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Related Concept Videos

Proteomics01:33

Proteomics

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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...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
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Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
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Therapeutic Drug Monitoring: Drug Analysis Methods01:26

Therapeutic Drug Monitoring: Drug Analysis Methods

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Therapeutic Drug Monitoring (TDM) is a clinical practice that measures specific drug levels in a patient's blood or body tissues to tailor drug therapy effectively. This monitoring is critical for managing drugs with narrow therapeutic indices like digoxin and phenytoin, ensuring they are both safe and effective. For instance, monitoring theophylline levels in asthma patients involves precision and sensitivity to adjust doses according to individual responses to therapy, ensuring efficacy and...
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Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Updated: Feb 8, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Expanding the Proteomics and Metabolomics Toolkit with Methods for Differential Expression Analysis from

Sebastian Dohm-Hansen1,2,3, Maria Giovanna Caruso1,2, Sarah Nicolas1,2

  • 1Department of Anatomy and Neuroscience, University College Cork, Cork T12 XF62, Ireland.

Journal of Proteome Research
|February 6, 2026
PubMed
Summary
This summary is machine-generated.

Differential expression analysis (DEA) tools from transcriptomics can effectively address challenges in proteomics and metabolomics. These methods help manage issues like sample quality, batch effects, normalization, and small sample sizes in discovery omics.

Keywords:
batch effectsbioinformaticsdifferential expression analysismetabolomicsmultiomicsnormalizationproteomicssample qualitytranscriptomics

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Area of Science:

  • Life Sciences
  • Bioinformatics
  • Data Analysis

Background:

  • Discovery omics, including transcriptomics, proteomics, and metabolomics, are increasingly adopted in life sciences.
  • Numerous analysis tools for differential expression analysis (DEA) exist, often tailored to specific omics modalities.
  • Common statistical and distributional properties across different omics data allow for cross-application of analysis tools.

Purpose of the Study:

  • To demonstrate the applicability of transcriptomics-derived DEA tools to proteomics and metabolomics.
  • To showcase how established DEA methods can solve common challenges in discovery proteomics and metabolomics.
  • To provide a practical guide for researchers utilizing cross-omics DEA approaches.

Main Methods:

  • Utilizing existing differential expression analysis (DEA) tools originally developed for transcriptomics.
  • Applying these tools to real-world datasets from discovery proteomics and metabolomics experiments.
  • Illustrating the methods' effectiveness in addressing issues like variable sample quality, batch effects, normalization, and small sample sizes.

Main Results:

  • Transcriptomics DEA tools successfully address common challenges in discovery proteomics and metabolomics.
  • Demonstrated practical solutions for variable sample quality, hidden batch effects, normalization, and small sample size issues.
  • Validated the cross-applicability of DEA methods across different omics data types.

Conclusions:

  • Tools developed for transcriptomics DEA are highly suitable for discovery proteomics and metabolomics.
  • Researchers can leverage existing transcriptomics tools to enhance their analyses in other omics fields.
  • This approach expands the toolkit for both novice and experienced practitioners, facilitating multi-omics and integrative analyses.