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Related Concept Videos

Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
2D NMR: Overview of Heteronuclear Correlation Techniques01:18

2D NMR: Overview of Heteronuclear Correlation Techniques

Heteronuclear correlation spectroscopy is an analytical technique that investigates the coupling between different types of nuclei, often a proton and an X-nucleus, such as carbon-13 or nitrogen-15. This method is commonly used in nuclear magnetic resonance (NMR) spectroscopy to gain insights into complex chemical compounds' structural and compositional aspects. A typical heteronuclear correlation spectrum displays X-nucleus chemical shifts on one axis and a proton spectrum on the other axis.
¹H NMR: Interpreting Distorted and Overlapping Signals01:02

¹H NMR: Interpreting Distorted and Overlapping Signals

Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are slanted or...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Investigating the correspondence between transcriptomic and proteomic expression profiles using coupled cluster

Simon Rogers1, Mark Girolami, Walter Kolch

  • 1Department of Computing Science, University of Glasgow, Glasgow G128QQ, UK. srogers@dcs.gla.ac.uk

Bioinformatics (Oxford, England)
|November 1, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to analyze links between RNA and protein expression. The findings reveal a complex relationship, with strong correlations mainly observed in specific molecular machines.

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Area of Science:

  • Molecular Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Transcriptomics and proteomics offer large-scale expression data, often analyzed separately.
  • Growing interest in co-analyzing transcriptome and proteome data to understand expression coordination.
  • A key question is the nature and extent of the link between RNA and protein expression.

Purpose of the Study:

  • To develop a probabilistic model for analyzing transcriptome-proteome expression links.
  • To investigate the relationship between mRNA and protein expression profiles.
  • To identify conditions under which RNA and protein expression are highly correlated.

Main Methods:

  • Developed a coupled mixture model for probabilistic clustering of transcriptomic and proteomic data.
  • Applied the model to quantitative expression data from a human breast epithelial cell line (HMEC).
  • Incorporated Gene Ontology information for functional analysis of expression correlations.

Main Results:

  • The model reveals a complex, non-linear relationship between mRNA and protein expression clusters.
  • Most mRNA clusters are linked to multiple protein clusters, and vice versa.
  • High correlation between mRNA and protein expression is primarily observed in specific molecular complexes like ribosomes and cell adhesion complexes.

Conclusions:

  • The developed probabilistic model provides a flexible framework for co-analyzing transcriptomic and proteomic data.
  • The study demonstrates a complex coordination between RNA and protein expression, not a simple one-to-one relationship.
  • Specific molecular functions, particularly those involving large protein complexes, show stronger mRNA-protein expression linkage.