README.md

Introduction

nf-core/circrna is a bioinformatics best-practice analysis pipeline for circRNA quantification, differential expression analysis and miRNA target prediction of RNA-Seq data.

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.

Pipeline summary

1. Read QC (FastQC)
2. Present QC for raw reads (MultiQC)

Workflow

The diagram below gives an overview of the modules in nf-core/circrna:

Pipeline Summary

1. Download reference genome files (Gencode)
2. Download miRNA database files (miRbase, TargetScan)
3. Adapter trimming (BBDUK)
4. Read QC (FastQC)
5. Generate genome indices
6. circRNA quantification
   1. STAR -> CIRCexplorer2
   2. STAR -> circRNA finder
   3. STAR -> DCC
   4. HISAT2 -> CIRI2 -> BWA -> CIRIquant
   5. Bowtie2 -> find circ
   6. Bowtie -> MapSplice
7. miRNA target prediction (miRanda, TargetScan)
8. DESeq2 differential expression analysis (DESeq2)

Ouputs given by each step in the pipeline can be viewed at the output documentation

Quick Start

1. Install Nextflow (>=21.04.0)

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/circrna -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.
   • 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. Alternatively, it is highly recommended to use the nf-core download command to pre-download all of the required containers before running the pipeline and to set the NXF_SINGULARITY_CACHEDIR or singularity.cacheDir Nextflow options to be able 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/circrna -profile <docker/singularity/podman/shifter/charliecloud/conda/institute> --module 'circrna_discovery, mirna_prediction, differential_expression' --tool 'circexplorer2' --input 'samples.csv' --input_type 'fastq' --phenotype 'phenotype.csv'

Documentation

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

Credits

nf-core/circrna was originally written by Barry Digby (@BarryDigby) from the National University of Ireland, Galway as a member of Dr. Pilib Ó Broins lab with the financial support of Science Foundation Ireland (Grant number 18/CRT/6214).

We thank the following people for their extensive assistance in the development of this pipeline:

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

Citations

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.

