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Related Concept Videos

FISH - Fluorescent In-situ Hybridization02:07

FISH - Fluorescent In-situ Hybridization

Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
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Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...

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Updated: May 18, 2026

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Detecting and visualizing gene fusions.

Jochen Supper1, Claudia Gugenmus, Johannes Wollnik

  • 1Genomatix Software GmbH, Bayerstr. 85a, 80335 München, Germany. supper@genomatix.de

Methods (San Diego, Calif.)
|October 6, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new platform for detecting gene fusions using Next-generation sequencing (NGS) and transcript annotation. The method identifies novel gene structures, aiding in biomarker discovery for diagnostics and therapies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene fusions are increasingly recognized as valuable biomarkers for cancer treatment decisions.
  • Detecting gene fusions requires specialized analysis of Next-generation sequencing (NGS) transcriptome data (RNA-seq).
  • Standard RNA-seq analysis often focuses on known genes, necessitating novel approaches for novel gene structures like fusions.

Purpose of the Study:

  • To present a novel computational approach for detecting gene fusion events using comprehensive transcript annotation.
  • To demonstrate the utility of this approach by identifying gene fusion candidates in breast cancer cell lines.
  • To introduce tools for visualizing and supporting detected gene fusions.

Main Methods:

  • Utilized a comprehensive transcript annotation (ElDorado) for gene fusion detection.
  • Analyzed RNA-seq data from eight breast cancer cell lines to identify gene fusion candidates.
  • Integrated copy number variation (CNV) analysis, literature mining, and network analyses for validation.
  • Developed the Transcriptome Viewer (TViewer) for interactive visualization of gene fusions.

Main Results:

  • Successfully detected gene fusion candidates in breast cancer cell lines, including specific events like BCAS3-BCAS4.
  • Demonstrated that gene fusions occur more frequently in copy number enriched regions.
  • Provided supporting evidence through literature mining and network analyses for detected fusions.
  • Showcased the BCAS3-BCAS4 fusion exclusively in the MCF7 breast cancer cell line.

Conclusions:

  • The developed platform enables robust detection and validation of gene fusions.
  • Enhanced visualization and literature-based support facilitate understanding of molecular processes and disease associations.
  • This approach advances the development of gene fusions as biomarkers for diagnostics and therapies.