phap - Phage Host Analysis Pipeline

A snakemake workflow that wraps various phage-host prediction tools.

When possible, tools and their dependencies are bundled in Singularity containers.

Current tools

Installation

Dependencies

To run the workflow your will need

• snakemake > 5.x (developed with 5.30.1)
• singularity >= 3.6 (developed with 3.6.3)
• biopython >= 1.78 (developed with 1.78)

Conda environemnt

It is recommended to use a conda environment. The file environment.txt can be used to recreate the complete environment used during development.

The provided environment.txt contains an explicit list of all packages, produced with conda list -n hp --explicit > environment.txt . This ensures all packages are exactly the same versions/builds, so we minimize the risk of running into dependencies issues

To get a working environment

# Clone this repo
$ git clone https://git.science.uu.nl/papanikos/phap.git

# Get in the dir
$ cd phap

# I am naming the environment `phap` here, you can call it whatever you like
# Note the long notation --file flag; -f will not work.
$ conda create -n phap --file=environment.txt

# Activate it - use the name you gave above, if it is different
$ conda activate phap

# The (hp) prefix shows we have activated it
# Check the snakemake version
(phap) $ snakemake --version
5.30.1

Configuration

Input data

The tools wrapped in this workflow expect phage sequences as input. You should try to make sure that the input sequences you want to analyze correspond to phage genomes/contigs (or at least viruses).

You can probably input any valid fasta file but the GIGO concept is probably applicable.

A separate workflow to identify phage/viral genomes/contigs is What the Phage.

The current workflow can handle multiple samples. For each sample, all viral contigs to be analyzed should be provided as a single multifasta (can be gzipped). A mapping between sample ids and their corresponding fasta file is provided as a samplesheet (see below).

Sample sheet

You must define a samplesheet with two comma (,) separated columns and the header sample,fasta. Values from the sample column must be unique and are used as sample identifiers. Their corresponding fasta values must be valid paths to multifasta files with the phage sequences for that sample.

An example

$ cat samples.csv
sample,fasta
s01,/path/to/s01.fna
s02,/path/to/another.fna.gz

Note There is no need to follow any convention for the fasta file name to reflect the sample id. The values in the sample column are the ones to worry about, as these are the ones used as wildcards within the Snakefile.

You can

• Fill in the location of the samplesheet within the config.yml.
• Drop the file in the workdir - Attention: It should be named samples.csv
• Use snakemake's --config samplesheet=/path/to/my_samples.csv when executing the wofkflow.

Models and data dependencies

• RaFaH, vHULK For these tools there is no need to pre-download and setup anything - all data and software dependencies required for running them are bundled within the singularity image.

• VirHostMatcher-Net, WIsH

Databases and models need to be downloaded from the VirHostMatcher data repo (see here). WIsH models for the 62,493 host genomes used in their paper are also provided and are used here for WIsH predictions.

Usage

Basic:

# From within this directory
# Make sure you have defined a samplesheet
(hp)$ snakemake --use-singularity -j16 \
      --singularity-args "-B /path/to/databases/:/data"

where /path/to/database/ is the directory containing tables, WIsH models and CRISPR blasts databases

Note

Binding the dir like this is required if the files are stored in some shared location and not on the local filesystem.

Output

All output is stored under a results directory within the main workdir. Results are stored per sample according to the sample ids you provided in the sample sheet. For each sample, results for each tool are stored in directories named after the tool. An example looks like this:

results/
├── A
│   ├── all_predictions.tsv
│   ├── rafah
│   │   ├── A_CDS.faa
│   │   ├── A_CDS.fna
│   │   ├── A_CDS.gff
│   │   ├── A_CDSxMMSeqs_Clusters
│   │   ├── A_Genomes.fasta
│   │   ├── A_Genome_to_Domain_Score_Min_Score_50-Max_evalue_1e-05.tsv
│   │   ├── A_Ranger_Model_3_Predictions.tsv
│   │   ├── A_Seq_Info.tsv
│   │   └── predictions.tsv
│   ├── tmp
│   │   ├── genomes
│   │   └── reflist.txt
│   └── vhulk
│       ├── predictions.tsv
│       └── results.csv

• all_predictions.tsv: Contains the best prediction per contig (rows) for each tool along with its confidence/p-value/whatever single value each tool uses to evaluate its confidence in the prediction. An example for three genomes:

contig  vhulk_pred      vhulk_score     rafah_pred      rafah_score
NC_005964.2     None    4.068828        Mycoplasma      0.461
NC_015271.1     Escherichia_coli        1.0301523       Salmonella      0.495
NC_023719.1     Bacillus        0.0012575098    Bacillus        0.55

Per tool

• tmp directory

  • Contains one fasta file per input genome, along with other intermediate files necessary for a smooth execution of the workflow.

• rafah

  • All files prefixed with <sample_id>_ are the rafah's raw output
  • predictions.tsv: A selection of the 1st (Contig) , 6th (Predicted_Host) and 7th (Predicted_Host_Score) columns from file <sample_id>_Seq_Info.tsv

• vhulk

  • results.csv: Copy of the results/sample/tmp/genomes/results/results.csv
  • predictions.tsv: A selection of the 1st (BIN/genome), 10th (final_prediction) 11th (entropy) columns from file results.csv.

Logs

Logs capturing stdout and stderr during execution of each rule are located in workdir/logs/<sample_id>/*.log files.