Jove
Visualize
Contact Us

Related Concept Videos

Gibbs Free Energy02:39

Gibbs Free Energy

One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
Gibbs Free Energy and Thermodynamic Favorability02:23

Gibbs Free Energy and Thermodynamic Favorability

The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:
Systematic Sampling Method01:17

Systematic Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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.
Systematic sampling is one of the simplest methods...
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...
Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures 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. Among the various sampling methods used by...

You might also read

Related Articles

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

Sort by
Same author

Identification of an episignature for CHD3-related Snijders Blok-Campeau syndrome reveals heterogeneity in the CHARGE syndrome episignature: towards a better characterisation of chromatinopathies.

Genome medicine·2026
Same author

Web-Based Discovery of Regulatory Motifs in Non-model Plants.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

Identification of novel type 1 and type 2 diabetes genes by co-localization of human islet eQTL and GWAS variants with colocRedRibbon.

Cell genomics·2025
Same author

Effects of light regimes on circadian gene co-expression networks in <i>Arabidopsis thaliana</i>.

Plant direct·2024
Same author

USP7/Maged1-mediated H2A monoubiquitination in the paraventricular thalamus: an epigenetic mechanism involved in cocaine use disorder.

Nature communications·2023
Same author

FAIR+E pathogen data for surveillance and research: lessons from COVID-19.

Frontiers in public health·2023
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
See all related articles
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 Experiment Video

Updated: Jun 20, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

info-gibbs: a motif discovery algorithm that directly optimizes information content during sampling.

Matthieu Defrance1, Jacques van Helden

  • 1Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles CP 263, Campus Plaine, Boulevard du Triomphe, B-1050 Bruxelles, Belgium. defrance@bigre.ulb.ac.be

Bioinformatics (Oxford, England)
|August 20, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces info-gibbs, a novel algorithm for discovering cis-regulatory elements by optimizing motif information content (IC) during the search process. This approach enhances motif discovery accuracy and efficiency compared to existing methods.

More Related Videos

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Related Experiment Videos

Last Updated: Jun 20, 2026

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying cis-regulatory elements in genome sequences is a significant challenge in molecular biology.
  • Current methods often use information content (IC) or relative entropy as post-hoc statistics, not integral parts of the motif search.
  • Transcription factor DNA binding affinity is well-estimated by motif IC, but its direct application in discovery is limited.

Purpose of the Study:

  • To develop an efficient algorithm for discovering cis-regulatory elements.
  • To integrate motif information content (IC) or log-likelihood ratio (LLR) directly into the motif search process.
  • To improve the accuracy and speed of motif discovery techniques.

Main Methods:

  • Introduction of info-gibbs, a Gibbs sampling algorithm designed for motif discovery.
  • The algorithm directly optimizes the information content (IC) or log-likelihood ratio (LLR) of motifs.
  • Focuses on efficient computation while maintaining high performance.

Main Results:

  • info-gibbs demonstrates efficient optimization of motif IC/LLR, leading to low computation times.
  • The algorithm performs competitively against established methods such as MEME, BioProspector, Gibbs, and GAME.
  • Evaluated on both synthetic and real biological datasets, showing robust performance.

Conclusions:

  • Directly optimizing motif IC or LLR significantly enhances motif discovery techniques.
  • info-gibbs offers an efficient and effective approach for identifying cis-regulatory elements.
  • The study highlights the benefit of integrating scoring metrics directly into the search algorithm.