Comparing Long Read Fusion Callers using Simulated Read Data

Van Twisk, D.; Rubinsteyn, A.; Vincent, B.

doi:10.1101/2022.09.23.509226

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bioRxiv · 10.1101/2022.09.23.509226

Comparing Long Read Fusion Callers using Simulated Read Data

Van Twisk, D.·Rubinsteyn, A.·Vincent, B.

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Abstract

The advent of single-molecule third generation sequencing technologies provide new possibilities for the detection of fusion transcripts in sequencing data. Here, we test three long-read fusions detection tools on simulated data, compare various tooling parameters and compare the performance between long-read and short-read fusion detection tools. We also use our fusion transcript detection pipeline to describe fusions transcripts detected in U87 and U937 glioblastoma cell lines. We find that LongGF is the most capable of the long-read fusion detection tools at identifying the most simulated fusion transcripts. While the short read fusion transcript detection tool, Arriba, had similar recall to some of the long-read tools, its precision was found to be much lower. Several fusions with ample evidence were found in U87 and U937 cell lines.

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Van Twisk, D., Rubinsteyn, A., Vincent, B.. 2022-09-26. Comparing Long Read Fusion Callers using Simulated Read Data. https://doi.org/10.1101/2022.09.23.509226

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