Juno-SNP

SNP analysis pipeline.

Pipeline information

Author(s): Alejandra Hernández Segura
Organization: Rijksinstituut voor Volksgezondheid en Milieu (RIVM)
Department: Infektieziekteonderzoek, Diagnostiek en Laboratorium Surveillance (IDS), Bacteriologie (BPD)
Start date: 07 - 03 - 2022

About this project

The goal of this pipeline is to perform SNP analysis. For that, it requires:

Two ‘.fastq’ files (paired-end sequencing) derived from short-read sequencing. They should be already filtered and trimmed (for instance, with the Juno-assembly pipeline).
An assembly from the same sample in the form of a single ‘.fasta’ file.

Importantly, the Juno-SNP pipeline works directly on output generated by the Juno-Assembly pipeline.

The Juno-SNP pipeline will then perform the following steps:

If no reference genome is provided, it will look for one in the NCBI database. This is done with the help of the referenceseeker package
If step 1 was performed, it will download the appropriate genome from the NCBI database
It will perform the SNP calling on every sample that was given using Snippy
It will calculate the distance matrix and produce a Newick file for the "Neighbor Joining" tree for the given samples using GrapeTree.

By default, most rules will be run using containers.

Prerequisities

Linux + mamba/conda A Linux-like environment with at least 'miniconda' installed.
Preferentially, Singularity. See instructions for running the pipeline if you don't have singularity installed.

Installation

Clone the repository:

git clone https://github.com/RIVM-bioinformatics/juno-snp.git

Alternatively, you can download it manually as a zip file (you will need to unzip it then).

Enter the directory with the pipeline and install the master environment:

cd juno-snp
conda env update -f envs/master_env.yaml

Parameters & Usage

Command for help

-h, --help Shows the help of the pipeline

Required parameters

-i, --input Directory with the input (fasta) files. The fasta files should be all in this directory (no subdirectories) and have the extension '.fasta'.

Optional parameters

  -i DIR, --input DIR   Relative or absolute path to the input directory. It must
                        either be the output directory of the Juno-assembly pipeline
                        or it must contain all the raw reads (fastq) and assemblies
                        (fasta) files for all samples to be processed. (default:
                        None)
  -r FILE, --reference FILE
                        Relative or absolute path to a reference fasta file.
                        (default: None)
  -o DIR, --output DIR  Relative or absolute path to the output directory. If non is
                        given, an 'output' directory will be created in the current
                        directory. (default: output)
  -d DIR, --db-dir DIR  Relative or absolute path to the database directory. If non
                        is given, /mnt/db/juno/snp (where the default db resides at
                        the RIVM will be used). (default: /mnt/db/juno/snp)
  -a INT, --ani INT     ANI threshold. Passed to referenceseeker (default: 0.95)
  -cd INT, --conserved-dna INT
                        Conserved DNA threshold. Passed to referenceseeker (default:
                        0.69)
  -sw INT, --sliding-window INT
                        Sliding window - the lower the more accurate but also slower.
                        Passed to referenceseeker (default: 400)
  -kl INT, --kmer-length INT
                        K-mer length - longer kmers increase specificity, shorter kmers increase sensitivity. Passed to mash sketch (default: 21)
  -ss INT, --sketch-size INT
                        Sketch size - larger sketch size better represents the original sequence, but leads to large files and longer running time. Passed to mash sketch (default: 1000)
  -mt FLOAT, --mash-threshold FLOAT
                        Mash threshold - maximum mash distance to consider genomes similar. Passed to preclustering script. (default: 0.01)
  -t ALGORITHM, --tree-algorithm ALGORITHM
                        Algorithm to use for making the tree. It can be 'upgma' or
                        'nj' (neighbor-joining). Default is upgma (default: upgma)
  --no-containers       Use conda environments instead of containers. (default: True)
  -p PATH, --prefix PATH
                        Conda or singularity prefix. Path to the place where you want
                        to store the conda environments or the singularity images.
                        (default: None)
  -c INT, --cores INT   Number of cores to use. Default is 300 (default: 300)
  -q STR, --queue STR   Name of the queue that the job will be submitted to if
                        working on a cluster. (default: bio)
  -l, --local           Running pipeline locally (instead of in a computer cluster).
                        Default is running it in a cluster. (default: False)
  -w INT, --time-limit INT
                        Time limit per job in minutes (passed as -W argument to
                        bsub). Jobs will be killed if not finished in this time.
                        (default: 60)
  -u, --unlock          Unlock output directory (passed to snakemake). (default:
                        False)
  -n, --dryrun          Dry run printing steps to be taken in the pipeline without
                        actually running it (passed to snakemake). (default: False)
  --rerunincomplete     Re-run jobs if they are marked as incomplete (passed to
                        snakemake). (default: False)
  --snakemake-args [SNAKEMAKE_ARGS ...]
                        Extra arguments to be passed to snakemake API (https://snakem
                        ake.readthedocs.io/en/stable/api_reference/snakemake.html).
                        (default: {})

The base command to run this program.

If you want the pipeline to choose a reference genome for you, run the command below.

By default, genomes will be clustered using mash and genomes that are too different are split into separate clusters. Mapping will subsequently be performed per cluster. This prevents isolates that are too diverse to be compared.

python juno_snp.py -i [path/to/input_directory]

If you want to provide your own reference genome, specify this with --reference. This will force all isolates to be mapped against the provided reference genome.

python juno_snp.py -i [path/to/input_directory] --reference [path/to/reference.fasta]

An example on how to run the pipeline.

python juno_snp.py -i my_input_files -o my_results --db_dir my_db_dir --local --cores 2

Explanation of the output

log: Log files with output and error files from each Snakemake rule/step that is performed.
audit_trail: Information about the versions of software and databases used.
output per sample: The pipeline will create one subfolder per each step performed (find_reference, ref_genome_used, snp_analysis, tree).

Issues

All default values have been chosen to work with the RIVM Linux environment, therefore, there might not be applicable to other environments (although they should work if the appropriate arguments/parameters are given).
Any issue can be reported in the Issues section of this repository.

Future ideas for this pipeline

License

This pipeline is licensed with an AGPL3 license. Detailed information can be found inside the 'LICENSE' file in this repository.

Contact

Contact person: Roxanne Wolthuis
Email roxanne.wolthuis@rivm.nl