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eukavarizer

https://github.com/lupphes/eukavarizer

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    Found 7 DOI reference(s) in README
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    Scientific vocabulary similarity
    Low similarity (11.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: Lupphes
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Size: 747 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

nf-core/eukavarizer

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

Nextflow run with conda run with docker

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Introduction

nf-core/eukavarizer is a modular and reproducible bioinformatics pipeline designed for the detection and analysis of structural variants (SVs) in eukaryotic genomes. The pipeline supports both short- and long-read data, integrates a variety of SV callers (e.g., GRIDSS, DELLY, Sniffles, CuteSV), and unifies the results for further analysis. Key features include:

  • Reference genome retrieval from RefSeq or use of a local reference
  • Automated FASTQ preprocessing and alignment (BWA or Minimap2)
  • Modular SV calling with customizable parameters
  • Merging and filtering of SVs using SURVIVOR and BCFtools
  • Visual and statistical reporting

Suitable for diverse eukaryotic organisms, from yeast to mammals.

Workflow Overview

  1. Reference genome retrieval or usage of user-provided genome
  2. Read QC and preprocessing
  3. Mapping using BWA or Minimap2 (long-read aware)
  4. SV calling via one or more tools:
    • Short-read: DELLY, GRIDSS, TIDDIT, Manta
    • Long-read: Sniffles, CuteSV
  5. SV merging & filtering with SURVIVOR and BCFtools
  6. Report generation (including MultiQC and HTML summaries)

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.

Quick Start

Minimal example:

bash nextflow run nf-core/eukavarizer \ -profile docker,short_quick \ --taxonomy_id 4932 \ --outdir results/

Local input example:

bash nextflow run nf-core/eukavarizer \ -profile docker,short_full \ --taxonomy_id 4932 \ --reference_genome ./data/4932/ref/genome.fa.gz \ --sequence_dir ./data/4932/ena/ \ --outdir results/

[!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.

Profiles and Parameters

The pipeline provides several pre-defined profiles to optimise analysis based on input read types and analysis depth. Combine these with a compute environment profile such as docker, conda, mamba, or kube.

Read Type and Analysis Depth Profiles

  • short_quick, short_medium, short_full: For short-read data
  • long_quick, long_medium, long_full: For long-read data
  • mix_quick, mix_medium, mix_full: For hybrid/mixed sequencing

Each level adjusts:

  • Enabled SV callers
  • Tool-specific arguments
  • Filtering thresholds for SURVIVOR and BCFtools

Compute Environment Profiles

  • docker: Uses Docker containers
  • conda / mamba: Uses Conda or Mamba for software installation
  • kube: For Kubernetes environments

Use -profile docker,short_full for a full analysis on short-read data with Docker.

Important Parameters

| Parameter | Description | | ------------------ | --------------------------------------------- | | taxonomy_id | NCBI Taxonomy ID for reference retrieval | | sequence_dir | Path to directory with raw sequence files | | reference_genome | Path to a FASTA file for the reference genome | | outdir | Output directory for results |

Pipeline output

The pipeline produces the following main outputs:

  • multiqc_report.html: quality control summary
  • report.html: merged SVs and summary statistics
  • plots/: additional per-caller plots and images
  • VCF files (per caller and merged)

Credits

nf-core/eukavarizer was originally written by Ondrej Lupphes Sloup.

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 #eukavarizer 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.

Owner

  • Name: Ondřej Sloup
  • Login: Lupphes
  • Kind: user
  • Location: Prague
  • Company: Red Hat

Citation (CITATIONS.md) 

# nf-core/eukavarizer: 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

- [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.

- [fastp](https://doi.org/10.1093/bioinformatics/bty560)

> 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.

> Chen S. (2023). Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta, 2:e107. https://doi.org/10.1002/imt2.107

- [fastplong]

> Shifu Chen. 2023. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta 2: e107. https://doi.org/10.1002/imt2.107

> Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu; fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i884–i890, https://doi.org/10.1093/bioinformatics/bty560

- [SVYNC](https://github.com/nvnieuwk/svync)

> Vannieuwkerke N. (2024). SVYNC: Structural Variant Integrator. https://github.com/nvnieuwk/svync

- [BWA](https://doi.org/10.1093/bioinformatics/btp324)

> Li H, Durbin R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 25(14), 1754–1760.

