Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Wald-Wolfowitz Runs Test II

Wald-Wolfowitz Runs Test II

The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and 0s. In...

Behrens–Fisher Test

Behrens–Fisher Test

The Behrens-Fisher test is a statistical method designed to address the Behrens-Fisher problem, which arises when comparing the means of two normally distributed populations with unequal variances. Unlike the Student's t-test, which assumes equal variances, the Behrens-Fisher test allows for mean comparison without this restrictive assumption. This flexibility makes it particularly valuable in scenarios where two independent samples exhibit normality but lack variance homogeneity.
This test is...

Sampling Distribution

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...

F Distribution

F Distribution

The F distribution was named after Sir Ronald Fisher, an English statistician. The F statistic is a ratio (a fraction) with two sets of degrees of freedom; one for the numerator and one for the denominator. The F distribution is derived from the Student's t distribution. The values of the F distribution are squares of the corresponding values of the t distribution. One-Way ANOVA expands the t test for comparing more than two groups. The scope of that derivation is beyond the level of this...

Uniform Distribution

Uniform Distribution

The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.Two essential properties of this distribution are The area under the rectangular shape equals 1. There is a correspondence between the probability of an event and the area under the curve.Further, the mean and standard deviation of the uniform distribution can be calculated when the lower and upper cut-offs, denoted as a and b,...

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

You might also read

Related Articles

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

Sort by

Same author

From Microcurrents to Macrodynamics: Harnessing Mixed Potentials for Large, Tunable Acceleration of Belousov-Zhabotinsky Oscillations.

Journal of the American Chemical Society·2026

Same author

Correction: Chemical degradation as an enabling pathway to polymersome functionalization.

RSC advances·2025

Same author

Droplet microfluidics, colloidal assembly and nanoscale processing: Synergistic control and properties of colloid-based photonic microobjects.

Advances in colloid and interface science·2025

Same author

Self-reproduction as an autonomous process of growth and reorganization in fully abiotic, artificial and synthetic cells.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Chemical degradation as an enabling pathway to polymersome functionalization.

RSC advances·2025

Same author

Interactions and Oscillatory Dynamics of Chemically Powered Soft Swimmers.

The journal of physical chemistry. B·2024

Search research articles

Related Experiment Video

Updated: May 22, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Genome sizes and the Benford distribution.

James L Friar¹, Terrance Goldman, Juan Pérez-Mercader

¹Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.

|May 26, 2012

Summary

The Benford distribution accurately models the number of Open Reading Frames (ORFs) in prokaryotic and eukaryotic genomes. This mathematical approach reveals insights into genome size limitations and information transmission efficiency.

Area of Science:

Genomics
Bioinformatics
Mathematical Biology

Background:

Genome size and the number of Open Reading Frames (ORFs) exhibit distinct patterns across the three domains of life.
Prokaryotes show a linear relationship between genome size and ORF count, while Eukaryotes display a logarithmic correlation.

Purpose of the Study:

To investigate the underlying mathematical principles governing ORF distribution in genomes.
To apply probability distribution functions to model genome organization and evolution.

Main Methods:

Utilized a dataset of over 1000 genomes available in early 2010.
Applied the Benford distribution model to analyze ORF counts in relation to genome size.
Assessed the fit of the Benford distribution to both prokaryotic and eukaryotic genomic data.

More Related Videos

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Related Experiment Videos

Last Updated: May 22, 2026

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Main Results:

The Benford distribution accurately predicts ORF numbers in Eukaryotes, following a specific logarithmic form.
Excellent fits were observed for the Benford distribution across several orders of magnitude in eukaryotic genomes.
The linear regime of the Benford distribution effectively models prokaryotic genome data.

Conclusions:

The Benford distribution provides a unified mathematical framework for understanding ORF distribution in both prokaryotes and eukaryotes.
Genomic differences between prokaryotes and eukaryotes can be interpreted through the lens of functional demands and information transmission.
The study estimates maximal prokaryotic genome size and provides insights into minimal genome sizes for eukaryotes.