Proteomics data Vs Transcriptomics data?

Question

I want to use either of Proteomics or Transcriptomics data for integrating it into my kinetic model. Before proceeding, I want to know what are the advantages of using either of them so that I could make an informed decision on it!

Many studies have shown that the best we can do is integrating both transcriptomics and proteomics data with our kinetic model, but I've some time constraints and have to proceed with only one of those.

My effort and findings: I've found from discussions with researchers that gathering transcriptomics data has an amplification step which increases the chance of finding a particular one whereas gathering proteomics data has no such step but has fragmentation and then rejoining which creates many problems(such as splice variants etc) and thus leads to a loss of data. But a PostDoc told me that even after the loss of that data, I'll get more information from Proteomics data.

I want to know such type of points and want to know if these are valid or not!

Answer 1

This is what I found from doing some research. Comments are welcome at any point!

• Capture percentage for data gathering:

  • Transcriptomics Data: There's an amplification step in Transcriptomics data gathering methods. Hence, it's possible to capture almost the totality of the Transcriptome using those methods(scRNA seq methods, Nanopore tech, Spatial transcriptomics, etc)

  • Proteomics Data: There's no amplification step in Proteomics data gathering methods. Those methods have fragmentation and a rejoining step which leads to a loss of the data.

• Scalability of methods:

  • Transcriptomics Data: Scalable methods available

  • Proteomics Data: Less scalable methods for protein studies

• Reference availability:

  • Transcriptomics Data: Fully annotated references available on consortiums such as ensembl biomart, etc

  • Proteomics Data: Universal and Comprehensive Human Proteome reference is still in question and because of the incomplete and inaccurate references, much of the newly generated data is being rendered useless

• Uniformity

  • Transcriptomics Data: Lots of publications on pipelines, and their benchmarking available

  • Proteomics Data: Lack of uniformity across labs/research groups and lack of related literature lead to differences in protein fragmentation and solubilization and differences in algorithms to run analyses

• Technical Bias

  • Transcriptomics Data: Many methods available to tackle cell-bias, noise in data, and batch effects, so as to capture maximum biological variability

  • Proteomics Data: Cell-wide Mass spectrometry has a bias towards the identification of peptides with higher concentration or contamination from other experiments