Lets say I only have a list of gene names. I know they are up regulated and I know they are related to cancer.
What information can I obtain from having only the names of genes? For example is there any way to find a gene-gene interaction, or anything else? Is it possible to retrieve any information by having only the name of the genes?
I would suggest searching the name in any trusted genetics database such as NCBI's GenBank (http://www.ncbi.nlm.nih.gov/genbank/). You can just Google search it, but it may take a little longer to find useful information that way.
I hope this helps and good luck with your research,
CDB
Was getting long in the comments.
In light of your comments, you might be interested in Gene-set enrichment analysis (GSEA). You can do a GSEA using your set, the other one coming from reference databases such as MSigDB (see here). You can categorize your list by gene families using this technique for example.
You can get an idea of what cellular process is the most impacted by the genes in your list and from there try to reach some conclusions but just as a warning, what you are attempting is not trivial at all. It will be hard to reach valid conclusions from a simple list of genes as correlation does not mean causation (i.e. is the gene up regulated because of cancer or is it provoking the cancer?)
To visualize pathways you might want to look at this post on Biostars. Several software are available for this purpose.
It depends; what species are the genes from? Some organisms have extensively annotated genomes, and actively curated species-specific databases, while other species may not even have a reference genome sequence available. By itself, a priori, if you were lucky, about all you list would tell you was how to spell the names of those genes--if you're lucky. But to a skilled Bioinformatician said list could be a gateway to a discovery, or a patent, or a publication, or a grant, although none of these is very likely.
Also try putting the list through Reactome or String DB to see if you see mapping to certain pathways. http://string-db.org/
You can also put lists through ConceptGen to carry out ontology based analyses http://portal.ncibi.org/gateway/conceptgen.html
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.
take the gene names and copy all of them, next open an online tool called gene mania. paste all your genes on the window. you will get their interaction and other related genes. Now go to a database called gene cards and type in all the gene you got from gene mania and paste it one after the other to get useful information about the individual genes. some of which are restriction site information's, transcription factors etc.
