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

Censoring Survival Data01:09

Censoring Survival Data

627
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
627
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

680
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
680
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

65.6K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
65.6K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
9.0K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

692
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
692

You might also read

Related Articles

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

Sort by
Same author

NanoPop: Nanopore amplicon sequencing of <i>Salmonella</i> virulence genes to characterize complex mixed serovar populations using k-mers to overcome high sequencing error rates.

Microbial genomics·2026
Same author

Exploring the impact of myotonia on daily functioning in myotonic dystrophy: a patient-reported survey.

BMC neurology·2026
Same author

Aerocavin Is an Antibiotic with Potent and Specific Anti-Neisserial Activity.

ACS infectious diseases·2026
Same author

Ground squirrel coprolites preserve complex archives of ancient environmental DNA over 700,000 years.

Nature communications·2026
Same author

Quantitative tandem mass tag-based serum proteomics for longitudinal biomarker monitoring in Duchenne muscular dystrophy.

Clinical proteomics·2026
Same author

MDBiomarkers: A queryable biomarkers database integrating multiple serum and tissue datasets for Duchenne muscular dystrophy.

Journal of neuromuscular diseases·2026

Related Experiment Video

Updated: Mar 15, 2026

Generation of Genomic Deletions in Mammalian Cell Lines via CRISPR/Cas9
09:40

Generation of Genomic Deletions in Mammalian Cell Lines via CRISPR/Cas9

Published on: January 3, 2015

96.8K

Estimation of Gene Insertion/Deletion Rates with Missing Data.

Utkarsh J Dang1, Alison M Devault2, Tatum D Mortimer3

  • 1Departments of Biology and Mathematics and Statistics, McMaster University, Hamilton, Ontario L8S-4L8, Canada.

Genetics
|August 28, 2016
PubMed
Summary
This summary is machine-generated.

This study models bacterial evolution by analyzing gene insertion and deletion rates, accounting for missing data. The findings offer insights into genome evolution and reduction mechanisms.

Keywords:
gene insertion/deletionindel ratesmaximum likelihoodunobserved data

More Related Videos

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K
Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library
07:28

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Published on: January 10, 2025

766

Related Experiment Videos

Last Updated: Mar 15, 2026

Generation of Genomic Deletions in Mammalian Cell Lines via CRISPR/Cas9
09:40

Generation of Genomic Deletions in Mammalian Cell Lines via CRISPR/Cas9

Published on: January 3, 2015

96.8K
Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

12.7K
Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library
07:28

Identification of Functionally-Relevant Lentivirus Integration Sites in an Insertional Mutagenesis Cell Library

Published on: January 10, 2025

766

Area of Science:

  • Evolutionary Biology
  • Genomics
  • Bioinformatics

Background:

  • Lateral gene transfer is a key driver of bacterial evolution.
  • Accurate modeling of gene insertion and deletion rates is crucial for understanding genome dynamics.
  • Existing models may not adequately handle missing data, potentially skewing evolutionary inferences.

Purpose of the Study:

  • To develop and validate a maximum-likelihood framework for modeling genome-wide gene insertion and deletion rates.
  • To incorporate the flexibility of modeling potential missing data within this framework.
  • To demonstrate novel applications in analyzing pseudogenization and genome reduction.

Main Methods:

  • Genome-wide analysis of gene insertion and deletion rates using a maximum-likelihood approach.
  • Incorporation of a flexible model for handling missing genomic data.
  • Application and validation using simulated data and empirical datasets from Gardnerella vaginalis and Mycobacterium spp.

Main Results:

  • The developed model accurately estimates gene insertion and deletion rates, even with missing data.
  • The framework successfully illustrates evolutionary dynamics in Gardnerella vaginalis.
  • Novel insights into pseudogenization and genome reduction magnitudes were obtained from Mycobacterium spp. data.

Conclusions:

  • The proposed maximum-likelihood framework provides a robust method for studying bacterial genome evolution.
  • The ability to model missing data enhances the accuracy of evolutionary rate estimations.
  • The indelmiss R package offers a valuable tool for researchers studying genome dynamics and evolution.