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Related Concept Videos

RNA-seq03:21

RNA-seq

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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...
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Related Experiment Video

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions.

Krutika S Gaonkar1,2,3, Federico Marini4,5, Komal S Rathi1,2,3

  • 1Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.

BMC Bioinformatics
|December 15, 2020
PubMed
Summary

Researchers developed annoFuse, an R package, and shinyFuse, a web application, to effectively filter and prioritize gene fusions in cancer. These tools aid in identifying potential oncogenic drivers and therapeutic targets from RNA-Seq data.

Keywords:
Annotation toolCancerGene fusionsOncogenesRNA-seqShiny web application

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene fusions are key drivers in various cancers, presenting therapeutic opportunities.
  • Challenges exist in accurately identifying and prioritizing oncogenic gene fusions due to algorithm variability and artifacts.
  • Distinguishing true oncogenic fusions from artifacts is crucial for effective cancer therapy.

Purpose of the Study:

  • To develop annoFuse, an R package, and shinyFuse, a web application, for annotating, prioritizing, and exploring gene fusions.
  • To provide a standardized method for filtering and prioritizing putative oncogenic fusions from RNA-Seq data.
  • To facilitate the rapid evaluation and translation of gene fusion findings in patient tumors.

Main Methods:

  • Developed annoFuse (R package) and shinyFuse (web application) for gene fusion analysis.
  • Validated annoFuse on TCGA RNA-Seq samples, achieving 96% sensitivity for high-confidence fusions.
  • Applied annoFuse to pediatric brain tumor RNA-Seq data to identify recurrent fusions and fused genes.

Main Results:

  • annoFuse demonstrated high sensitivity (96%) in retaining high-confidence gene fusions.
  • The package filters non-oncogenic and artifactual fusions using FusionAnnotator annotations.
  • Prioritization criteria include TCGA reports, known oncogenes, tumor suppressors, COSMIC genes, and transcription factors.
  • Analysis of pediatric brain tumor data identified recurrent fusions and fused genes within specific histologies.
  • Tools annotate protein domains, assess reciprocality, and identify kinase domain retention.

Conclusions:

  • annoFuse offers standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba.
  • The package effectively merges, filters, and prioritizes oncogenic fusions across large cancer datasets.
  • Future expansion aims for broader applicability to other fusion-calling algorithms.
  • annoFuse is expected to accelerate the evaluation and translation of fusion findings for clinical application.