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

Mismatch Repair

Mismatch Repair

Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...

You might also read

Related Articles

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

Sort by

Same author

RadAnnotate: Large Language Models for Efficient and Reliable Radiology Report Annotation.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026

Same author

Intelligent documentation in medical education: can AI replace manual case logging?

JAMIA open·2026

Same author

Layer-specific genetic variation unlocks secondary metabolite diversity in long-lived clonal peppermint.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

The impact of the Social Exposome in Cardiovascular Health and Disease.

Journal of precision medicine (Amsterdam, Netherlands)·2026

Same author

Natural language processing of biomedical text to map and prioritize protein-disease associations in HFpEF.

Computers in biology and medicine·2026

Same author

Transcriptomic analysis of Thai rice varieties at the reproductive stage uncovers potential genetic determinants of salt tolerance.

BMC plant biology·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 31, 2026

Development of Targeting Induced Local Lesions IN Genomes (TILLING) Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Development of Targeting Induced Local Lesions IN Genomes (TILLING) Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Published on: July 16, 2019

Statistical mutation calling from sequenced overlapping DNA pools in TILLING experiments.

Victor Missirian¹, Luca Comai, Vladimir Filkov

¹Department of Computer Science, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.

BMC Bioinformatics

|July 16, 2011

Summary

This summary is machine-generated.

A new probabilistic method accurately detects mutations in large populations using TILLING (Targeting induced local lesions IN genomes) and next-generation sequencing data. This approach enhances mutation discovery in functional genomics research.

More Related Videos

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Published on: September 2, 2019

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Related Experiment Videos

Last Updated: May 31, 2026

Development of Targeting Induced Local Lesions IN Genomes (TILLING) Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Development of Targeting Induced Local Lesions IN Genomes (TILLING) Populations in Small Grain Crops by Ethyl Methanesulfonate Mutagenesis

Published on: July 16, 2019

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

Published on: September 2, 2019

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Targeting induced local lesions IN genomes (TILLING) is a key reverse genetics technique.
High-throughput sequencing and overlapping pool design enhance TILLING efficiency.
TILLING offers an economical platform for functional genomics in diverse organisms.

Purpose of the Study:

To develop a probabilistic method for identifying TILLING-induced mutations and carriers from sequencing data.
To assess the method's performance on variable quality, high-throughput sequences.
To improve mutation detection in large, mutagenized populations.

Main Methods:

Utilized a probabilistic approach applying Bayes' Theorem.
Employed a simplified binomial model accounting for sequencing error and mutation.
Incorporated sequence coverage levels into the analysis.
Tested on wheat and rice mutagenized populations.

Main Results:

Achieved high sensitivity (92.5%) and specificity (99.8%) in mutation discovery.
Outperformed existing SNP detection methods for real mutations.
Demonstrated superior performance with coverage variability and low-quality sequence data.

Conclusions:

The developed method effectively discovers mutations in large populations.
The approach is robust across varying sequencing data quality and coverage.
An implementation is available for broader application in functional genomics.