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

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 result in visible changes...

You might also read

Related Articles

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

Sort by

Same author

Identification and functional validation of EPS15L1 as a key driver of triple-negative breast cancer.

NPJ breast cancer·2026

Same author

APOE ε4 and Accelerated Cognitive Decline Among Cognitively Healthy Middle-Aged and Older Adults.

JAMA network open·2026

Same author

Genetic evidence for causality of late chronotype on metabolic syndrome in East Asians and Europeans.

Sleep medicine·2026

Same author

Elucidating the Epigenetic Landscape of Type 2 Diabetes Mellitus: A Multi-Omics Analysis Revealing Novel CpG Sites and Their Association with Cardiometabolic Traits.

Diabetes & metabolism journal·2025

Same author

DRD1 gene is associated with craving changes during withdrawal among patients with alcohol use disorder.

Journal of psychiatric research·2025

Same author

Sex- and APOE Genotype-Dependent Pain Susceptibility and Alzheimer's Risk Mediated by the Lipid Metabolism Enzyme LPCAT2.

Aging cell·2025

Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026

Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026

Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026

Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026

Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026

Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 10, 2026

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

SeqSIMLA: a sequence and phenotype simulation tool for complex disease studies.

Ren-Hua Chung¹, Chung-Chin Shih

¹Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Miaoli, Taiwan. rchung@nhri.org.tw

BMC Bioinformatics

|June 21, 2013

Summary

This summary is machine-generated.

SeqSIMLA is a new simulation tool that generates next-generation sequencing (NGS) data for various disease and quantitative trait models. This tool aids in evaluating statistical power for study designs and comparing NGS statistical methods.

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Related Experiment Videos

Last Updated: May 10, 2026

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

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

Published on: August 20, 2019

Area of Science:

Genetics
Bioinformatics
Statistical Genetics

Background:

Next-generation sequencing (NGS) association studies are increasingly popular.
Rapid development of statistical tests for NGS data necessitates robust evaluation tools.
A flexible simulation tool is needed for planning NGS studies and comparing statistical methods.

Purpose of the Study:

To develop a flexible simulation tool for generating NGS sequence data.
To facilitate the evaluation of statistical power in study designs using NGS data.
To enable comparison of different statistical methods applied to NGS data.

Main Methods:

Developed SeqSIMLA, a simulation tool for sequence data.
Implemented user-specified disease models with flexible parameters (loci number, effect size, prevalence).
Implemented a quantitative trait model with specified quantitative trait loci (QTL) and variance proportions.
Incorporated HapMap recombination rates for realistic genomic structure simulation.

Main Results:

SeqSIMLA can efficiently simulate sequence data based on user-defined disease or quantitative trait models.
The tool allows flexible specification of genetic and disease parameters.
Simulated genomic structures resemble real data due to incorporated recombination rates.

Conclusions:

SeqSIMLA provides an efficient platform for simulating NGS data.
The tool is valuable for assessing statistical properties of new study designs and methods for NGS data.
SeqSIMLA is freely available for download.