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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...

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Related Experiment Video

Updated: Dec 29, 2025

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Published on: January 27, 2016

IDR2D identifies reproducible genomic interactions.

Konstantin Krismer^1,2, Yuchun Guo¹, David K Gifford^1,2,3

¹Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA.

Nucleic Acids Research

|February 4, 2020

Summary

This summary is machine-generated.

Chromatin interaction data analysis is improved with IDR2D, a new method identifying reproducible genome interactions. This tool filters out spurious signals, ensuring reliable insights into genome organization and gene regulation.

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Last Updated: Dec 29, 2025

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

DamID-seq: Genome-wide Mapping of Protein-DNA Interactions by High Throughput Sequencing of Adenine-methylated DNA Fragments

Published on: January 27, 2016

Associated Chromosome Trap for Identifying Long-range DNA Interactions

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Published on: April 23, 2011

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Chromatin interaction data (ChIA-PET, HiChIP, Hi-C) reveals genome organization and gene regulation.
These datasets can contain spurious interactions, complicating biological interpretation.

Purpose of the Study:

To introduce IDR2D, an extension of the Irreproducible Discovery Rate (IDR) method.
To identify replicable chromatin interactions shared across multiple experiments.
To eliminate artifacts present in single chromatin interaction datasets.

Main Methods:

Developed IDR2D, a novel computational method building upon the Irreproducible Discovery Rate (IDR).
Applied IDR2D to chromatin interaction datasets (e.g., ChIA-PET, HiChIP, Hi-C).
Implemented IDR2D as a Bioconductor package for R and an online service.

Main Results:

IDR2D successfully identifies a principled set of reproducible chromatin interactions.
The method effectively filters out spurious interactions and experimental artifacts.
Ensures higher confidence in the biological relevance of identified interactions.

Conclusions:

IDR2D enhances the reliability of chromatin interaction data analysis.
Provides a robust approach for identifying true genome organization patterns.
Facilitates accurate gene regulation studies by removing noise from experimental data.