Question: how to get genes and their interactions from RNA-Seq data?
gravatar for raza.ul.haq.ruh
2.5 years ago by
raza.ul.haq.ruh0 wrote:

I am working on gene regulatory networks and to construct one I need genes and their interactions with each other. But I can't understand how do I get this information from RNA-Seq data.

rna-seq gene • 2.0k views
ADD COMMENTlink modified 2.5 years ago • written 2.5 years ago by raza.ul.haq.ruh0

you need a list of genes in rows and sample names in column as a txt file. the file should include normalized expression values of genes. open Cytoscape, import-network-table then Cyni toolbox, from interface algorithm select one of options for instance ARACNE and run. a network would be created.

ADD REPLYlink written 2.5 years ago by A3.8k

and my question is how do I get gene and sample names(their interactions, which I mentioned ) from RNA-Seq data downloaded from SRA NCBI?

ADD REPLYlink written 2.5 years ago by raza.ul.haq.ruh0

have a look at string and reactome

ADD REPLYlink written 2.5 years ago by cpad011213k
gravatar for raza.ul.haq.ruh
2.5 years ago by
raza.ul.haq.ruh0 wrote:

Thank you all, I have found a convenient way to resolve this problem by using geneFriends database, you can find find more about it here.

ADD COMMENTlink written 2.5 years ago by raza.ul.haq.ruh0
gravatar for Kevin Blighe
2.5 years ago by
Kevin Blighe60k
Kevin Blighe60k wrote:

Many people do use Cytoscape, as Fereshteh mentions - it is quite good for constructing graphs and networks. I believe that it is now open source (?). Other pathway / network tools, like Ingenuity (commercial), are very comprehensive and professional, and really do a great job.

Another useful implementation, but in R, comes with the igraph package. It is a very broad package and has a lot of functionality. It takes a while to get into it (took me a week initiailly to really grasp it). The first step from an expression dataset to a graph object would be to create a correlation distance matrix and to coerce it to a graph adjacency object (see below).

QUESTION: I don't have a tutorial for this on Biostars but I will put one up if there is demand / interest (?). There are not many tutorials online about it.

graph.adjacency(as.matrix(as.dist(cor(t(MyData), method="pearson"))), mode="undirected", weighted=TRUE, diag=FALSE)

One can also use Euclidean distance

graph.adjacency(as.matrix(dist(MyData, method="Euclidean"))), mode="undirected", weighted=TRUE, diag=FALSE)

Then, by working through numerous (many) functions, you can produce very nice and weird graph objects that you will gaze at for a long time:



pic upload

ADD COMMENTlink modified 2.5 years ago • written 2.5 years ago by Kevin Blighe60k

I have created a tutorial for this: Network plot from expression data in R

ADD REPLYlink written 2.5 years ago by Kevin Blighe60k
gravatar for svlachavas
2.5 years ago by
svlachavas670 wrote:

Just to add some complementary comments to the already great answers above. The term "Gene Regulatory Networks", is still a generic term, being part of the general concept of biological networks (which also includes for instance signaling pathways, metabolic networks), which still includes various categories, and numerous approaches for "network-reconstruction". Thus, which is your main goal for inferring gene regulatory networks ? For example:

1) You want to utilize your total expression set of RNA-Seq gene counts, define from this some "co-expressed" modules, and relate them to phenotypic traits or similar downstream analysis, such as functional enrichment ? To see their role in your phenotype pertubation ? Then in R, WGCNA is an excellent choise.

2) Or alternatively, wou would like mostly to infer co-regulatory networks ? That is, infer networks of interacting Transcriptional factors, that regulate a list of DE genes of interest ? and might play a crusial role in your biological system ? Then, CoRegNet R package is a wonderful choise, as it also has an option to use experimentally validated TF-gene interactions, PPIs, etc.

If still you are not interested in R, Cytoscape, Gephi or other standalone tools might be more easy to handle.

Also i would like to suggest a very interesting review about gene regulatory networks, and especially a specific sub-category of these, which is very popular: "gene co-expression networks" (Gene co-expression analysis for functional classification and gene–disease predictions)



ADD COMMENTlink written 2.5 years ago by svlachavas670

Agree on the amazing review.

Among all the GRN tools, GENIE3 still looks like the most promising one starting from expression data (and i would assume we all want to start from RNA-seq data nowadays). The next challenge in GRNs is to integrate experimentally validated data from multiple experiments (e.g. PPI, CHIP-seq, etc) and eventually combine it which predicted TFBS and interologs in order to have a mixed in silico/experimentally validated most accurate representation of the regulatory context of our process of investigation.

That said, I have a question for @svlachavas and @kevin.
Would you just compile this non-RNA-seq based information (where available for your species of course) or would you still use tools such as the above mentioned CoRegNet, but also cMonkey2 and Merlin+P (which are the only ones able to combine multiple sources of information to assist RNA-seq based GRN inference [even if so far have not been used that much]) ???

Thanks in advance

ADD REPLYlink written 15 months ago by lessismore880
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