NanoASV is a conda environment snakemake based workflow using state of the art bioinformatic softwares to process full-length SSU rRNA (16S/18S) amplicons acquired with Oxford Nanopore Sequencing technology. Its strength lies in reproducibility, portability and the possibility to run offline. It can be installed on the Nanopore MK1C sequencing device and process data locally.
Usage: nanoasv -d path/to/dir -o path/to/output [--options]
| Option | Description |
| -------------------- | ---------------------------------------------------------------------|
| `-h`, `--help` | Show help message |
| `-v`, `--version` | Show version information |
| `-d`, `--dir` | Path to demultiplexed barcodes | |
| `-db`, `--database` | Path to reference fasta file |
| `-q`, `--quality` | Quality threshold for Chopper, default: 8 |
| `-l`, `--minlength` | Minimum amplicon length for Chopper, default: 1300 |
| `-L`, `--maxlength` | Maximum amplicon length for Chopper, default: 1700 |
| `-i`, `--id-vsearch` | Identity threshold for vsearch clustering step, default: 0.7 |
| `-ab`, `--minab` | Minimum unknown cluster total abundance to be kept |
| `-p`, `--num-process`| Number of cores for parallelization, default: 1 |
| `--subsampling` | Max number of sequences per barcode, default: 50,000 |
| `--no-r-cleaning` | Flag - to keep Eukaryota, Chloroplast, and Mitochondria sequences |
| | from phyloseq object |
| `--metadata` | Specify metadata.csv file directory, default is --dir |
| `--notree` | Flag - To remove phylogeny step and tree from phyloseq object |
| `--sam-qual` | To tune samtools filtering quality threshold, default: 30 |
| `--requirements` | Flag - To display personal reference fasta requirements |
| `--dry-run` | Flag - NanoASV Snakemake dry run |
| `--mock` | Flag - Run mock dataset with NanoASV |
| `--remove-tmp` |To remove tmp data after execution. No snakemake resume option if set.|
(to install NanoASV on Oxford Nanopore MK1C sequencing devices, see section ONT MK1C Installation)
Clone the repository from github:
cd ${HOME}
git clone https://github.com/ImagoXV/NanoASV.gitRun the installation script:
bash ${HOME}/NanoASV/config/install.shThen activate the environment:
conda activate NanoASVDon't forget to activate the environment before running nanoasv. It
will not work otherwise.
NanoASV can be used with any reference fasta file. If you want to have a broad idea of your community taxonomy, we recommend you to use latest Silva.
Download the database and put it in ./resources/:
RELEASE=138.2
URL="https://www.arb-silva.de/fileadmin/silva_databases/release_${RELEASE}/Exports"
INPUT="SILVA_${RELEASE}_SSURef_NR99_tax_silva.fasta.gz"
OUTPUT="SINGLELINE_${INPUT/_NR99/}"
FOLDER="resources"
mkdir -p "${FOLDER}"
echo "downloading and formating SILVA reference, this will take a few minutes."
wget --output-document - "${URL}/${INPUT}" | \
gunzip --stdout | \
awk '/^>/ {printf("%s%s\n", (NR == 1) ? "" : RS, $0) ; next} {printf("%s", $0)} END {printf("\n")}' | \
gzip > "./${FOLDER}/${OUTPUT}"
unset RELEASE URL INPUT OUTPUTnanoasv --dry-runnanoasv --mockYou can inspect NanoASV's output structure in ./Mock_run_OUPUT/.
You need to use the aarch64-MK1C branch, otherwise, it will not work.
You need to install
miniconda. Note that /data/
will be used for installation for storage capacity matters.
mkdir -p /data/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-aarch64.sh -O /data/miniconda3/miniconda.sh --no-check-certificate
bash /data/miniconda3/miniconda.sh -b -u -p /data/miniconda3
rm /data/miniconda3/miniconda.sh
source /data/miniconda3/bin/activateThen proceed to install conda.
Chopper needs to be Aarch64 compiled. Therefore, you need to download this specific archive or a newer one if someone cross-compiles it.
Warning, don't setup NanoASV environment from the conda (base)
environment. Otherwise you'll run into issues.
cd /data/
git clone \
--branch origin/aarch64-MK1C-conda \
--single-branch https://github.com/ImagoXV/NanoASV.git
cd ./NanoASV/
conda deactivate
conda env create -f environment.yml
(
cd ./config/
wget https://github.com/wdecoster/chopper/releases/download/v0.7.0/chopper-aarch64.zip
unzip chopper-aarch64.zip
)
ROOT_DIR="$(conda env list | grep -w 'NanoASV' | awk '{print $2}')"
ACTIVATE_DIR="${ROOT_DIR}/etc/conda/activate.d"
cp ./config/{alias,paths}.sh ${ACTIVATE_DIR}/
echo "export NANOASV_PATH=$(pwd)" >> ${ACTIVATE_DIR}/paths.sh
DEACTIVATE_DIR="${ROOT_DIR}/etc/conda/deactivate.d"
cp ./config/unalias.sh ${DEACTIVATE_DIR}/
chmod +x ./workflow/run.shconda create --name R-phyloseq -c bioconda -c conda-forge bioconductor-phyloseq
conda activate R-phyloseq
Rscript -e 'install.packages("dplyr", repos = "https://cran.r-project.org")'
conda deactivatenanoasv --mockDirectly input your /path/to/sequence/data/fastq_pass directory
4000 sequences fastq.gz files are concatenated by barcode identity to make one barcodeXX.fastq.gz file.
