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

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Properties of Laplace Transform-II01:16

Properties of Laplace Transform-II

Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

You might also read

Related Articles

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

Sort by
Same author

Development of Novel mRNA Classifiers to Stratify Preoperative Thyroid Tumor Risk.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

Association of Fontan Circulation With Gut Microbiome-Derived Straight and Branched Short-Chain Fatty Acids.

Journal of gastroenterology and hepatology·2026
Same author

The Molecular Heterogeneity of NRAS Variants in Thyroid Nodules.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

High folate receptor expression is associated with aggressive features in prostate cancer with low prostate-specific membrane antigen expression.

BJUI compass·2026
Same author

Characterizing the Activity of Inflammasome-Related Genes and Their Association With Oncological Outcomes in Prostate Cancer.

The Prostate·2026
Same author

A Hybrid Deep Learning Approach for Performance Prediction in Optical Communication Systems Based on PON Scenarios.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 25, 2026

A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Adaptive machine learning technique for periodicity detection in biological sequences.

Faraz Rasheed1, Mohammed Alshalalfa, Reda Alhajj

  • 1Department of Computer Science, University of Calgary, Calgary, Canada. frasheed@ucalgary.ca

International Journal of Neural Systems
|March 6, 2009
PubMed
Summary

Researchers developed a new algorithm to find repeating patterns in DNA sequences, crucial for understanding gene regulation and chromatin organization. This method aids in identifying important DNA binding regions by analyzing dinucleotide periodicity.

More Related Videos

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Related Experiment Videos

Last Updated: Jun 25, 2026

A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
09:35

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • DNA sequences exhibit periodicity, particularly dinucleotide signals, with approximately a 10 base-pair period along nucleosomal DNA.
  • Positioned nucleosomes play a critical role in transcriptional regulation and chromatin organization within cell nuclei.
  • Identifying these periodic signals can indicate important functional regions within DNA, such as binding sites.

Purpose of the Study:

  • To describe and apply a novel dynamic periodicity detection algorithm for analyzing DNA and protein sequences.
  • To discover the periodicity of specific dinucleotides and alternative substrings (e.g., AA/TA/TT) within biological sequences.
  • To evaluate the algorithm's effectiveness, applicability, and resilience to noise compared to existing methods.

Main Methods:

  • Development and application of a dynamic periodicity detection algorithm.
  • Utilizing a suffix tree as the core data structure for sequence analysis.
  • Incorporating a dynamic window approach to detect periodicity of specific substring instances and considering alternative dinucleotides.

Main Results:

  • The algorithm successfully detects periodicity in DNA and protein sequences.
  • Demonstrated effectiveness in identifying periodic dinucleotide signals, indicative of functional regions.
  • The approach shows resilience to noise, outperforming some existing algorithms in comparative tests.

Conclusions:

  • The dynamic periodicity detection algorithm provides a robust method for analyzing sequence periodicity.
  • This algorithm can be applied to various data types and targets, aiding in the study of DNA organization and function.
  • The findings contribute to a better understanding of nucleosome positioning and its role in gene regulation.