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Related Concept Videos

Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Data Collection by Experiments01:13

Data Collection by Experiments

Data collection is a systematic method of obtaining, observing, measuring, and analyzing accurate information. An experimental study is a standard method of data collection that involves the manipulation of the samples by applying some form of treatment prior to data collection. It refers to manipulating one variable to determine its changes on another variable. The sample subjected to treatment is known as “experimental units.”
An example of the experimental method is a public clinical trial...
Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...

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Updated: May 30, 2026

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

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Published on: August 21, 2019

The modENCODE Data Coordination Center: lessons in harvesting comprehensive experimental details.

Nicole L Washington1, E O Stinson, Marc D Perry

  • 1Lawrence Berkeley National Laboratory, Genomics Division, 1 Cyclotron Road MS64-121, Berkeley, CA 94720, USA.

Database : the Journal of Biological Databases and Curation
|August 23, 2011
PubMed
Summary
This summary is machine-generated.

The modENCODE project characterizes genomes using a Data Coordination Center (DCC) to manage data. This DCC ensures comprehensive metadata, including experimental details and protocols, is available to researchers.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Model Organism Research

Background:

  • The Encyclopedia of DNA Elements (modENCODE) project aims to comprehensively characterize the genomes of model organisms.
  • A central Data Coordination Center (DCC) is essential for managing and disseminating the vast amounts of data generated by such large-scale initiatives.

Purpose of the Study:

  • To describe the design principles and operational aspects of the modENCODE Data Coordination Center (DCC).
  • To highlight the importance of collecting deep and thorough metadata for experimental data.
  • To detail the methods used for data organization, storage, and community access.

Main Methods:

  • Implementation of a Data Coordination Center (DCC) for data management.
  • Development and utilization of a wiki for capturing experimental protocols and reagent information.
  • Adoption of the BIR-TAB specification for linking biological samples to experimental results.

Main Results:

  • Established a robust system for collecting, storing, and cataloging modENCODE data.
  • Ensured availability of detailed metadata regarding experimental conditions, protocols, and verification checks.
  • Provided community access to primary, interpreted, and analyzed genomic data.

Conclusions:

  • An effective DCC is crucial for the success of large-scale genomic projects like modENCODE.
  • Thorough metadata collection and standardized data organization are vital for data reproducibility and usability.
  • The modENCODE DCC facilitates the accessibility and understanding of complex genomic datasets for the research community.