neopred-nf

Nextflow implementation of Neoantigen prediction with HLA-typing grouping matched normal-tumor WGS/WES and tumor RNA-seq

https://github.com/akazhiel/neopred-nf

Science Score: 31.0%

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

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
  • .zenodo.json file
  • DOI references
    Found 7 DOI reference(s) in README
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (14.2%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Nextflow implementation of Neoantigen prediction with HLA-typing grouping matched normal-tumor WGS/WES and tumor RNA-seq

Basic Info
  • Host: GitHub
  • Owner: Akazhiel
  • License: mit
  • Language: Nextflow
  • Default Branch: master
  • Size: 2.16 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed over 2 years ago
Metadata Files
Readme Changelog License Code of conduct Citation

README.md

akazhiel/neoprednf

Nextflow run with docker

Introduction

akazhiel/neoprednf is a bioinformatics best-practice analysis pipeline for Pipeline that performs variant calling on DNA Tumor-Normal samples and RNA Tumor to predict putative neoantigens that bind to the hla of the patient..

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.

Pipeline summary

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

Quick Start

  1. Install Nextflow (>=21.04.0)

  2. Install any of Docker, for full pipeline reproducibility.

    • 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.
  3. Start running your own analysis!

    console nextflow run akazhiel/neoprednf -profile docker --input samplesheet.csv --genome GRCh37 --refDir /path/to/reference

Credits

akazhiel/neoprednf was originally written by Jonatan Gonzalez Rodriguez.

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

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: Jonatan Gonzalez Rodriguez
  • Login: Akazhiel
  • Kind: user
  • Location: Barcelona
  • Company: Vall d'Hebron Institute of Oncology (VHIO)

Citation (CITATIONS.md)

# Akazhiel/NeoPred-NF: 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/)

* [MultiQC](https://www.ncbi.nlm.nih.gov/pubmed/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)

* [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.

GitHub Events

Total
Last Year