nf-core/uno is a bioinformatics pipeline for the co-assembly, binning, and read mapping of mNGS outbreak samples from the The UnO project
UnO is an mNGS bioinformatics pipeline that supports The UnO Project. nf-core UnO is the second tier The UnO project which analyzes set of mNGS reads from a well-characterized outbreak. The draft UnO pipeline takes as input outbreak sets of mNGS reads. Reads are co-assembled into a single outbreak assembly and then binned into Metagenomic Assembled Genomes (MAGs). Individual mNGS reads are mapped back to MAGs to identify individual sample contributions. MAGs in common across individual mNGS reads are chosen for further analysis and characterization.
- Inital reference-based taxonomic classfication of reads is performed using (
MIDAS2) - Read QC (
FastQC) - Perform quality and adapter trimming on raw reads (
Trimmomatic) - Read QC on trimmed reads
- Co-assemble mNGS reads from outbreak dataset into single outbreak assembly (
Megahit) - Preparation for binning of metagenomic co-assembly with (
Bowtie2). Outbreak co-assembly is used to create an index which individual reads are mapped to determine depth information for downstream binning tools. - (
MetaBat2) and (MaxBin2) are used to bin MAGs. - (
DAStool) is used to refine bins. - (
Bowtie2) is used to map individual reads back to refined bins to identify MAGs in common across outbreak samples. - (
CheckM) is used to asses quality of refined bins. - (
MultiQC) is used to summarize some of the findings and software versions.
Running nf-uno requires Nextflow (>=21.10.3) and conda to be installed. There are detailed instructions below for Nextflow installation, including Nextflow's Bash and Java requirements.
Conda is required for the MIDAS2 process therefore, we recommend using conda for all required dependencies.
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.
For conda installation, please refer to this page.
When Nextflow and conda are installed, clone the pipeline:
git clone https://github.com/uel3/nf-core-unoNext, test it on the minimal dataset provided using the test.config:
nextflow run nf-core-uno -profile conda,test --outdir <OUTDIR>Next, prepare a samplesheet with your input data that looks as follows:
samplesheet.csv:
sample,group,short_reads_1,short_reads_2,
SAMPLE1,UnO,/path/to/reads/SAMPLE1_1.fastq.gz,/path/to/reads/SAMPLE1_2.fastq.gz,
SAMPLE2,UnO,/path/to/reads/SAMPLE2_1.fastq.gz,/path/to/reads/SAMPLE2_2.fastq.gz,
SAMPLE3,UnO,/path/to/reads/SAMPLE3_1.fastq.gz,/path/to/reads/SAMPLE3_2.fastq.gz,
Each row represents a pair of fastq files (paired end).
Now, you can run the pipeline using:
nextflow run nf-core-uno \
-profile <conda/institute> \
--input samplesheet.csv \
--outdir <OUTDIR>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.
Current output of the draft UnO output consists of the user specified with following directories: Assembly, FastQC, GenomeBinning, multiqc, pipeline_info, QC_shortreads, and Trimmomatic. For more details about the output files and reports, please refer to the output documentation.
uel3/uno is adapted by Candace Cole from nf-core/mag, written by Hadrien Gourlé at SLU, Daniel Straub and Sabrina Krakau at the Quantitative Biology Center (QBiC).
We thank the following people for their extensive assistance in the development of this pipeline:
Andrew Huang
Rong Jin
If you would like to contribute to this pipeline, please see the contributing guidelines.
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.
This repository constitutes a work of the United States Government and is not subject to domestic copyright protection under 17 USC § 105. This repository is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication. All contributions to this repository will be released under the CC0 dedication. By submitting a pull request you are agreeing to comply with this waiver of copyright interest.
This repository contains only non-sensitive, publicly available data and information. All material and community participation is covered by the Disclaimer and Code of Conduct. For more information about CDC's privacy policy, please visit http://www.cdc.gov/other/privacy.html.
Anyone is encouraged to contribute to the repository by forking and submitting a pull request. (If you are new to GitHub, you might start with a basic tutorial.) By contributing to this project, you grant a world-wide, royalty-free, perpetual, irrevocable, non-exclusive, transferable license to all users under the terms of the Apache Software License v2 or later.
All comments, messages, pull requests, and other submissions received through CDC including this GitHub page may be subject to applicable federal law, including but not limited to the Federal Records Act, and may be archived. Learn more at http://www.cdc.gov/other/privacy.html.
This repository is not a source of government records, but is a copy to increase collaboration and collaborative potential. All government records will be published through the CDC web site.