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PopPUNK (POPulation Partitioning Using Nucleotide Kmers)

Project description

# PopPUNK (POPulation Partitioning Using Nucleotide Kmers)

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[![Documentation Status](](
[![PyPI version](](

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](

## Installation
The easiest way is through pip, which we would also recommend being
a [miniconda]( install:
pip install poppunk

The command `poppunk` will then be directly executable. Alternatively
clone this repository:
git clone
Then run with `python`.

### 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

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

## 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 \

If running without having installed through PyPI, run `python` instead of `poppunk`.

See the [documentation]( for
full details.

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