Answers.Body: <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 />
  
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Answers.CreationDate: 2019-11-27T14:52:52.340Z
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Answers.Body: <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>
  
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Answers.CreationDate: 2019-11-27T13:34:32.467Z
Answers.Text: https://www.nature.com/articles/s41467-018-06676-2
Answers.UserDisplayName: 0x90
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Answers.Text: what's network analyst?
Answers.UserDisplayName: 0x90
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Answers.Text: https://www.networkanalyst.ca
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Body: <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>
  
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Comments.CreationDate: 2019-11-27T14:40:56.717Z
Comments.Text: do you have a list of genes that you would like to investigate through network analysis?
Comments.UserDisplayName: gbt
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Comments.Text: @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
Comments.UserDisplayName: 0x90
Tags: <genomics><gwas><crispr><systems-biology>
Title: CRISPR screening and systems biology
ThreadText: # 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  
  
  ---
  
  