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Prioritisation of structural variant calls in cancer genomes

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

No MeSH data available.


Binning of structural variants into 3 priorities.
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fig-1: Binning of structural variants into 3 priorities.

Mentions: The process is visualised in Fig. 1.


Prioritisation of structural variant calls in cancer genomes
Binning of structural variants into 3 priorities.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5382922&req=5

fig-1: Binning of structural variants into 3 priorities.
Mentions: The process is visualised in Fig. 1.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.

No MeSH data available.