PopPUNK πŸ‘¨β€πŸŽ€ (POPulation Partitioning Using Nucleotide Kmers)
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README.md

PopPUNK (POPulation Partitioning Using Nucleotide Kmers)

Build Status Documentation Status PyPI version Anaconda package

Step 1) Use k-mers to calculate core and accessory distances

Step 2) Use core and accessory distance distribution to define strains

Step 3) Pick references from strains, which can be used to assign new query sequences

See the documentation and the pre-print.

Installation

The easiest way is through conda, which is most easily accessed by first installing miniconda. PopPUNK can then be installed by running:

conda install poppunk

If the package cannot be found you will need to add the necessary channels:

conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge

If you do not have conda you can also install through pip:

python3 -m pip install poppunk

You will need to be using Python 3.

Using both of these methods command poppunk will then be directly executable. Alternatively clone this repository:

git clone git@github.com:johnlees/PopPUNK.git

Then run with python poppunk-runner.py.

Dependencies

You will need a mash installation which is v2.0 or higher.

The following python packages are required, which can be installed through pip. In brackets are the versions we used:

  • python3
  • DendroPy (4.3.0)
  • hdbscan (0.8.13)
  • matplotlib (2.1.2)
  • networkx (2.1)
  • numba (0.36.2)
  • numpy (1.14.1)
  • pandas (0.22.0)
  • scikit-learn (0.19.1)
  • scipy (1.0.0)
  • sharedmem (0.3.5)

Test installation

Run the following command:

cd test && python run_test.py

If successful, you can clean the test data by running:

cd test && python clean_test.py

Quick usage

Easy run mode, go from assemblies to clusters of strains:

poppunk --easy-run --r-files reference_list.txt --output poppunk_db

Or in two parts. First, create the database:

poppunk --create-db \
   --r-files reference_list.txt \
   --output poppunk_db \
   --threads 2 \
   --k-step 2 \
   --min-k 9 \
   --plot-fit 5

Then fit the model:

poppunk --fit-model \
   --ref-db poppunk_db \
   --distances poppunk_db/poppunk_db.dists \
   --output poppunk_db \
   --full-db \
   --K 2

Once fitted, new query sequences can quickly be assigned:

poppunk --assign-query \
   --ref-db poppunk_db \
   --q-files query_list.txt \
   --output query_results \
   --update-db

If running without having installed through conda or pip, run python poppunk-runner.py instead of poppunk.

See the documentation for full details.