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Title: DEEPEST-Fusion      
aggregation:
digital object
privacy:
not applicable
refinement:
uncurated
ID:
https://doi.org/10.6084/m9.figshare.7812335.v1
storedIn:
Multiple: Github, figshare, Cancer Genomics Cloud
availability:
Available
creators:
Roozbeh Dehghannasiri, Milos Jordanski, Julia Salzman
keywords:
Gene fusions, RNA-Seq, Bioinformatics, Cancer Genomics
description:
This is a statistical fusion detection algorithm particularly engineered for screening big RNA sequencing databases
authors:
Gillian Hsieh, Rob Bierman, Linda Szabo, Alex Gia Lee, Donald E. Freeman, Nathaniel Watson, Alejandro Sweet-Cordero, Julia Salzman
publicationVenue:
Oxford Academic
description:
Gene fusions are known to play critical roles in tumor pathogenesis. Yet, sensitive and specific algorithms to detect gene fusions in cancer do not currently exist. In this paper, we present a new statistical algorithm, MACHETE (Mismatched Alignment CHimEra Tracking Engine), which achieves highly sensitive and specific detection of gene fusions from RNA-Seq data, including the highest Positive Predictive Value (PPV) compared to the current state-of-the-art, as assessed in simulated data. We show that the best performing published algorithms either find large numbers of fusions in negative control data or suffer from low sensitivity detecting known driving fusions in gold standard settings, such as EWSR1-FLI1. As proof of principle that MACHETE discovers novel gene fusions with high accuracy in vivo, we mined public data to discover and subsequently PCR validate novel gene fusions missed by other algorithms in the ovarian cancer cell line OVCAR3. These results highlight the gains in accuracy achieved by introducing statistical models into fusion detection, and pave the way for unbiased discovery of potentially driving and druggable gene fusions in primary tumors.
ID:
https://doi.org/10.1093/nar/gkx453
title:
Statistical algorithms improve accuracy of gene fusion detection
dateReleased:
07-27-2017
name:
Not Applicable
name:
DEEPEST-Fusion
name:
Creative Commons Attribution License
landingPage: http://creativecommons.org/licenses/by-nc/4.0/
identifier:
R00 CA168987-03
funders:
National Institute of Health, National Science Foundation, National Cancer Institute, Stanford University School of Medicine
count:
1
title:
GitHub
storedIn:
Cancer Genomics Cloud
size:
3.7
unit:
MB
ID:
SCR:016270
name:
CEDAR Workbench
abbreviation:
CEDAR
homePage: https://cedar.metadatacenter.org
  • K01 AG044439/AG/NIA NIH HHS/United States

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