A simple visual tool to illustrate the relationship between transcript and protein specificity in human organs
- 1Organ Pathobiology and Therapeutics Institute
- 2University of Pittsburgh
Abstract
Today a large amount of high depth quantitative proteomic information exists for nearly every human tissue. Despite this wealth of information simple tools for visualizing protein distribution in the human body are somehow lacking. Due to the challenges in accessing protein measurements directly biologists commonly use accessible web portals which provide RNA transcript read counts, which end up being used as a proxy for protein data. This is unfortunate because it is well established that the correlation between transcript counts and protein concentration in mammalian material is low. This is, however, a relatively vague concept that is difficult to both envision and to explain in the context of the central dogma. One way to rectify this problem would be by making protein quantification data as simple and accessible as RNA read count data. To illustrate how a large amount of proteomic data can be easily visualized, I have constructed an example lightweight HTML page that is small enough to be attached to email even when containing deep proteomic analysis of 29 healthy human tissues. The value of simple proteomic data representation is further demonstrated by overlaying proteins predicted to be organ specific from transcriptomic data. Displayed together, it is possible to rapidly determine which transcript measurements can be trusted to be predictive of protein specificity. All data, code and step-by-step analyses are made available in the Supporting Information and a web instance of the resulting tool comparing two gold standard datasets is hosted at: https://www.orsburnlab.org/protein-specificity.
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BibTeX
@misc{orsburn2026simple,
title = {{A simple visual tool to illustrate the relationship between transcript and protein specificity in human organs}},
author = {Benjamin C. Orsburn},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15008472/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008472/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - A simple visual tool to illustrate the relationship between transcript and protein specificity in human organs
AU - Benjamin C. Orsburn
PY - 2026
DA - 2026/09/08
PB - ChemRxiv
DO - 10.26434/chemrxiv.15008472/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15008472/v1
AB - Today a large amount of high depth quantitative proteomic information exists for nearly every human tissue. Despite this wealth of information simple tools for visualizing protein distribution in the human body are somehow lacking. Due to the challenges in accessing protein measurements directly biologists commonly use accessible web portals which provide RNA transcript read counts, which end up being used as a proxy for protein data. This is unfortunate because it is well established that the correlation between transcript counts and protein concentration in mammalian material is low. This is, however, a relatively vague concept that is difficult to both envision and to explain in the context of the central dogma. One way to rectify this problem would be by making protein quantification data as simple and accessible as RNA read count data. To illustrate how a large amount of proteomic data can be easily visualized, I have constructed an example lightweight HTML page that is small enough to be attached to email even when containing deep proteomic analysis of 29 healthy human tissues. The value of simple proteomic data representation is further demonstrated by overlaying proteins predicted to be organ specific from transcriptomic data. Displayed together, it is possible to rapidly determine which transcript measurements can be trusted to be predictive of protein specificity. All data, code and step-by-step analyses are made available in the Supporting Information and a web instance of the resulting tool comparing two gold standard datasets is hosted at: https://www.orsburnlab.org/protein-specificity.
ER -