Skip to main content
Biology

Return to Answer

added image of the network
Source Link
ddiez
ddiez
  • 1.7k
  • 1
  • 15
  • 24

Interaction network of human KRAS

Then you could repeat the cycle with the interacting partners. There are many other things you can do, starting from a list of symbols; this is just a brief example. More detailed examples and workflows can be found here.

Then you could repeat the cycle with the interacting partners. There are many other things you can do, starting from a list of symbols; this is just a brief example. More detailed examples and workflows can be found here.

Interaction network of human KRAS

Then you could repeat the cycle with the interacting partners. There are many other things you can do, starting from a list of symbols; this is just a brief example. More detailed examples and workflows can be found here.

Source Link
ddiez
ddiez
  • 1.7k
  • 1
  • 15
  • 24

Assuming each gene symbol represents a unique gene or protein (this may not be true; see for gene name aliases in e.g. NCBI Entrez gene) you can get a lot of information programmatically. Below is an example using R and Bioconductor resources.

Define your list of genes:

# list of gene symbols, here we focus on one.
> genes <- "KRAS"

Load the annotation package org.Hs.eg.db and retrieve basic information including Gene Ontology ids:

# annotation package for human genes.
> library(org.Hs.eg.db)

# query Entrez gene id, description (GENENAME) and Gene Ontology id (GO)
> info <- select(org.Hs.eg.db, keys = genes, keytype = "SYMBOL", columns = c("ENTREZID", "GENENAME", "GO"))
> head(info)
  SYMBOL ENTREZID                                   GENENAME         GO EVIDENCE
1   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0000165      TAS
2   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0000186      TAS
3   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0001934      IMP
4   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0005515      IPI
5   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0005525      IEA
6   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0005737      IDA
  ONTOLOGY
1       BP
2       BP
3       BP
4       MF
5       MF
6       CC

Load the GO.db package and retrieve the term associated with the GO ids (this gives info about putative function):

> library(GO.db)
> info$Term <- Term(GOTERM[info$GO])
> head(info)
  SYMBOL ENTREZID                                   GENENAME         GO EVIDENCE
1   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0000165      TAS
2   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0000186      TAS
3   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0001934      IMP
4   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0005515      IPI
5   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0005525      IEA
6   KRAS     3845 Kirsten rat sarcoma viral oncogene homolog GO:0005737      IDA
  ONTOLOGY                                           Term
1       BP                                   MAPK cascade
2       BP                   activation of MAPKK activity
3       BP positive regulation of protein phosphorylation
4       MF                                protein binding
5       MF                                    GTP binding
6       CC

Load the RefNet package and get information about interacting proteins:

> library(RefNet)
> refnet <- RefNet() # this step downloads from AnnotationHub and may take some time.
> int <- interactions(refnet, species = "9606", id = genes, provider = "BioGrid")
# create data.frame with Entrez id of proteins.
> d <- int[,1:2]
> d$A <- sub(".*locuslink:(.*)\\|.*", "\\1", d$A)
> d$B <- sub(".*locuslink:(.*)\\|.*", "\\1", d$B)
> head(d)
      A    B
1  3065 3845
2  8841 3845
3 23411 3845
4  1994 3845
5  3845 9770
6  9770 3845

Use the igraph package to visualize the interacting proteins.

> library(igraph)
> g <- igraph::simplify(graph.data.frame(d, directed = FALSE))

# annotate the nodes with the symbol.
> V(g)$label <- select(org.Hs.eg.db, keys = V(g)$name, columns = c("SYMBOL"), )$SYMBOL
> plot(g)

Then you could repeat the cycle with the interacting partners. There are many other things you can do, starting from a list of symbols; this is just a brief example. More detailed examples and workflows can be found here.