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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...

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Related Experiment Video

Updated: May 7, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

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

Published on: October 19, 2021

Integrated analysis of transcriptomic and proteomic data.

Saad Haider1, Ranadip Pal

  • 1Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.

Current Genomics
|October 2, 2013
PubMed
Summary
This summary is machine-generated.

Joint analysis of transcriptomic and proteomic data reveals cellular regulatory insights beyond individual mRNA or protein expression studies. This review categorizes major approaches for integrated analysis, highlighting research challenges.

Keywords:
Combined analysis review.Data fusion approachesIntegrated omicsJoint modelingProteomeTranscriptome

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

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Last Updated: May 7, 2026

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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
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Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Published on: November 15, 2017

Area of Science:

  • * Molecular Biology
  • * Bioinformatics
  • * Systems Biology

Background:

  • * Traditional cell regulation studies focused independently on transcriptomics (mRNA) or proteomics (protein).
  • * The central dogma assumed a direct mRNA-protein correlation, but recent findings show low correlation due to post-transcriptional modifications and varying half-lives.
  • * Independent analyses limit understanding of complex cellular regulatory behaviors.

Purpose of the Study:

  • * To review and categorize existing major approaches for the joint analysis of transcriptomic and proteomic data.
  • * To provide a structured overview of integrated multi-omics analysis methodologies.
  • * To identify current research challenges and future directions in transcriptomics-proteomics integration.

Main Methods:

  • * Comprehensive literature review of existing joint analysis methods for transcriptomic and proteomic data.
  • * Categorization of identified approaches into eight main groups based on algorithmic strategies and analytical objectives.
  • * Comparative analysis and discussion of the strengths and limitations of different integrated methods.

Main Results:

  • * Eight distinct categories of joint transcriptomic and proteomic analysis approaches were identified and defined.
  • * The review highlights the necessity of integrated analysis for a more accurate understanding of cellular regulation.
  • * Analogies from other scientific domains were presented to contextualize the methodologies.

Conclusions:

  • * Joint analysis of transcriptomic and proteomic data offers deeper biological insights than single-modality approaches.
  • * A structured categorization of methods aids researchers in selecting appropriate analytical strategies.
  • * Further research is needed to address existing challenges in integrated multi-omics data analysis.