Clinical implementation of MetaFusion for accurate cancer-driving fusion detection from RNA Sequencing

Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-Seq. However, in a clinical setting, identifying these alterations is challenging against a background of non-relevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-Seq, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole transcriptome and targeted sequencing datasets.

Source: Journal of Molecular Diagnostics - September 23, 2023 Category: Pathology Authors: Michael Apostolides, Michael Li, Anthony Arnoldo, Michelle Ku, Mia Husi ć, Arun K. Ramani, Michael Brudno, Andrei Turinsky, Cynthia Hawkins, Robert Siddaway Tags: Regular Article Source Type: research