We have done transcriptome profiling of larvae of Drosophila melanogaster through Hiseq2500 platform, and proteome profiling through LC-MS/MS of the same. We wanted to check the genes which are going to translate into protein from our DEGs list, only 1.7% of genes were found similar in both data set.
Is it right profiling in both cases?
How can I correlate both data sets?
If, there is any issue, how can I improve it?
Hi,
What an interesting opportunity to be able to generate both proteomics and transcriptomics data!
Even though I don't know what a typical number of correlating genes would be (that is highly dependent on the organism and research question, I guess. Just out of interest: what is your research question?), it is known that transcriptomic and proteomic datasets do not correlate perfectly.
And this can be explained, to some extent, by phenomena like post-translational modifications (such as phosphorylation, acetylation, etc.), protein trafficking and/or degradation, which are detected at the protein, but not the mRNA, level.
Again, I'm no expert, so I'm looking forward to other people's views on this!
Hope this helps!
Thank you so much Eef Dirksen
We are working on Drosophila, and we are looking the effect of some Ayurvedic formulation on the transcriptome and proteome of different stages of life cycle (larval, pupal, young adult and old adult) of Drosophila.
Hello Yashvant,
If by saying " protein from our DEGs list, only 1.7% of genes were found similar in both data set. " you mean that for about 20,000 transcripts you have matching proteins for 400 proteins or so; it is OK and normal. Proteomics does not cover/ capture the entirety of transcriptome. No issues typically. Mass-specs do not capture (i.e., membrane proteins) all proteins (as one can not amplify proteins) as sequencers do for transcripts, but also, not transcripts do translate to proteins (i.e., transcription factors, structural RNAs)
Start with Paintomics to map both proteins and transcripts which are differential or all together! Try here: http://www.paintomics.org/
For more advanced analysis to combine the 2 data sets try many of the popular tools described in our recent review:
Misra, B.B., Langefeld, C.D., Olivier, M. and Cox, L.A., 2018. Integrated Omics: Tools, Advances, and Future Approaches. Journal of molecular endocrinology, 1(aop). https://jme.bioscientifica.com/view/journals/jme/62/1/JME-18-0055.xml
What you are asking as 'correlation' could be -pathways, networks, GOntology, enrichment, over-representation analysis and so on. For those tools also, consult the above review for insights and links.
Thanks,
Biswa
Hi, Biswapriya Biswavas Misra
Thank you so much for your generous suggestions, let me check tools suggested by you.
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