Chopper will filter for inappropriate sequences.
Is executed in parallel (default --num-process 1)
Default parameters will filter for sequences with quality > 8 and 1300bp < length < 1700bp
There is no efficient chimera detection step at the moment.
Porechop will trim known adapters
Is executed in parallel (default --num-process 1)
50 000 sequences per barcode is enough for most common questions.
Default is set to 50 000 sequences per barcode.
Can be modified with --subsampling int
minimap2 will align previously filtered sequences against the reference dataset (SILVA 138.2 by default)
Can be executed in parallel (default --num-process 1)
barcode*_abundance.tsv, Taxonomy_barcode*.csv and barcode*_exact_affiliations.tsv like files are produced.
Those files can be found in the ./Results/ directory.
Non matching sequences fastq are extracted then clustered with vsearch (default --id 0.7).
Clusters with abundance under 5 are discarded to avoid useless heavy computing.
Outputs into ./Results/Unknown_clusters
Reference ASV sequence from fasta reference file are extracted accordingly to detected entities.
Unknown OTUs seed sequence are added. The final file is fed to FastTree to produce a tree file
Tree file is then implemented into the final phyloseq object.
This allows for phylogeny of unknown OTUs and 16S based phylogeny taxonomical estimation of the entity.
This step can be avoided with the --notree option.
Alignments results, taxonomy, clustered unknown entities and 16S based phylogeny tree are used to produce a phyloseq object: NanoASV.rdata
Please refer to the metadata.csv file in Minimal dataset to be sure to input the correct file format for phyloseq to produce a correct phyloseq object.
You can choose not to remove Eukaryota, Chloroplasta and Mitochondria sequences (pruned by default) using --r_cleaning 0
A CSV file encompassing taxonomy and abundance is produced as well and stored into ./Results/CSV.
We thank Antoine Cousson, Fiona Elmaleh and Meren for their time and energy with NanoASV beta testing!
Please don't forget to cite NanoASV and dependencies if it helped you treat your Nanopore data Thank you!
Dependencies citations :
Danecek, Petr, James K Bonfield, Jennifer Liddle, John Marshall, Valeriu Ohan, Martin O Pollard, Andrew Whitwham, et al. 2021. “Twelve Years of SAMtools and BCFtools.” GigaScience 10 (2): giab008. https://doi.org/10.1093/gigascience/giab008.
De Coster, Wouter, and Rosa Rademakers. 2023. “NanoPack2: Population-Scale Evaluation of Long-Read Sequencing Data.” Edited by Can Alkan. Bioinformatics 39 (5): btad311. https://doi.org/10.1093/bioinformatics/btad311.
Li, Heng. 2018. “Minimap2: Pairwise Alignment for Nucleotide Sequences.” Edited by Inanc Birol. Bioinformatics 34 (18): 3094–3100. ttps://doi.org/10.1093/bioinformatics/bty191.
Katoh, K., and D. M. Standley. 2013. “MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability.” Molecular Biology and Evolution 30 (4): 772–80. https://doi.org/10.1093/molbev/mst010.
McMurdie, Paul J., and Susan Holmes. 2013. “Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data.” Edited by Michael Watson. PLoS ONE 8 (4): e61217. https://doi.org/10.1371/journal.pone.0061217.
Nygaard, Anders B., Hege S. Tunsjø, Roger Meisal, and Colin Charnock. 2020. “A Preliminary Study on the Potential of Nanopore MinION and Illumina MiSeq 16S rRNA Gene Sequencing to Characterize Building-Dust Microbiomes.” Scientific Reports 10 (1): 3209. https://doi.org/10.1038/s41598-020-59771-0.
Price, M. N., P. S. Dehal, and A. P. Arkin. 2009. “FastTree: Computing Large Minimum Evolution Trees with Profiles Instead of a Distance Matrix.” Molecular Biology and Evolution 26 (7): 1641–50. https://doi.org/10.1093/molbev/msp077.
Quast, Christian, Elmar Pruesse, Pelin Yilmaz, Jan Gerken, Timmy Schweer, Pablo Yarza, Jörg Peplies, and Frank Oliver Glöckner. 2012. “The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools.” Nucleic Acids Research 41 (D1): D590–96. https://doi.org/10.1093/nar/gks1219.
Rognes, Torbjørn, Tomáš Flouri, Ben Nichols, Christopher Quince, and Frédéric Mahé. 2016. “VSEARCH: A Versatile Open Source Tool for Metagenomics.” PeerJ 4 (October): e2584. https://doi.org/10.7717/peerj.2584.