- [BWA-MEM2](https://arxiv.org/abs/1907.12931)

> Vasimuddin M, Misra S, Li H, Aluru S. (2019). Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems. arXiv:1907.12931

- [Minimap2](https://doi.org/10.1093/bioinformatics/bty191)

> Li H. (2018). Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34(18), 3094-3100.

- [SAMtools](https://doi.org/10.1093/bioinformatics/btp352)

> Li H, Handsaker B, Wysoker A, et al. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079.

- [BCFtools](https://doi.org/10.1093/gigascience/giab008)

> Danecek P, Bonfield JK, Liddle J, et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008.

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

> Bushnell B. (2014). BBMap: A fast, accurate, splice-aware aligner. https://sourceforge.net/projects/bbmap/

- [seqtk](https://github.com/lh3/seqtk)

> Li H. (2012). seqtk: a fast and lightweight tool for processing sequences in the FASTA or FASTQ format. https://github.com/lh3/seqtk

- [DELLY](https://doi.org/10.1093/bioinformatics/bts378)

> Rausch T, Zichner T, Schlattl A, Stüzer A, Benes V, Korbel JO. (2012). DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics, 28(18), i333-i339.

- [GRIDSS](https://doi.org/10.1101/gr.222109.117)

> Cameron DL, Di Stefano L, Papenfuss AT. (2017). GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly. Genome Res., 27(12), 2050-2060.

- [TIDDIT](https://doi.org/10.12688/f1000research.11501.1)

> Eisfeldt J, Vezzi F, Olason P, Nilsson D, Lindstrand A. (2017). TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data. F1000Research, 6:664.

- [Dysgu](https://doi.org/10.1093/bioinformatics/btac144)

> Cawte N, Stadler PF, Simmonds MJ. (2022). Dysgu: a tool for structural variant detection from short-read sequencing data. Bioinformatics, 38(9), 2465-2471.

- [Sniffles](https://doi.org/10.1038/s41592-018-0001-7)

> Sedlazeck FJ, Rescheneder P, Smolka M, et al. (2018). Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods, 15(6), 461-468.

- [CuteSV](https://doi.org/10.1186/s13059-020-02107-y)

> Jiang T, Liu Y, Jiang Y, et al. (2020). Long-read-based human genomic structural variation detection with cuteSV. Genome Biol., 21:189.

- [SvABA](https://doi.org/10.1101/gr.221028.117)

> Wala JA, Bandopadhayay P, Greenwald NF, et al. (2018). SvABA: genome-wide detection of structural variants and indels by local assembly. Genome Res., 28(4), 581–591.

- [Manta](https://doi.org/10.1093/bioinformatics/btv710)

> Chen X, Schulz-Trieglaff O, Shaw R, et al. (2016). Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics, 32(8), 1220–1222.

- [SURVIVOR](https://doi.org/10.1038/ncomms14061)

> Jeffares DC, Jolly C, Hoti M, et al. (2017). Transient structural variations have strong effects on quantitative traits and reproductive isolation in fission yeast. Nat Commun., 8, 14061.

- [Varify]

> Sloup O. et al. (2024). Varify: structural variant analysis summary and visualization. https://github.com/Lupphes/Varify

- [SRA](https://doi.org/10.1093/nar/gkq1019)

> Leinonen R, Sugawara H, Shumway M; International Nucleotide Sequence Database Collaboration. (2011). The Sequence Read Archive. Nucleic Acids Res., 39(Database issue): D19–D21.

- [BioDbCore](https://github.com/luppo/biodbcore)

> Sloup O. (2024). BioDbCore: ENA/RefSeq retrieval module. https://github.com/luppo/biodbcore

- [coreutils](https://www.gnu.org/software/coreutils/)

> GNU Project. coreutils (including gunzip). https://www.gnu.org/software/coreutils/

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