq

Science Score: 44.0%

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Repository

Dicerna-RNA SEQ pipeline based on nf-core

Basic Info
  • Host: GitHub
  • Owner: junyu-boston
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage:
  • Size: 52.4 MB
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  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 5 years ago · Last pushed over 4 years ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/rnaseq

GitHub Actions CI Status GitHub Actions Linting Status AWS CI Cite with Zenodo

Nextflow run with conda run with docker run with singularity

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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:

    bash 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:

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

* Typical command for downloading public data:

    ```bash
    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.

Owner

  • Name: Jun Yu
  • Login: junyu-boston
  • Kind: user
  • Location: Boston, MA
  • Company: Boston University

Citation (CITATIONS.md) 

# nf-core/rnaseq: Citations

## [nf-core](https://pubmed.ncbi.nlm.nih.gov/32055031/)

> Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020 Mar;38(3):276-278. doi: 10.1038/s41587-020-0439-x. PubMed PMID: 32055031.

## [Nextflow](https://pubmed.ncbi.nlm.nih.gov/28398311/)

> Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017 Apr 11;35(4):316-319. doi: 10.1038/nbt.3820. PubMed PMID: 28398311.

## Pipeline tools

* [BEDTools](https://pubmed.ncbi.nlm.nih.gov/20110278/)
  > Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010 Mar 15;26(6):841-2. doi: 10.1093/bioinformatics/btq033. Epub 2010 Jan 28. PubMed PMID: 20110278; PubMed Central PMCID: PMC2832824.

* [FastQC](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

* [featureCounts](https://pubmed.ncbi.nlm.nih.gov/24227677/)
  > Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014 Apr 1;30(7):923-30. doi: 10.1093/bioinformatics/btt656. Epub 2013 Nov 13. PubMed PMID: 24227677.

* [GffRead](https://pubmed.ncbi.nlm.nih.gov/32489650/)
  > Pertea G, Pertea M. GFF Utilities: GffRead and GffCompare. F1000Res. 2020 Apr 28;9:ISCB Comm J-304. doi: 10.12688/f1000research.23297.2. eCollection 2020. PubMed PMID: 32489650; PubMed Central PMCID: PMC7222033.

* [HISAT2](https://pubmed.ncbi.nlm.nih.gov/31375807/)
  > Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019 Aug;37(8):907-915. doi: 10.1038/s41587-019-0201-4. Epub 2019 Aug 2. PubMed PMID: 31375807.

* [MultiQC](https://pubmed.ncbi.nlm.nih.gov/27312411/)
  > Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16. PubMed PMID: 27312411; PubMed Central PMCID: PMC5039924.

* [picard-tools](http://broadinstitute.github.io/picard)

* [preseq](https://pubmed.ncbi.nlm.nih.gov/23435259/)
  > Daley T, Smith AD. Predicting the molecular complexity of sequencing libraries. Nat Methods. 2013 Apr;10(4):325-7. doi: 10.1038/nmeth.2375. Epub 2013 Feb 24. PubMed PMID: 23435259; PubMed Central PMCID: PMC3612374.

* [Qualimap 2](https://pubmed.ncbi.nlm.nih.gov/26428292/)
  > Okonechnikov K, Conesa A, García-Alcalde F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data Bioinformatics. 2016 Jan 15;32(2):292-4. doi: 10.1093/bioinformatics/btv566. Epub 2015 Oct 1. PubMed PMID: 26428292; PubMed Central PMCID: PMC4708105.

* [RSEM](https://pubmed.ncbi.nlm.nih.gov/21816040/)
  > Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome BMC Bioinformatics. 2011 Aug 4;12:323. doi: 10.1186/1471-2105-12-323. PubMed PMID: 21816040; PubMed Central PMCID: PMC3163565.

* [RSeQC](https://pubmed.ncbi.nlm.nih.gov/22743226/)
  > Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments Bioinformatics. 2012 Aug 15;28(16):2184-5. doi: 10.1093/bioinformatics/bts356. Epub 2012 Jun 27. PubMed PMID: 22743226.

* [Salmon](https://pubmed.ncbi.nlm.nih.gov/28263959/)
  > Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression Nat Methods. 2017 Apr;14(4):417-419. doi: 10.1038/nmeth.4197. Epub 2017 Mar 6. PubMed PMID: 28263959; PubMed Central PMCID: PMC5600148.

* [SAMtools](https://pubmed.ncbi.nlm.nih.gov/19505943/)
  > Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: 10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PubMed PMID: 19505943; PubMed Central PMCID: PMC2723002.

* [SortMeRNA](https://pubmed.ncbi.nlm.nih.gov/23071270/)
  > Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data Bioinformatics. 2012 Dec 15;28(24):3211-7. doi: 10.1093/bioinformatics/bts611. Epub 2012 Oct 15. PubMed PMID: 23071270.

* [STAR](https://pubmed.ncbi.nlm.nih.gov/23104886/)
  > Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner Bioinformatics. 2013 Jan 1;29(1):15-21. doi: 10.1093/bioinformatics/bts635. Epub 2012 Oct 25. PubMed PMID: 23104886; PubMed Central PMCID: PMC3530905.

