Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Proteomics01:33

Proteomics

8.3K
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...
8.3K
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.1K
Biostatistics: Overview01:20

Biostatistics: Overview

394
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
394
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.9K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
13.9K
Ribosome Profiling02:24

Ribosome Profiling

3.7K
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...
3.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ANARCII enables alignment-free antigen receptor numbering using a generalised language model.

Communications biology·2026
Same author

iNOS modulates inflammatory responses in an NO-independent manner through direct interaction with IRG1 in mitochondria.

Nature metabolism·2026
Same author

Ginkgo Datapoints Antibody Developability Competition outcomes: limited model performance and a call for data standardization.

mAbs·2026
Same author

LICHEN enables light-chain immunoglobulin sequence generation conditioned on the heavy chain and experimental needs.

Communications biology·2026
Same author

Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler.

Communications biology·2026
Same author

Identification of an allosteric site on the E3 ligase adapter cereblon.

Nature·2026
Same journal

Deep Plasma Proteomics-Based Diagnostic Panel for Early Detection of Amnestic Mild Cognitive Impairment.

Journal of proteome research·2026
Same journal

Proteomic and Phosphoproteomic Characterization of Disease-Associated Alterations in Nerve Terminals and Protein Inclusions of Alzheimer's Disease Patients.

Journal of proteome research·2026
Same journal

Proteomic Profiling of Endothelial Cells Under Laminar Shear Stress Confirms the Importance of KLF4 in the Regulation of Membrane Protein Expression Compared to Oscillatory Flow.

Journal of proteome research·2026
Same journal

Identification of Age-Associated Circulating Proteins and Lipids in 3800 Comorbidity-Enriched Older Adults from Japan-Based Cohorts Using Olink Assays and MRM Mass Spectrometry.

Journal of proteome research·2026
Same journal

Molecular Solution to the Paradox of Ancient Brain Preservation.

Journal of proteome research·2026
Same journal

From Method-Defined Signals to Reference Measurement Procedures: Two Decades of Mass Spectrometry-Based ProGRP Quantification.

Journal of proteome research·2026
See all related articles

Related Experiment Video

Updated: Oct 1, 2025

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

3.3K

Challenges and Opportunities for Bayesian Statistics in Proteomics.

Oliver M Crook1, Chun-Wa Chung2, Charlotte M Deane1

  • 1Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom.

Journal of Proteome Research
|March 8, 2022
PubMed
Summary
This summary is machine-generated.

Bayesian statistics offers a powerful way to analyze complex proteomics data by quantifying uncertainty. This approach enables more nuanced interpretations than traditional methods, matching the sophistication of modern experiments.

Keywords:
Bayesian statisticsmass spectrometryphase-separationproteomicsuncertaintyworkflow

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
09:52

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

790

Related Experiment Videos

Last Updated: Oct 1, 2025

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

3.3K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
09:52

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

790

Area of Science:

  • Proteomics
  • Statistical Modeling
  • Bioinformatics

Background:

  • Proteomics generates large, complex datasets requiring advanced statistical analysis.
  • Current methods often provide point estimates, limiting nuanced interpretation of uncertainty.
  • Bayesian statistics offers a framework to quantify uncertainty using probability distributions.

Purpose of the Study:

  • To review the application of Bayesian methods in proteomics.
  • To demonstrate the power and challenges of Bayesian approaches in this field.
  • To illustrate Bayesian modeling with a case study on dynamic OOPS data.

Main Methods:

  • Review of existing Bayesian statistical methodologies for proteomics.
  • Explanation of Bayesian principles for uncertainty quantification.
  • Development and walkthrough of a Bayesian model for specific proteomics data (dynamic OOPS).

Main Results:

  • Bayesian statistics allows for nuanced interpretation of proteomics data by quantifying uncertainty.
  • The modular framework of Bayesian analysis explicitly models parameter dependencies.
  • Complex experimental designs in proteomics can be matched by sophisticated Bayesian models.

Conclusions:

  • Bayesian statistics provides a robust framework for analyzing complex proteomics data.
  • Adopting Bayesian methods in proteomics presents both opportunities and challenges.
  • The presented model illustrates the practical application of Bayesian inference in proteomics research.