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

Trihybrid Crosses

Trihybrid Crosses

Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...

Genetic Screens

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

Punnett Squares

Punnett Squares

Chi-square Analysis

Chi-square Analysis

The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...

Pedigree Analysis

Pedigree Analysis

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

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

Sort by

Same author

Tet-ON and Tet-OFF regulation in hypervirulent <i>Klebsiella pneumoniae</i>.

Microbiology spectrum·2026

Same author

Molecular epidemiology of community-acquired MRSA (CA-MRSA) and hospital-acquired MRSA (HA-MRSA): sequence types, virulence profiles, and antimicrobial resistance in a Belgian hospital network.

Infection·2026

Same author

Urosepsis by multidrug-resistant <i>Nakaseomyces glabratus</i> with non-functional Erg3 and Erg11-do collateral sensitivity and a unique mode of action make nitroxoline a viable UTI antifungal?

mBio·2026

Same author

The role of whole genome sequencing in antimicrobial susceptibility prediction of bacteria: 2025 update from the European Committee on Antimicrobial Susceptibility Testing Subcommittee.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026

Same author

Clinical and genomic profiling of <i>Klebsiella pneumoniae</i> in liver abscesses.

Virulence·2026

Same author

Reinfection and ceftriaxone tolerance in a clinical case of recurrent gonorrhoea: a case report supported by <i>in vitro</i> and <i>in vivo</i> models.

Frontiers in cellular and infection microbiology·2026

Search research articles

Related Experiment Video

Updated: Dec 12, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

ResFinder 4.0 for predictions of phenotypes from genotypes.

Valeria Bortolaia¹, Rolf S Kaas¹, Etienne Ruppe²

¹Technical University of Denmark, National Food Institute, European Union Reference Laboratory for Antimicrobial Resistance, WHO Collaborating Centre for Antimicrobial Resistance in Foodborne Pathogens and Genomics, FAO Reference Laboratory for Antimicrobial Resistance, Kgs. Lyngby, Denmark.

The Journal of Antimicrobial Chemotherapy

|August 12, 2020

Summary

Whole genome sequencing (WGS)-based antimicrobial susceptibility testing (AST) is reliable, but requires bioinformatics skills. This study improved WGS-based AST tools, making them faster and easier to interpret for clinical use.

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Related Experiment Videos

Last Updated: Dec 12, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Rapid Analysis and Exploration of Fluorescence Microscopy Images

Published on: March 19, 2014

Area of Science:

Microbiology
Genomics
Computational Biology

Background:

Whole genome sequencing (WGS)-based antimicrobial susceptibility testing (AST) offers a reliable alternative to traditional phenotypic AST.
WGS-based AST is currently limited by the need for specialized bioinformatics expertise and complex data interpretation.
Existing tools often require significant technical knowledge, hindering widespread adoption in routine diagnostics.

Purpose of the Study:

To enhance the speed and interpretability of WGS-based AST.
To develop an easily interpretable antibiogram output from WGS data.
To leverage freely accessible tools like ResFinder and PointFinder for broader usability.

Main Methods:

Rewriting ResFinder code for raw read processing using Kmer-based alignment.
Expanding and revising existing ResFinder and PointFinder databases.
Developing new databases, including a genotype-to-phenotype key and species-specific panels for in silico antibiograms.
Validating ResFinder 4.0 on diverse bacterial species (E. coli, Salmonella spp., C. jejuni, E. faecium, E. faecalis, S. aureus) from various sources and origins.

Main Results:

Achieved genotype-phenotype concordance of ≥95% for 46/51 Gram-negative and 25/32 Gram-positive antimicrobial/species combinations.
Discrepancies at lower concordance rates were primarily attributed to phenotypic interpretation criteria and sequence quality, not tool performance.
ResFinder 4.0 demonstrated high accuracy in predicting antimicrobial susceptibility from WGS data.

Conclusions:

WGS-based AST using ResFinder 4.0 provides reliable in silico antibiograms comparable to phenotypic AST.
The enhanced tool offers a more accessible and interpretable solution for WGS-based AST.
This advancement supports the routine clinical application of WGS for antimicrobial resistance surveillance and diagnostics.