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riboseq

Pipeline for the analysis of ribosome profiling, or Ribo-seq (also named ribosome footprinting) data.

https://github.com/nf-core/riboseq

Science Score: 57.0%

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Keywords

nextflow nf-core pipeline workflow
Last synced: 8 months ago · JSON representation ·

Repository

Pipeline for the analysis of ribosome profiling, or Ribo-seq (also named ribosome footprinting) data.

Basic Info
  • Host: GitHub
  • Owner: nf-core
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Homepage: https://nf-co.re/riboseq
  • Size: 19.8 MB
Statistics
  • Stars: 13
  • Watchers: 172
  • Forks: 11
  • Open Issues: 15
  • Releases: 3
Topics
nextflow nf-core pipeline workflow
Created over 2 years ago · Last pushed 10 months ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/riboseq

GitHub Actions CI Status GitHub Actions Linting StatusAWS CICite with Zenodo nf-test

Nextflow run with conda run with docker run with singularity Launch on Seqera Platform

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Introduction

nf-core/riboseq is a bioinformatics pipeline for analysis of Ribo-seq data. It borrows heavily from nf-core/rnaseq in the preprocessing stages:

nf-core/riboseq metro map

  1. Merge re-sequenced FastQ files (cat)
  2. Sub-sample FastQ files and auto-infer strandedness (fq, Salmon)
  3. Read QC (FastQC)
  4. UMI extraction (UMI-tools)
  5. Adapter and quality trimming (Trim Galore!)
  6. Removal of genome contaminants (BBSplit)
  7. Removal of ribosomal RNA (SortMeRNA)
  8. Genome alignment of reads, outputting both genome and transcriptome alignments with STAR
  9. Sort and index alignments (SAMtools)
  10. UMI-based deduplication (UMI-tools)

Differences occur in the downstream analysis steps. Currently these specialist steps are:

  1. Check reads distribution around annotated protein coding regions on user provided transcripts, show frame bias and estimate P-site offset for different group of reads (Ribo-TISH)
  2. (default, optional) Predict translated open reading frames and/ or translation initiation sites de novo from alignment data (Ribo-TISH)
  3. (default, optional) Derive candidate ORFs from reference data and detect translated ORFs from that list (Ribotricer)
  4. (optional) Use a translational efficiency approach to study the dynamics of transcription and translation, with anota2seq. requires matched RNA-seq and Ribo-seq data

Usage

[!NOTE] If you are new to Nextflow and nf-core, please refer to this page on how to set-up Nextflow. Make sure to test your setup with -profile test before running the workflow on actual data.

First, prepare a samplesheet with your input data that looks as follows:

samplesheet.csv:

csv sample,fastq_1,fastq_2,strandedness,type CONTROL_REP1,AEG588A1_S1_L002_R1_001.fastq.gz,AEG588A1_S1_L002_R2_001.fastq.gz,forward,riboseq

Each row represents a fastq file (single-end) or a pair of fastq files (paired end). Each row should have a 'type' value of riboseq, tiseq or rnaseq. Future iterations of the workflow will conduct paired analysis of matched riboseq and rnaseq samples to accomplish analysis types such as 'translational efficiency, but in the current version you should set this to riboseq or tiseq for reglar Ribo-seq or TI-seq data respectively.

Now, you can run the pipeline using:

bash nextflow run nf-core/riboseq \ -profile <docker/singularity/.../institute> \ --input samplesheet.csv \ --outdir <OUTDIR>

Including a translational efficiency analysis

anota2seq - fold change plot

In the translational efficiency analysis provided by anota2seq, we use matched pairs of Ribo-seq and RNA-seq data to study the relationship between transcription and translation as they differ between two treatment groups. For example the test data for this workflow has a contrasts file like:

csv id,variable,reference,target,batch,pair treated_vs_control,treatment,control,treated,,pair

This describes how to compare groups of samples between treament groups, and between RNA-seq and Ribo-seq. In order the columns are:

  • id: a unique identifier to use for the contrast
  • 'variable`: which vaiable (column) of the sample sheet should be used to separate the treatment groups?
  • reference: which value of the variable column should be used to select samples to be used as the reference/ base group?
  • target: which value of the variable column should be used to select samples to be used as the target/treated group?
  • batch: (optional) specify a variable in the sample sheet that defines sample batches
  • pair: (optional) specify a variable in the sample shet that defines sample pairing between RNA-seq and Ribo-seq samples. If not specified, it is assumed that the two types of sample are ordered the same.

