Question: how to get genes and their interactions from RNA-Seq data?
0
gravatar for raza.ul.haq.ruh
22 days 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 • 288 views
ADD COMMENTlink modified 21 days ago • written 22 days ago by raza.ul.haq.ruh0
3

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 22 days ago by Fereshteh2.8k

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 21 days ago by raza.ul.haq.ruh0

If you are looking to model or 'map' your RNA-seq data to current known and curated interaction networks ('pathways'), then just do simple pathway analysis with DAVID or some of the other tools mentioned here. There are also commercial tools.

You only now mention SRA (not in your original question), but why? Do you plan to download an existing RNA-seq study and work from there? If so, in which disease are you interested? Most or all of us have assumed that you already have your own RNA-seq data.

ADD REPLYlink modified 21 days ago • written 21 days ago by Kevin Blighe8.9k

I come from computer science background, not biology I didn't know that there is a difference between these two data sets.

ADD REPLYlink modified 19 days ago • written 19 days ago by raza.ul.haq.ruh0
2

have a look at string and reactome

ADD REPLYlink written 22 days ago by cpad01123.5k
6
gravatar for Kevin Blighe
22 days ago by
Kevin Blighe8.9k
Europe/Americas
Kevin Blighe8.9k 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:

mst

Captura_de_tela_de_2017_11_18_07_57_17

communities
pic upload

ADD COMMENTlink modified 22 days ago • written 22 days ago by Kevin Blighe8.9k

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

ADD REPLYlink written 18 days ago by Kevin Blighe8.9k
4
gravatar for svlachavas
22 days ago by
svlachavas300
Greece
svlachavas300 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"

https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbw139/2888441 (Gene co-expression analysis for functional classification and gene–disease predictions)

Cheers,

Efstathios

ADD COMMENTlink written 22 days ago by svlachavas300
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 944 users visited in the last hour