Genomique-Comparee

Génomique Comparée (Cours M2 AMI2B Paris-Saclay) Students :

* Théo Roncalli
* Gustavo Magaña López
* Anthony Boutard

Usage

Parse the whole blast_outputs directory into a dict, containing the best hits.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import gencomp.core_genome as cgenome
from gencomp.parsing import parse_blast_directory_to_dict


# unidirectional best hits
bh = parse_blast_directory_to_dict("Data/Outputs/")
# bidirectional best hits
bbh = cgenome.search_bbh(bh)
# core genome (list of cliques)
core_genome = cgenome.find_core_genome(bbh, 21)

# random strain subsets sorted by size in increasing order
strains = [
    set(np.random.choice(list(bh.keys()), i, replace=False)) for i in range(2, 21)
]

sub_bbh = cgenome.search_bbh(bh, strains[4])
sub_core_genome = cgenome.find_core_genome(sub_bbh, len(strains[4]))

## WARNING, this part takes 371 seconds on my i7-10875H (16) @ 5.100GHz
# Diversity calculation :
core_genomes_dict = cgenome.parallel_find_random_core_genomes(
    bh, # unidirectional best hits
    sizes=list(range(2, 21)), # sizes [2, ..., 20]
    batch_size=20, # batch size is set to 20C21 = 20 (higher number of repetitions implies duplicates on 
                   # strain subsets of size 20
    n_threads=16   # adjust according to you machine
)
cg_df = pd.DataFrame(core_genomes_dict)
cg_df.to_csv("core_genomes_diversity.csv")
cg_df.plot.scatter("n_strains", "core_genome_size")
plt.show()

All of the entries of bh have been selected according to our thresholds computed by KMeans.

TODO

• Compute Bidirectional Best Hits (should be trivial with the previous dict).
• Compute the clique.

Installation

First step is cloning the repo (if we publish to PyPI you won't need to clone the repo unless you want to have the development version).

 # clone the repo
 git clone https://github.com/gmagannaDevelop/Genomique-Comparee.git

Using poetry (recommended)

We recommend using poetry to install the package as its dependency solver and lock files are far more reliable than Anaconda's. This module is developped using Python 3.8.8. No support is intended for older Python versions, specially so for Python 2* as it has been deprecated.

# ask poetry to install all the dependencies
poetry install
# open a new shell within the virtual environment
poetry shell
# You are ready to go :)

If you don't have poetry installed, run this first

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python -
#curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python3 -

WARNING: This assumes python points to python3 on your system. If it is not the case, then you should run the second option.

Using Anaconda

# create a new conda environment (you can replace "gencomp" with any name you like)
conda create -n gencomp python=3.8.8
# activate it 
conda activate gencomp
# install the dependencies using pip
pip install -r requirements.txt