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The Retinoblastoma Gene01:20

The Retinoblastoma Gene

Tumor suppressor genes are normal genes that can slow down cell division, repair DNA mistakes, or program the cells for apoptosis in case of irreparable damage. Hence, they play an essential role in preventing the proliferation of damaged cells.
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<i>Sleeping Beauty</i> mutagenesis identifies <i>BACH2</i> and other regulators of CD8<sup>+</sup> T-cell exhaustion, persistence in vivo, and CAR-T cell function under tumor-associated chronic antigen stimulation.

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Related Experiment Video

Updated: May 28, 2026

Identification of Sleeping Beauty Transposon Insertions in Solid Tumors using Linker-mediated PCR
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NetCIS: a network-based common insertion site pipeline for case-control Sleeping Beauty screens.

Mathew J Fischbach1,2, Daniel E Reiter3, Wen Wang1

  • 1Department of Computer Science and Engineering, 200 Union St SE, University of Minnesota, Minneapolis, MN, 55455, United States.

Briefings in Bioinformatics
|April 8, 2026
PubMed
Summary

A new tool called NetCIS (network-based common insertion site analysis) helps researchers analyze genetic screens. It identifies key genetic mutations by comparing cases and controls, improving the discovery of cancer drivers and drug resistance mechanisms.

Keywords:
Sleeping Beautycase controlcommon insertion sitesforward genetic screensgraph/network

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Area of Science:

  • Genetics
  • Bioinformatics
  • Cancer Research

Background:

  • Forward genetic screens are essential for discovering genes controlling biological phenotypes.
  • The Sleeping Beauty (SB) transposon system is a widely used tool for insertional mutagenesis in genetic screens.
  • Previous SB screens have identified cancer drivers, drug resistance mechanisms, and immunotherapy targets.

Purpose of the Study:

  • To develop a novel analysis tool for Sleeping Beauty transposon screen data.
  • To enable data-driven case-control comparisons for identifying significant common insertion sites (CISes).
  • To address limitations in existing tools for analyzing screens with case-control designs.

Main Methods:

  • Developed NetCIS, a network-based analysis tool utilizing a graph-based algorithm.
  • Implemented NetCIS for robust identification of statistically significant CISes between cases and controls.
  • Benchmarked NetCIS against existing insertional mutagenesis analysis tools using a published SB dataset.

Main Results:

  • NetCIS effectively identifies statistically significant CISes in case-control screens.
  • The tool prioritizes biologically validated genes comparably or superiorly to existing methods.
  • NetCIS uniquely identifies CISes in unannotated genomic regions, which are often missed by other tools.

Conclusions:

  • NetCIS provides a robust and efficient method for analyzing Sleeping Beauty transposon screen data.
  • The tool enhances the discovery of novel genes and mechanisms underlying various biological phenotypes.
  • NetCIS supports advanced genetic screening designs, particularly case-control comparisons, advancing biological discovery.