This repository has been created to host files (code and data files) related to the integrated miRNomics and lipidomics datasets produced by the Laboratory of Experimental Pharmacology and Integrative Biology of Atherosclerosis (EN|IT), of the University of Milan.

Authors: Stefano Manzini¹, Elsa Franchi¹, Alice Colombo¹, Marco Busnelli¹ and Giulia Chiesa¹.

1. Department of Pharmacological and Biomolecular Sciences "Rodolfo Paoletti", Università degli Studi di Milano, via Balzaretti 9, 20133 Milano, Italy;

Please note that, as the article is currently being written, data is uploaded in either anonymized tables or in password-protected zipfiles. Final data and results will be available without restriction upon publication.

• Reproducing the results

The repository root contains two bash scripts that can be launched to reproduce the analyses described in the paper. Please consider that each reconciler.py run takes a few hours to complete (depending on your hardware). reconciler.py supports a wide range of paramenters and settings, please refer to the paper and to reconciler.py’s help to adapt the parameters to suit the needs of your analysis.

To create your local clone, type in a terminal:

git clone https://github.com/Lab-Chiesa/Integrated_miRNomics_and_Lipidomics

To use the scripts, cd into the directory and type:

cd Integrated_miRNomics_and_Lipidomics/
chmod +x *.sh

You will find two bash scripts in the repo directory. run_merged_diets_analysis.sh runs the correlation search in the experimental datasets. run_random_merged_diets_analysis.sh builds model datasets (1 each), then runs the same analysis on them.

You can call the scripts directly if your bash lies in /bin/bash. Also, python programs are expecting that your python/python3 executable lives in an Anaconda environment at /Applications/Anaconda3/anaconda/bin/python3.

Depending on your system configuration, you might need to inspect the bash scripts to manually run the python programs appropriately.

• Repository content

/data

Contains the two datasets: digested data from smallRNAseq and mass-spec lipidomics of murine tissues, in tab-delimited tables.

/lib

Contains a Python library of common functions called by other programs.

/miRNA_targets

Contains TargetScan mRNA predictions of miRNAs discussed in the paper, keyword-based GeneCards gene lists, and results about miRNA to mRNA predictions.

/reconciler

Contails reconciler.py, the main program that does the correlations. Extensive description of its design is contained in the paper’s Supplementary Materials and Methods.

/results

Contains the pre-computed output of reconciler.py, discussed in the paper.

/source

Contains other Python source files of programs and scripts used in the analysis workflow. It also contains pickled Python objects used by these programs.