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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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Updated: Mar 16, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

Accessing the High-Throughput Screening Data Landscape.

Daniel P Russo¹, Hao Zhu²

¹Department of Chemistry, Rutgers Center for Computational and Integrative Biology, Rutgers University, 315 Penn Street, Camden, NJ, 08102, USA.

Methods in Molecular Biology (Clifton, N.J.)

|August 13, 2016

Summary

This summary is machine-generated.

High-throughput screening (HTS) generates vast biological data. This guide details accessing public repositories like PubChem for large compound datasets, overcoming challenges with diverse chemical identifiers for efficient data retrieval.

Keywords:

Biological data Chemical identifier Compounds PubChem

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Area of Science:

Chemical Biology
Bioinformatics
Data Science

Background:

High-throughput screening (HTS) generates extensive biological data from compound testing.
Public repositories like PubChem aggregate this HTS data, updating daily.
Diverse chemical identifiers (SMILES, InChIKey, IUPAC name) complicate data retrieval without a universal standard.

Purpose of the Study:

To guide users in accessing public chemical and biological data repositories.
To provide methods for automatically downloading large datasets of compound information.
To address the challenge of retrieving biological data for thousands of compounds efficiently.

Main Methods:

Exploration of data access strategies for public repositories (e.g., PubChem).
Focus on techniques for batch downloading of chemical and biological data.
Demonstration of methods to handle various chemical identifiers for large-scale queries.

Main Results:

Established procedures for programmatic access to HTS data.
Successful retrieval of biological data for large compound libraries.
Demonstrated efficiency gains in data acquisition for computational modeling.

Conclusions:

Automated data retrieval from public repositories is feasible and crucial for large-scale studies.
Standardized access methods can significantly accelerate research in drug discovery and chemical biology.
Overcoming identifier challenges is key to unlocking the full potential of HTS data.