MLPP

Paper: https://doi.org/10.1038/s41467-021-26792-w
Data:

---

Code for reproducing the method described in:

Co-evolution based machine-learning for predicting functional interactions between human genes

Doron Stupp, Elad Sharon, Idit Bloch, Marinka Zitnik, Or Zuk, Yuval Tabach*

*. To whom correspondence should be addressed. YuvalTab@ekmd.huji.ac.il

Citation:

@article{stupp2021co,
  title={Co-evolution based machine-learning for predicting functional interactions between human genes},
  author={Stupp, Doron and Sharon, Elad and Bloch, Idit and Zitnik, Marinka and Zuk, Or and Tabach, Yuval},
  journal={Nature communications},
  volume={12},
  number={1},
  pages={1--14},
  year={2021},
  publisher={Nature Publishing Group}
}

---

Description

MLPP - Machine Learning based Phylogenetic Profiling, the method described in the aforementioned paper, is a method for predicting functional interactions between human genes based on their phylogenetic profile. This method leverages machine learning based clade-wise co-evolution to improve upon existing phylogenetic profiling approaches.

The code in this repo enables one to reproduce the models and predictions presented in the paper. A small example for reproducing figure 1a and supp. figure 3 from the paper is included as well.

Installation:

Installation and execution was tested on Ubuntu 20.04 running miniconda. To run the full pipeline it is advised to utilize a parallalized execution using snakemake due to the high workload of predicting all gene-pairs across all lables.

Conda Enviroment

One can install the required software to reproduce our results by using conda. Clone this repo into a directory, and using the terminal from inside the cloned repository run:

conda env create -f enviroment.yaml

This will create a conda enviroment called MLPP.

Data files:

To run this example one needs to download the associated phylogenetic profiling matrices and other auxillary data which is available on zenodo at: 10.5281/zenodo.5111607.

To download the data, please execute the data script in a bash terminal inside the folder of the cloned repo. The scripts downloads the data to the current folder. Data total size approximately 1GB.

bash retrieve_data.sh

Running:

Small example

This example trains several machine learning models to predict functional interactions between genes. This examples reproduces figures 1a and supp. figure 3 from the paper. For reproducing pathway types comparisons change the argument to --which inReactome_Path

Using the command line, run from inside the cloned repo:

conda activate MLPP
python comparing_classifiers.py --which in_Reactome

Expected output is available through the reports/figures/01_comparing_classifiers folder.

Running the full pipeline

The full pipeline can be run using snakemake. This takes considerable time due to the prediction of all gene-pairs across all different labels. For the results shown in the paper, the pipeline was ran on an HPC cluster using a slurm scheduler.

First fill config/config_smk.yaml with the correct project dir:

#proj_dir
proj_dir: XXXXXXXXXXXXXX

Then, to run the pipeline locally run the script run_snakemake.sh This first installs the src directory as a python package and then runs the snakemake pipeline.

conda activate MLPP
bash run_snakemake.sh

Citation

When using the method or predictions described in the paper please cite

@article{stupp2021co,
  title={Co-evolution based machine-learning for predicting functional interactions between human genes},
  author={Stupp, Doron and Sharon, Elad and Bloch, Idit and Zitnik, Marinka and Zuk, Or and Tabach, Yuval},
  journal={Nature communications},
  volume={12},
  number={1},
  pages={1--14},
  year={2021},
  publisher={Nature Publishing Group}
}