<p>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.</p>
<p>You can look into established databases containing information about interactions like, for instance, <a href="https://string-db.org" rel="nofollow noreferrer">STRING</a>. Or, you can build networks using the <a href="https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/" rel="nofollow noreferrer">wgcna</a> method with R.</p>
<p>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 <a href="https://cytoscape.org/" rel="nofollow noreferrer">cytoscape</a>, R libraries such as <a href="https://igraph.org/r/" rel="nofollow noreferrer">igraph</a>, python libraries such as <a href="https://networkx.github.io/" rel="nofollow noreferrer">networkX</a> online tools like <a href="http://www.centiserver.org/" rel="nofollow noreferrer">centiserver</a>...</p>
<p>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 <a href="http://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?_Interpreting_GSEA_Results" rel="nofollow noreferrer">here</a>, <a href="http://www.bioconductor.org/packages/release/bioc/vignettes/fgsea/inst/doc/fgsea-tutorial.html" rel="nofollow noreferrer">here</a>, and <a href="https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html#gsea-analysis" rel="nofollow noreferrer">here</a>.</p>
<p>Here there is also a nice <a href="http://kateto.net/networks-r-igraph" rel="nofollow noreferrer">tutorial</a> about network analysis with R.</p>
<hr />

CC BY-SA 4.0
2019-11-27T14:52:52.340Z
2021-08-18T10:28:04.043Z
2021-08-18T10:28:04.043Z
gbt
<p>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 <a href="https://amp.pharm.mssm.edu/Enrichr" rel="nofollow noreferrer">Enrichr</a> or <a href="https://www.networkanalyst.ca" rel="nofollow noreferrer">network analyst</a> to better understand the pathways affected by your treatment.</p>

CC BY-SA 4.0
2019-11-27T13:34:32.467Z
https://www.nature.com/articles/s41467-018-06676-2
0x90
CC BY-SA 4.0
2019-11-27T14:55:54.907Z
what's network analyst?
0x90
CC BY-SA 4.0
2019-11-27T16:11:36.770Z
https://www.networkanalyst.ca
FPS
CC BY-SA 4.0
2019-11-27T06:16:23.947Z
2019-11-27T16:13:27.610Z
2019-11-27T16:13:27.610Z
FPS
<p>I am not sure how to tackle this one, so I’ll explain the general idea I have in mind. </p>

<p>Given results of a CRISPR knockout screening experiment <a href="https://www.nature.com/articles/s41467-018-06676-2" rel="nofollow noreferrer">(like in this paper)</a> with two types of assays: control vs. treated with 5 time points per each of the assays. </p>

<p>Is there a way to use the screening data to perform systems biology <a href="https://www.ebi.ac.uk/training/online/course/network-analysis-protein-interaction-data-introduction/network-analysis-biology-0" rel="nofollow noreferrer">network analysis</a>? Something which is related to signal transduction, transcriptional regulatory, or metabolic networks? </p>

<p>Maybe something which uses Bayesian inference? Or something similar to <a href="https://www.mathworks.com/help/bioinfo/ug/identifying-differentially-expressed-genes-from-rna-seq-data.html" rel="nofollow noreferrer">differentially expressed representation</a> of the CRISPR screening? Alternatively, a GWAS analysis similar to <a href="https://www.nature.com/articles/s41467-019-13585-5" rel="nofollow noreferrer">this paper</a>?</p>

CC BY-SA 4.0
2019-11-27T14:40:56.717Z
do you have a list of genes that you would like to investigate through network analysis?
gbt
CC BY-SA 4.0
2019-11-27T14:52:08.157Z
@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
0x90
<genomics><gwas><crispr><systems-biology>
CRISPR screening and systems biology
# CRISPR screening and systems biology

(Asked by: username_0 on 2019-11-26)

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)](https://www.nature.com/articles/s41467-018-06676-2) 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](https://www.ebi.ac.uk/training/online/course/network-analysis-protein-interaction-data-introduction/network-analysis-biology-0)? 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](https://www.mathworks.com/help/bioinfo/ug/identifying-differentially-expressed-genes-from-rna-seq-data.html) of the CRISPR screening? Alternatively, a GWAS analysis similar to [this paper](https://www.nature.com/articles/s41467-019-13585-5)?

**Tags:** genomics, gwas, crispr, systems-biology
> **Comments:**
> **username_1**: do you have a list of genes that you would like to investigate through network analysis?  
> **username_0**: @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  

---

## 2 answers

### Answer by username_1 on 2019-11-27. Score: 1

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](https://string-db.org). Or, you can build networks using the [wgcna](https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/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](https://cytoscape.org/), R libraries such as [igraph](https://igraph.org/r/), python libraries such as [networkX](https://networkx.github.io/) online tools like [centiserver](http://www.centiserver.org/)...

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](http://www.gsea-msigdb.org/gsea/doc/GSEAUserGuideFrame.html?_Interpreting_GSEA_Results), [here](http://www.bioconductor.org/packages/release/bioc/vignettes/fgsea/inst/doc/fgsea-tutorial.html), and [here](https://bioinformatics-core-shared-training.github.io/cruk-summer-school-2018/RNASeq2018/html/06_Gene_set_testing.nb.html#gsea-analysis).

Here there is also a nice [tutorial](http://kateto.net/networks-r-igraph) about network analysis with R.

---


---

### Answer by username_2 on 2019-11-27. Score: 1

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](https://amp.pharm.mssm.edu/Enrichr) or [network analyst](https://www.networkanalyst.ca) to better understand the pathways affected by your treatment.

> **Comments:**
> **username_0**: https://www.nature.com/articles/s41467-018-06676-2  
> **username_0**: what's network analyst?  
> **username_2**: https://www.networkanalyst.ca  

---

