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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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
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 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,...
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
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: Jun 22, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Proteome coverage prediction with infinite Markov models.

Manfred Claassen1, Ruedi Aebersold, Joachim M Buhmann

  • 1Department of Computer Science, Institute of Molecular Systems Biology, ETH Zurich, Competence Center for Systems Physiology and Metabolic Diseases, Zurich, Switzerland. manfredc@inf.ethz.ch

Bioinformatics (Oxford, England)
|May 30, 2009
PubMed
Summary
This summary is machine-generated.

Predicting proteome coverage is crucial for efficient proteomics studies. DiriSim, an extended infinite Markov model, accurately forecasts proteome coverage from initial liquid chromatography tandem mass spectrometry (LC-MS/MS) experiments, optimizing study design.

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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

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Related Experiment Videos

Last Updated: Jun 22, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
06:50

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

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
  • Computational Biology
  • Biochemistry

Background:

  • Liquid chromatography tandem mass spectrometry (LC-MS/MS) is the primary technique for analyzing complex protein mixtures.
  • Maximizing proteome coverage requires extensive, repeated LC-MS/MS experiments.
  • Reliable early-stage prediction of proteome coverage increase is currently lacking.

Purpose of the Study:

  • To develop a method for predicting proteome coverage progression in LC-MS/MS experiments.
  • To enhance the efficiency and design of proteomics studies through accurate coverage prediction.
  • To estimate the maximal achievable proteome coverage for a given sample.

Main Methods:

  • An extended infinite Markov model, named DiriSim, was developed.
  • DiriSim extrapolates proteome coverage progression using a small number of initial LC-MS/MS experiments.
  • The model explicitly incorporates uncertainty in peptide identifications.

Main Results:

  • DiriSim accurately predicted proteome coverage progression from a subset of 37 LC-MS/MS experiments on a complete proteome sample.
  • The predictions enabled the determination of maximal coverage for the tested sample.
  • Study findings indicate that quality requirements limit the number of effective experimental repetitions and achievable proteome coverage.

Conclusions:

  • DiriSim provides a reliable method for early-stage proteome coverage prediction in LC-MS/MS studies.
  • The model aids in optimizing experimental design and resource allocation in proteomics.
  • Understanding coverage progression is essential for setting realistic goals and avoiding redundant experiments.