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

• BBDUK Bushnell, B. (Unpublished). Download: https://sourceforge.net/projects/bbmap/
• bedtools Quinlan, A.R. & Hall, I.M., (2010). BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics , 26(6), pp.841–842. Available at: http://dx.doi.org/10.1093/bioinformatics/btq033. Download: https://github.com/arq5x/bedtools2/releases
• Bowite Langmead, B., Trapnell, C., Pop, M. et al., (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10, R25. Availabe at: https://doi.org/10.1186/gb-2009-10-3-r25. Download: https://sourceforge.net/projects/bowtie-bio/
• Bowtie2 Langmead, B. & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), p. 357–359. Available at: 10.1038/nmeth.1923. Download: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml
• bwa Li, H., & Durbin, R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics , 25(14), 1754–1760. Available at: https://doi.org/10.1093/bioinformatics/btp324. Download: http://bio-bwa.sourceforge.net/bwa.shtml.
• CIRCexplorer2 Zhang XO, Dong R, Zhang Y, Zhang JL, Luo Z, Zhang J, Chen LL, Yang L. (2016). Diverse alternative back-splicing and alternative splicing landscape of circular RNAs. Genome Res. 2016 Sep;26(9):1277-87. Available at: https://doi.org/10.1101/gr.202895.115. Download: https://circexplorer2.readthedocs.io/en/latest/tutorial/setup/
• circRNA finder Westholm, J.O., Lai, E.C., et al. (2016). Genome-wide Analysis of Drosophila Circular RNAs Reveals Their Structural and Sequence Properties and Age-Dependent Neural Accumulation Westholm et al. Cell Reports. Available at: https://doi.org/10.1016/j.celrep.2014.10.062. Download: https://github.com/orzechoj/circRNA_finder
• CIRIquant Zhang, J., Chen, S., Yang, J. et al. (2020). Accurate quantification of circular RNAs identifies extensive circular isoform switching events. Nat Commun 11, 90. Available at: https://doi.org/10.1038/s41467-019-13840-9. Download: https://github.com/bioinfo-biols/CIRIquant
• DCC Jun Cheng, Franziska Metge, Christoph Dieterich, (2016). Specific identification and quantification of circular RNAs from sequencing data, Bioinformatics, 32(7), 1094–1096. Available at: https://doi.org/10.1093/bioinformatics/btv656. Download: https://github.com/dieterich-lab/DCC
• find circ Memczak, S., Jens, M., Elefsinioti, A., Torti, F., Krueger, J., Rybak, A., Maier, L., Mackowiak, S. D., Gregersen, L. H., Munschauer, M., Loewer, A., Ziebold, U., Landthaler, M., Kocks, C., le Noble, F., & Rajewsky, N. (2013). Circular RNAs are a large class of animal RNAs with regulatory potency. Nature, 495(7441), 333–338. Available at: https://doi.org/10.1038/nature11928. Download: https://github.com/marvin-jens/find_circ
• HISAT2 Kim, D., Paggi, J.M., Park, C. et al. (2019). Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019). Available at: https://doi.org/10.1038/s41587-019-0201-4. Download: http://daehwankimlab.github.io/hisat2/download/
• MapSplice2 Wang, K., Liu J., et al. (2010) MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery, Nucleic Acids Research, 38(18), 178. Avaialable at: https://doi.org/10.1093/nar/gkq622. Download: http://www.netlab.uky.edu/p/bioinfo/MapSplice2Download
• miRanda Enright, A.J., John, B., Gaul, U. et al. (2003). MicroRNA targets in Drosophila. Genome Biol 5, R1. Available at: https://doi.org/10.1186/gb-2003-5-1-r1. Download: http://cbio.mskcc.org/miRNA2003/miranda.html.
• Picard Broad Institute (Unpublished). Download: http://broadinstitute.github.io/picard/
• R: R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Download: https://www.R-project.org/.
  • biomaRt Durinck S, Spellman PT, Birney E, Huber W. (2009). Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 4(8):1184-91. Available at: https://doi.org/10.1038/nprot.2009.97. Download: https://bioconductor.org/packages/release/bioc/html/biomaRt.html
  • circlize Zuguang Gu, Lei Gu, Roland Eils, Matthias Schlesner, Benedikt Brors (2014). circlize implements and enhances circular visualization in R , Bioinformatics, 30,(19) 2811–2812. Available at: https://doi.org/10.1093/bioinformatics/btu393. Download: https://cran.r-project.org/web/packages/circlize/index.html
  • DESeq2 Love, M.I., Huber, W. & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550. Available at: https://doi.org/10.1186/s13059-014-0550-8. Download: https://bioconductor.org/packages/release/bioc/html/DESeq2.html
  • EnhancedVolcano Blighe K, Rana S, Lewis M (2020). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. Download: https://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html
  • ggplot2 Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, Download: https://ggplot2.tidyverse.org.
  • ggpubr Kassambara A. (2020). ggpubr: 'ggplot2' Based Publication Ready Plots. Download: https://rpkgs.datanovia.com/ggpubr/
  • ihw Ignatiadis, N., Klaus, B., Zaugg, J. et al. (2016). Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat Methods 13, 577–580. Available at: https://doi.org/10.1038/nmeth.3885. Download: https://bioconductor.org/packages/release/bioc/html/IHW.html
  • PCAtools Blighe K, Lun A (2020). PCAtools: PCAtools: Everything Principal Components Analysis. Download: https://bioconductor.org/packages/release/bioc/html/PCAtools.html
  • pheatmap Kolde, R. (2019) Pretty Heatmaps. Download: https://cran.r-project.org/package=pheatmap
  • pvclust Suzuki R., Shimodaira H., (2006). Pvclust: an R package for assessing the uncertainty in hierarchical clustering, Bioinformatics, 22(12), 1540–1542. Available at: https://doi.org/10.1093/bioinformatics/btl117. Download: https://cran.r-project.org/web/packages/pvclust/index.html
  • tximport Soneson C, Love MI, Robinson MD (2015). Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Research, 4. Avaialable at: https://doi.org/10.12688/f1000research.7563.1. Download: http://bioconductor.org/packages/release/bioc/html/tximport.html
• SAMtools Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., … 1000 Genome Project Data Processing Subgroup. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics , 25(16), 2078–2079. https://doi.org/10.1093/bioinformatics/btp352. Download: http://www.htslib.org/
• STAR Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics (Oxford, England), 29(1), 15–21. Available at: https://doi.org/10.1093/bioinformatics/bts635. Download: https://github.com/alexdobin/STAR
• StringTie Pertea, M., Pertea, G., Antonescu, C. et al. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol 33, 290–295. Available at: https://doi.org/10.1038/nbt.3122. Download: https://ccb.jhu.edu/software/stringtie/
• TargetScan Agarwal V, Bell GW, Nam JW, Bartel DP. (2015). Predicting effective microRNA target sites in mammalian mRNAs. Elife, 4:e05005. Available at: https://doi.org/10.7554/elife.05005. Download: http://www.targetscan.org/cgi-bin/targetscan/data_download.vert72.cgi
• ViennaRNA Lorenz, R., Bernhart, S.H., Höner zu Siederdissen, C. et al. (2011). ViennaRNA Package 2.0. Algorithms Mol Biol 6, 26. Available at: https://doi.org/10.1186/1748-7188-6-26. Download: https://www.tbi.univie.ac.at/RNA/#download

Data References

This repository generated test data using:

• CIRI_simulator.pl Gao, Y., Wang, J. & Zhao, F. (2015). CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol 16, 4. Available at: https://doi.org/10.1186/s13059-014-0571-3. Download: https://sourceforge.net/projects/ciri/