[!WARNING] Please provide pipeline parameters via the CLI or Nextflow -params-file option. Custom config files including those provided by the -c Nextflow option can be used to provide any configuration except for parameters; see docs.

For more details and further functionality, please refer to the usage documentation and the parameter documentation.

Pipeline output

To see the results of an example test run with a full size dataset refer to the results tab on the nf-core website pipeline page. For more details about the output files and reports, please refer to the output documentation.

Credits

nf-core/riboseq was originally written by Jonathan Manning (Bioinformatics Engineer at Seqera) with support from Altos Labs and in discussion with Felix Krueger and Christel Krueger. We thank the following people for their input:

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

Citations

If you use nf-core/riboseq for your analysis, please cite it using the following doi: 10.5281/zenodo.10966364

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: nf-core
  • Login: nf-core
  • Kind: organization
  • Email: core@nf-co.re

A community effort to collect a curated set of analysis pipelines built using Nextflow.

Citation (CITATIONS.md) 

# nf-core/riboseq: 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

- [anota2seq](https://bioconductor.org/packages/release/bioc/html/anota2seq.html)

  > Oertlin C, Lorent J, Murie C, Furic L, Topisirovic I, Larsson O. Generally applicable transcriptome-wide analysis of translation using anota2seq. Nucleic Acids Res. 2019 Jul 9;47(12):e70. doi: 10.1093/nar/gkz223. PMID: 30926999; PMCID: PMC6614820.

- [BBMap](https://sourceforge.net/projects/bbmap/)

- [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.

- [fastp](https://www.ncbi.nlm.nih.gov/pubmed/30423086/)

  > Chen S, Zhou Y, Chen Y, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018 Sep 1;34(17):i884-i890. doi: 10.1093/bioinformatics/bty560. PubMed PMID: 30423086; PubMed Central PMCID: PMC6129281.

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

> Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data [Online].

- [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.

- [Ribo-TISH](https://pubmed.ncbi.nlm.nih.gov/29170441/)

  > Zhang P, He D, Xu Y, Hou J, Pan BF, Wang Y, Liu T, Davis CM, Ehli EA, Tan L, Zhou F, Hu J, Yu Y, Chen X, Nguyen TM, Rosen JM, Hawke DH, Ji Z, Chen Y. Genome-wide identification and differential analysis of translational initiation. Nat Commun. 2017 Nov 23;8(1):1749. doi: 10.1038/s41467-017-01981-8. PMID: 29170441; PMCID: PMC5701008.

- [Ribotricer](https://pubmed.ncbi.nlm.nih.gov/31750902/)

  > Choudhary S, Li W, D Smith A. Accurate detection of short and long active ORFs using Ribo-seq data. Bioinformatics. 2020 Apr 1;36(7):2053-2059. doi: 10.1093/bioinformatics/btz878. PMID: 31750902; PMCID: PMC7141849.

- [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.

- [Trim Galore!](https://github.com/FelixKrueger/TrimGalore)

- [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.

## 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)

  > Merkel, D. (2014). Docker: lightweight linux containers for consistent development and deployment. Linux Journal, 2014(239), 2. 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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Issues and Pull Requests

Last synced: 8 months ago

All Time
  • Total issues: 26
  • Total pull requests: 56
  • Average time to close issues: 3 months
  • Average time to close pull requests: 4 days
  • Total issue authors: 10
  • Total pull request authors: 6
  • Average comments per issue: 1.23
  • Average comments per pull request: 1.23
  • Merged pull requests: 39
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 19
  • Average time to close issues: 9 days
  • Average time to close pull requests: 2 days
  • Issue authors: 5
  • Pull request authors: 5
  • Average comments per issue: 1.86
  • Average comments per pull request: 1.11
  • Merged pull requests: 14
  • Bot issues: 0
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