Hi Paul,

thanks for answering.

I'm already a RCytoscape user, it's very great, but I still have to spend
some time customizing the graphs.

I agree with your point about kegg, but is still the most comprehensive
(reactome: http://www.reactome.org/stats.html and kegg:
http://www.kegg.jp/kegg/docs/statistics.html)

I'm trying to associate compound information with biochemical (reactions,
pathways...) information, to analyse mass spectrometry data, now
I'm analyzing Trypanosoma brucei data.

 I've tried some alternatives to KEGG myself, I've translated every pathway
of an organism to wikipathways format
http://www.pathvisio.org/wiki/KeggConverter. But, the division of pathways
is too artificial in many cases, I need to know how a node links with every
all.

Now, I would like to have a graph with all information I can get like
http://www.kegg.jp/kegg-bin/show_pathway?map01100, but, only with reactions
described for a given organism.

It will be perfect to have this graph on R, with all the information easy
translated to Cytoscape. Cytoscape already has plugins to read KGML, is it
possible to merge graphs and export them back to R?

Cheers

Ricardo

On Tue, Oct 30, 2012 at 4:01 PM, Paul Shannon <pshannon@fhcrc.org> wrote:

> Hi Ricardo,
>
> A few quick observations, and then an invitation to further discussion.
>
> 1) KEGG has played a central role in bioinformatics for years but now, it
> seems, it no longer can.  None of the publicly available data has been
> updated since KEGG 'went private' in the summer of 2011.
>
> 2) Here in Seattle, I -- and others elsewhere -- are exploring REACTOME,
> Nature/NCI and WikiPathways as ongoing sources of metabolic (and other)
> network graphs.
>
> 3) The RCytoscape package
> http://bioconductor.org/packages/2.11/bioc/html/RCytoscape.html is
> designed to make R/Cytoscape network visualization easy.  It comes with
> lots of documentation and a website:
> http://rcytoscape.systemsbiology.net/versions/current/index.html
>
> Which metabolic pathway are you working on?  Perhaps I can use it as a
> case study in my exploration of KEGG alternatives.
>
> Best,
>
>  - Paul
>
>
> On Oct 30, 2012, at 10:47 AM, Ricardo Silva wrote:
>
> > Hello all,
> >
> > I've been trying to create the most complete possible compound map for
> > an organism, and KEGGgraph was the best approach until now.
> >
> > Someone already worked with graphs metabolites in R?
> >
> > I have a doubt if the KEGGgraph merging process can miss nodes, that's
> > because,
> > looking at the compounds in each reaction:
> >
> >    read.delim(paste("http://rest.kegg.jp/link/reaction/", orgId,
> > sep=""),header=FALSE)
> >    read.delim(paste("http://rest.kegg.jp/link/compound/", orgId,
> > sep=""),header=FALSE)
> >
> > where orgId is the tree letter organism code, I have a set of compounds
> > different than the one retrivied bellow:
> >
> > When I use the mergeGraphs function:
> >
> >
> > for (i in 1:length(allPath)) {
> >        retrieveKGML(allPath[i], organism=orgId, destfile=tmp,
> > method="wget", quiet=TRUE)
> >        lg[[i]]  <- parseKGML(tmp)
> >        if(length(lg[[i]]@reactions))  lt[[i]] <-
> > KEGGpathway2reactionGraph(lg[[i]])
> >    }
> >    lt <- lt[!unlist(lapply(lt, is.null))]
> >    merged <- mergeGraphs(lt)
> >
> > where allPath represents all organism pathways in kegg.
> >
> > the nodes of obtained graph are different from those obtained asking
> > compounds by reaction, m'I missing something?
> >
> > Additionally, someone know's an automated way to export the graph to
> > cytoscape, or even to write a KGML?
> >
> > Thanks in advance
> >
> > Ricardo
> >
> >       [[alternative HTML version deleted]]
> >
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>

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