MiR_Combo_Targeting

Companion code for the paper: "Network potential identifies therapeutic miRNA cocktails in Ewing sarcoma" You can find the puplication for this analysis here: https://journals.plos.org/ploscompbiol/article/comments?id=10.1371/journal.pcbi.1008755 If you have any questions regarding how to run this pipeline on your device/ use it for your research, feel free to shoot me an email at davis.weaver@case.edu.

Here is the general flow:

Most of the heavy lifting is done by an R package that I wrote entitled "disruptr". You can install disruptr using the command:

devtools::install_github("https://github.com/DavisWeaver/disruptr")

You can skip this step if you like!

The main network potential pipeline can then be run by sourcing the file Main_EWS_MiR.R. This will likely require a computing cluster (if you comment out the compute_null command it can run on a laptop in a few hours). Sample batch files are provided for submitting to a linux cluster.

Start here if you are short on time

I have included the output of our main computational pipeline as a system file in the disruptr package. To run the second part of our analysis (the miRNA optimization bit), simply open miR_Analysis_Ewing.R and hit run. After that, the miR_Figs_Ewing.R script that generates all of our figures and tables should run mostly without a hitch. If you would like to reproduce all of our figures - you will need to get access to some data that we are not authorized to distribute (namely the transcriptomic data from St. Jude Children's Research Hospital)

Dependencies:

Package dependencies are at the top of each script. miR_Figs_Ewing.R will break into a million pieces if you try and run it before miR_Analysis_Ewing.R. One of our figures makes use of the data provided by this paper: https://pubmed.ncbi.nlm.nih.gov/31978347/. We did not include it in this repository because it is too large and github didn't like it but it is freely available for download from the original authors.

Questions

Feel free to reach out to me at davis.weaver@case.edu if you have any questions! If you would like to contribute / learn more about in-silico repression techniques, head on over to https://github.com/DavisWeaver/disruptr