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Updated: May 14, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
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Pooled shRNA screenings: computational analysis.

Jiyang Yu1, Preeti Putcha, Andrea Califano

  • 1Department of Biomedical Informatics, Columbia University, New York, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)
|January 30, 2013
PubMed
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Genome-wide RNA interference screening aids disease research and drug discovery. This chapter details computational methods for analyzing complex screening data from microarrays and sequencing, addressing challenges like noise and small sample sizes.

Area of Science:

  • Functional Genomics and Disease Research
  • Computational Biology and Bioinformatics

Background:

  • Genome-wide RNA interference (RNAi) screening is vital for understanding disease mechanisms and identifying therapeutic targets.
  • Commercial short hairpin RNA (shRNA) libraries are widely employed in these studies.
  • Microarray and next-generation sequencing technologies are used to analyze RNAi screening outcomes.

Purpose of the Study:

  • To address the computational challenges in analyzing large-scale RNAi screening data.
  • To present pipelines and statistical methods for processing and analyzing microarray- and sequencing-based screening data.

Main Methods:

  • Discussion of data processing pipelines for RNAi screening.
  • Description of quality assessment techniques for screening data.

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MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells

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Last Updated: May 14, 2026

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
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Published on: September 4, 2019

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype
14:51

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Published on: June 17, 2022

MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells
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MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells

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  • Explanation of statistical methods for post-analysis of microarray and sequencing data.

    • Main Results:

    • Identification of key computational challenges including data noise and small sample sizes.
    • Outline of integrated approaches for handling diverse screening data types.

    Conclusions:

    • Effective computational analysis is crucial for maximizing the utility of RNAi screening data.
    • Standardized pipelines and robust statistical methods are needed for reliable interpretation of screening results in disease research.