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Related Concept Videos

Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order to...
Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
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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,...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Sampling strategy for protein complex prediction using cluster size frequency.

Daisuke Tatsuke1, Osamu Maruyama

  • 1Graduate School of Mathematics, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan. ma211030@math.kyushu-u.ac.jp

Gene
|December 14, 2012
PubMed
Summary
This summary is machine-generated.

We developed PPSampler, a new method for predicting protein complexes using protein-protein interactions. This algorithm outperforms existing tools and identifies potentially novel complexes.

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Analyzing Large Protein Complexes by Structural Mass Spectrometry
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Analyzing Large Protein Complexes by Structural Mass Spectrometry
15:35

Analyzing Large Protein Complexes by Structural Mass Spectrometry

Published on: June 19, 2010

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Systems Biology

Background:

  • Protein complexes are crucial for cellular functions.
  • Existing protein complex prediction methods often struggle with smaller complexes.
  • Protein complex size distribution follows a power-law, with most complexes being small.

Purpose of the Study:

  • To develop an improved algorithm for predicting protein complexes from protein-protein interaction networks.
  • To address the limitations of density-based measures in predicting smaller complexes.
  • To leverage the power-law distribution of complex sizes in prediction.

Main Methods:

  • Proposed a novel Markov chain Monte Carlo sampling method called PPSampler (Proteins' Partition Sampler).
  • Utilized the Metropolis-Hastings algorithm.
  • Incorporated a parameter to control the target relative frequency of predicted complex sizes.

Main Results:

  • PPSampler demonstrated superior performance compared to existing protein complex prediction algorithms.
  • Approximately 50% of predicted clusters not found in the CYC2008 database showed statistical significance via Gene Ontology term enrichment.
  • These novel predictions are potential candidates for true protein complexes.

Conclusions:

  • PPSampler offers a more effective approach for protein complex prediction, particularly for smaller complexes.
  • The method successfully identifies statistically significant novel protein complexes.
  • This work contributes to a better understanding of protein complex organization and function.