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

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...
Protein Networks02:26

Protein Networks

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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

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 polypeptide...
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...

You might also read

Related Articles

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

Sort by
Same author

Screen for Tissue-Specific Markers of Drug-Induced Phospholipidosis Using Mass Spectrometry Imaging.

Journal of the American Society for Mass Spectrometry·2026
Same author

Vδ1 T-cell subset appears to be responsive to PD-1 blockade therapy and is associated with survival in melanoma.

Journal for immunotherapy of cancer·2026
Same author

CAR-adapted PIK3CD base editing enhances T cell anti-tumor potency.

Nature cancer·2026
Same author

Continuous sPatial-temporal deformable image registration and 4D frame interpolation.

Medical physics·2025
Same author

Exploratory trajectory inference reveals convergent lineages for CD8 T cells in chronic LCMV infection.

PloS one·2025
Same author

Effects of segmentation errors on downstream-analysis in highly-multiplexed tissue imaging.

PLoS computational biology·2025
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

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

Published on: November 15, 2017

Proteome coverage prediction for integrated proteomics datasets.

Manfred Claassen1, Ruedi Aebersold, Joachim M Buhmann

  • 1Department of Computer Science, ETH Zurich, Zurich, Switzerland. manfred.claassen@inf.ethz.ch

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 10, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for predicting proteome coverage in mass spectrometry experiments. The method accurately estimates protein discoveries from integrated datasets, aiding efficient study design.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 3, 2026

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
10:37

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

Published on: November 15, 2017

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

Area of Science:

  • Proteomics
  • Bioinformatics
  • Computational Biology

Background:

  • Maximizing proteome coverage is crucial for comprehensive protein identification in complex mixtures.
  • Current methods involve multiple fractionation and mass spectrometry analyses, generating large, integrated datasets.
  • Reliable prediction of proteome coverage from these integrated datasets is lacking.

Purpose of the Study:

  • To develop a robust method for predicting proteome coverage from integrated proteomics datasets.
  • To enable efficient experimental design and planning in proteomics studies.
  • To estimate the maximal achievable proteome coverage for specific experimental setups.

Main Methods:

  • Development of a generalized hierarchical Pitman-Yor process model.
  • Explicitly modeling redundancy within integrated proteomics datasets.
  • Application and assessment on an integrated proteomics dataset for L. interrogans.

Main Results:

  • The proposed model accurately predicts proteome coverage from integrated datasets.
  • The method outperforms existing ad hoc and non-integrated dataset prediction approaches.
  • Estimation of the maximally achievable proteome coverage for the L. interrogans experimental setup was achieved.

Conclusions:

  • The developed model offers a reliable approach for proteome coverage prediction.
  • This facilitates rational determination of experimental stop criteria.
  • Improves the design of efficient and reliable proteomics studies.