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nf-core-rsvseq

https://github.com/rasmuskoriis/nf-core-rsvseq

Science Score: 44.0%

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    Low similarity (9.3%) to scientific vocabulary
Last synced: 11 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: RasmusKoRiis
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Size: 2.19 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 1 year ago
Metadata Files
Readme Changelog Contributing License Code of conduct Citation

README.md

rsvseq :high_brightness:

Nextflow run with docker

Introduction

This pipeline processes FASTQ files from Nanopore sequencing of RSV A and B, generating consensus sequences and analyzing them for mutations and sequencing statistics The main steps include:

  • Alignment and consensus sequencing with IRMA.
  • Consensus sequence analysis with Nextclade.
  • Mutation calling
  • Generation of a comprehensive report in CSV format.
  • Output of sequences in a multiple FASTA file.

The pipeline consist of four different worflows listed bellow:

1) RSV A and B FASTQ analysis (human) Alignment of FASTQ and mutation analysis 2) RSV A and B FASTA analysis (human-fasta) (under development) Mutation analysis

Compatibility

  • Operating System: Linux
  • Dependencies: Docker and Nextflow

Usage

Sample Sheet Preparation

Prepare a sample sheet (CSV or TSV*) in the assets folder with the following format: * TSV file is not not compulsory

PCR-PlatePosition,SequenceID,Barcode,KonsCt A1*,sampleID,barcodeID,ct-value* *not compulsory

Each row lists a sample to be analyzed. Samples not listed in the sheet will be excluded from the analysis.

Directory Structure

For FASTQ-analysis

Ensure your directory structure is as follows:

./ |-data |-barcode3 |-XXXX_pass_barcode03_XXXX.fastq.gz |-YYYY_pass_barcode03_YYYY.fastq.gz |-nf-core-rsv |-assets |-samplesheet.csv |-samplesheet.tsv |-...

Running the Pipeline

Navigate to the nf-core-rsv folder and execute the following command with default parameters:

bash nextflow run main.nf -profile docker --runid runid_name --input samplesheet.csv --outdir ../outdir_name

Important Parameters

  • --input (default: assets/samplesheet.csv): Path to the samplesheet.
  • --samplesDir (default: ../data): Directory containing the FASTQ files in the structure given above.

All parameters are detailed in the nextflow.config file.

Pipeline Output

The output includes:

  • Consensus sequences.
  • Mutation calls.
  • Sequencing statistics (coverage, quality parameters).
  • A report in CSV format.
  • A multiple FASTA file of sequences that passed quality filters.

Credits

rsvseq was originally written by Rasmus Kopperud Riis.

Owner

  • Login: RasmusKoRiis
  • Kind: user

Citation (CITATIONS.md) 

# nf-core/rsvseq: 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.

## 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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