3Omics is also good. But, even though one uses, same material for both studies, it would be hard to compare for obvious reasons. A transcriptome can easily come close to show expression of 99% of the coding/ expressing regions of the genome, while identifiable metabolomes is only 5-10% of the genome with best possible available tools, annotations and mass-specs. thus limitation is "annotation" and the "diverse class of chemistry" of metabolome in general. Proteomes lay somewhere in the middle of both. Combing both may help you 'focus on few pathways' where most of your metabolites concentrate (instrumental and chemical bias/ basis being the major reasons!), but even would be hard to explain why in a metabolic pathway, one molecule upstream is upregulated, the one below it is downregulated while many do not show any change. Statistics alone would not help in deciphering the 'missing dots' in metabolomic datasets.

Hi,

I am not sure but I suggest you check out:

http://spectra.psc.riken.jp/menta.cgi/primelink/index

Here is the paper:

Sakurai et al. (2013) PRIMe Update: Innovative Content for Plant Metabolomics and Integration of Gene Expression and Metabolite Accumulation. Plant Cell Physiol. 54:e5 [PubMed].

This is a very valuable tool for plant metabolomics.

Cheers,

Holger

Dear Holger,

Thanks for the material.

You might also use Dynamic Impact Approach (DIA) methodology to study the most important pathways in plants from your transcriptomics and metabolomics data.

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0032455

Not sure whether you have metabolomics data linked with their respective pathways? 

Thanks Ms. Khuram.

Hi Davi,

Some two possible options you can explore are the mixOmics and kohonen R packages.

With the first package (mixOmics), you can use either a CCA or PLS approach to try to find common behaviour between the two datasets. I think their website also has some sort of online tool too, if you don't use R.

The second package (kohonen) is mostly focus on self-organizing maps. You can use the multi-level map approach to try to see the common patterns between your metabolomic and transcriptomic datasets.

Regardless of what analysis option you choose, just make sure to measure the metabolomic and transcriptomic data on the same set of samples.

http://cran.r-project.org/web/packages/mixOmics/index.html

http://cran.r-project.org/web/packages/kohonen/index.html

http://www.jstatsoft.org/v21/i05/paper

http://perso.math.univ-toulouse.fr/mixomics/

Dear Tomé Silva,

This is very interesting, I will look with attention.

Thanks!.

3Omics is also good. But, even though one uses, same material for both studies, it would be hard to compare for obvious reasons. A transcriptome can easily come close to show expression of 99% of the coding/ expressing regions of the genome, while identifiable metabolomes is only 5-10% of the genome with best possible available tools, annotations and mass-specs. thus limitation is "annotation" and the "diverse class of chemistry" of metabolome in general. Proteomes lay somewhere in the middle of both. Combing both may help you 'focus on few pathways' where most of your metabolites concentrate (instrumental and chemical bias/ basis being the major reasons!), but even would be hard to explain why in a metabolic pathway, one molecule upstream is upregulated, the one below it is downregulated while many do not show any change. Statistics alone would not help in deciphering the 'missing dots' in metabolomic datasets.

Dear Mr. Biswapriya,

Firstly, thanks for your opinion. I will take a look in this tool, maybe can help me in the future.

I have a pertinent question. Is it possible use the Arabidopsis as a scaffold to the integrative analysis of sugarcane? Because we don't have much information about metabolic pathway of sugarcane.

I would like to use the tool that Ms.Holger Schmidt showed me previously.

http://spectra.psc.riken.jp/menta.cgi/primelink/index

For Sugarcane, "Oryza/ Rice" should be used. Use GrameneDB's pathways or MetaCyc/ PlantCyc for Rice for pathway analysis. So refrain from using Arabidopsis for sugarcane which is a monocot. Even wheat tools would help a bit, but rice/ Brachypodium would be best. sorry for delayed answer as I did not notice. best wishes. Mr. (and not Ms.!) Biswa

Try PaintOMICs too...