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Quickstart Guide

Installation

To run happie, you will need to have Docker or Singularity installed, and python3.

Next, clone this repository, and install with python

git clone https://github.com/nickp60/happie
cd happie
python setup.py install

If you had any issues with the python install, try installing into a bioconda environment.

Running happie the first time

Happie is now installed, but we need to get the docker/singularity images for all the programs it uses. This is a bit of a pain, but having pre-built versions ensures that happie can be run replroducibly on any system, and can scale with distributed computing.

We have pre built all the images, and include a program, happie_install_or_update to do the work of downloading them from dockerhub.

Run that command:

happie_install_or_update

You should see the status as all the programs are installed.

A simple analysis

The data

Lets investigate the mobilomes of three /Staph/ strains:

  • Staphylococcus aureus D30
  • Staphylococcus aureus subsp. aureus MR1
  • Staphylococcus aureus subsp. aureus str. CF-Marseille

First of all, lets make a directory, and download our genomes into it, and gunzip them.

mkdir staph
cd staph
curl -o D30.fasta.gz  ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/171/455/GCF_000171455.1_ASM17145v1/GCF_000171455.1_ASM17145v1_genomic.fna.gz
curl -o MR1.fasta.gz ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/176/195/GCF_000176195.1_ASM17619v1/GCF_000176195.1_ASM17619v1_genomic.fna.gz
curl -o Marseille.fasta.gz ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/180/395/GCF_000180395.1_ASM18039v1/GCF_000180395.1_ASM18039v1_genomic.fna.gz
gunzip ./*
cd ../

You should have a directory with three genomes in it now. You are ready to run happie.

setting important paremeters

We know that staph genomes typically are between 2.4Mb and 3.4Mb, so we need to let happie know with --QC_min_assembly and --QC_max_assembly; it will throw an eerror for a genome outside the expected assembly size. For this analysis, lets skip running annofilt, the pipeline to remove bad annotations on the ends of contigs. That requires a bit of work, so for now, we turn annofilt off wit --skip_annofilt.

We need to select one or more plasmid-detection tools. Because mlplasmids doesnt have a built in database for Staph, lets use plasfow by saying --plasmid_tools plasflow.

Lets say you're running on a machine with 4 cores; add --cores 4 to the call

Finally, lets have it run abricate with vfdb to detect virulence factors, and the CARD database to detect AMR genes: --analyses vfdb card

We will run these commands in a bash loop, once for each genome:

for i in D30 MR1 Marseille;
do
   happie --contigs ./staph/${i}.fasta --QC_min_assembly 2400000 --QC_max_assembly 3400000 --skip_annofilt --plasmid_tools plasflow --analyses vfdb card --cores 4 --name ${i} --output happie_${i}
done

On my machine, this takes about 12 minutes per genome