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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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deFuse: an algorithm for gene fusion discovery in tumor RNA-Seq data.

Andrew McPherson1, Fereydoun Hormozdiari, Abdalnasser Zayed

  • 1Centre for Translational and Applied Genomics, BC Cancer Agency, Vancouver, British Columbia, Canada.

Plos Computational Biology
|June 1, 2011
PubMed
Summary

A new computational method, deFuse, enhances the discovery of gene fusions in cancer using RNA-Seq data. This approach improves sensitivity and specificity, revealing novel gene fusions in ovarian cancer.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Somatic gene fusions are critical in cancer development, particularly lymphomas and sarcomas.
  • RNA-Sequencing (RNA-Seq) is valuable for identifying gene fusions in cancer transcriptomes.
  • Existing algorithms for gene fusion discovery from RNA-Seq data are underdeveloped.

Purpose of the Study:

  • To develop a novel computational method, deFuse, for improved gene fusion discovery in tumor RNA-Seq data.
  • To enhance sensitivity and specificity in identifying fusion sequences compared to existing methods.
  • To investigate the prevalence and impact of gene fusions in ovarian cancer.

Main Methods:

  • Developed deFuse, a computational method considering all alignments and potential fusion boundaries.
  • Curated a validation set of 60 true positive and 61 true negative fusion sequences.
  • Trained an adaboost classifier using 11 novel sequence features, achieving an AUC of 0.91.

Main Results:

  • deFuse demonstrated superior sensitivity in identifying fusion sequences.
  • The adaboost classifier achieved high specificity.
  • Discovered novel gene fusions in 40 ovarian tumor samples and one cell line, identifying them as potentially frequent events in ovarian cancer.

Conclusions:

  • Gene fusions are not infrequent in ovarian cancer and can significantly alter gene expression.
  • deFuse is a sensitive and specific tool for gene fusion discovery in cancer RNA-Seq data.
  • Gene fusions should be considered in comprehensive mutational profiling of ovarian cancer.