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I am not sure how to tackle this one, so I’ll explain the general idea I have in mind.

Given results of a CRISPR knockout screening experiment (like in this paper) with two types of assays: control vs. treated with 5 time points per each of the assays.

Is there a way to use the screening data to perform systems biology network analysis? Something which is related to signal transduction, transcriptional regulatory, or metabolic networks?

Maybe something which uses Bayesian inference? Or something similar to differentially expressed representation of the CRISPR screening? Alternatively, a GWAS analysis similar to this paper?

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  • $\begingroup$ do you have a list of genes that you would like to investigate through network analysis? $\endgroup$
    – gabt
    Nov 27 '19 at 14:40
  • $\begingroup$ @gabrielet, yes I do have such a list. Even a few related such lists. But I also want to incorporate the information from the up regulation and down regulation values I got in the analysis. Not just the list of genes $\endgroup$
    – 0x90
    Nov 27 '19 at 14:52
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What do you call screening results? Do you have a kind of array with protein or RNA data? If so, you can use several open-source tools available on the internet, like Enrichr or network analyst to better understand the pathways affected by your treatment.

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To investigate genes through a network based approach you can use several tools. I don't know which level of programming you have or which tool you prefer but here is a brief overview of the possibilities you have.

You can look into established databases containing information about interactions like, for instance, STRING. Or, you can build networks using the wgcna method with R.

Then, once you have your network you can analyse its properties and here you also have several tools available such as standalone, java-base software like cytoscape, R libraries such as igraph, python libraries such as networkX online tools like centiserver...

Also you can perform gene set enrichment analsysis, which means adding a further layer of biological information to you set of genes like metabolic pathways (from KEGG, for example) and molecular functions (from GeneOntology, for instance) (gsea) as here, here, and here.

Here there is also a nice tutorial about network analysis with R.


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