* [StringTie2](https://pubmed.ncbi.nlm.nih.gov/31842956/)
  > Kovaka S, Zimin AV, Pertea GM, Razaghi R, Salzberg SL, Pertea M. Transcriptome assembly from long-read RNA-seq alignments with StringTie2 Genome Biol. 2019 Dec 16;20(1):278. doi: 10.1186/s13059-019-1910-1. PubMed PMID: 31842956; PubMed Central PMCID: PMC6912988.

* [Trim Galore!](https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/)

* [UMI-tools](https://pubmed.ncbi.nlm.nih.gov/28100584/)
  > Smith T, Heger A, Sudbery I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy Genome Res. 2017 Mar;27(3):491-499. doi: 10.1101/gr.209601.116. Epub 2017 Jan 18. PubMed PMID: 28100584; PubMed Central PMCID: PMC5340976.

* [UCSC tools](https://pubmed.ncbi.nlm.nih.gov/20639541/)
  > Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics. 2010 Sep 1;26(17):2204-7. doi: 10.1093/bioinformatics/btq351. Epub 2010 Jul 17. PubMed PMID: 20639541; PubMed Central PMCID: PMC2922891.

## R packages

* [R](https://www.R-project.org/)
  > R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

* [DESeq2](https://pubmed.ncbi.nlm.nih.gov/25516281/)
  > Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. PubMed PMID: 25516281; PubMed Central PMCID: PMC4302049.

* [dupRadar](https://pubmed.ncbi.nlm.nih.gov/27769170/)
  > Sayols S, Scherzinger D, Klein H. dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data BMC Bioinformatics. 2016 Oct 21;17(1):428. doi: 10.1186/s12859-016-1276-2. PubMed PMID: 27769170; PubMed Central PMCID: PMC5073875.

* [ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html)
  > H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

* [optparse](https://CRAN.R-project.org/package=optparse)
  > Trevor L Davis (2018). optparse: Command Line Option Parser.

* [pheatmap](https://CRAN.R-project.org/package=pheatmap)
  > Raivo Kolde (2018). pheatmap: Pretty Heatmaps.

* [RColorBrewer](https://CRAN.R-project.org/package=RColorBrewer)
  > Erich Neuwirth (2014). RColorBrewer: ColorBrewer Palettes.

* [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html)
  > Morgan M, Obenchain V, Hester J and Pagès H (2020). SummarizedExperiment: SummarizedExperiment container.

* [Tximeta](https://pubmed.ncbi.nlm.nih.gov/32097405/)
  > Love MI, Soneson C, Hickey PF, Johnson LK, Pierce NT, Shepherd L, Morgan M, Patro R. Tximeta: Reference sequence checksums for provenance identification in RNA-seq PLoS Comput Biol. 2020 Feb 25;16(2):e1007664. doi: 10.1371/journal.pcbi.1007664. eCollection 2020 Feb. PubMed PMID: 32097405; PubMed Central PMCID: PMC7059966.

## Software packaging/containerisation tools

* [Anaconda](https://anaconda.com)
  > Anaconda Software Distribution. Computer software. Vers. 2-2.4.0. Anaconda, Nov. 2016. Web.

* [Bioconda](https://pubmed.ncbi.nlm.nih.gov/29967506/)
  > Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nat Methods. 2018 Jul;15(7):475-476. doi: 10.1038/s41592-018-0046-7. PubMed PMID: 29967506.

* [BioContainers](https://pubmed.ncbi.nlm.nih.gov/28379341/)
  > da Veiga Leprevost F, Grüning B, Aflitos SA, Röst HL, Uszkoreit J, Barsnes H, Vaudel M, Moreno P, Gatto L, Weber J, Bai M, Jimenez RC, Sachsenberg T, Pfeuffer J, Alvarez RV, Griss J, Nesvizhskii AI, Perez-Riverol Y. BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics. 2017 Aug 15;33(16):2580-2582. doi: 10.1093/bioinformatics/btx192. PubMed PMID: 28379341; PubMed Central PMCID: PMC5870671.

* [Docker](https://dl.acm.org/doi/10.5555/2600239.2600241)

* [Singularity](https://pubmed.ncbi.nlm.nih.gov/28494014/)
  > Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS One. 2017 May 11;12(5):e0177459. doi: 10.1371/journal.pone.0177459. eCollection 2017. PubMed PMID: 28494014; PubMed Central PMCID: PMC5426675.

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modules/nf-core/software/fastqc/meta.yml cpan
modules/nf-core/software/picard/markduplicates/meta.yml cpan
modules/nf-core/software/samtools/flagstat/meta.yml cpan
modules/nf-core/software/samtools/idxstats/meta.yml cpan
modules/nf-core/software/samtools/index/meta.yml cpan
modules/nf-core/software/samtools/sort/meta.yml cpan
modules/nf-core/software/samtools/stats/meta.yml cpan
modules/nf-core/software/trimgalore/meta.yml cpan