Open-access synthetic spike-in mRNA-seq data for cancer gene fusions

摘要: Oncogenic fusion genes underlie the mechanism of several common cancers. Next-generation sequencing based RNA-seq analyses have revealed an increasing number recurrent fusions in a variety However, absence publicly available gene-fusion focused data impedes comparative assessment and collaborative development novel gene detection algorithms. We generated nine synthetic poly-adenylated RNA transcripts that correspond to previously reported oncogenic fusions. These RNAs were spiked at known molarity over wide range into total prior construction next-generation mRNA libraries generate data. Leveraging priori knowledge about replicates each transcript, we demonstrate utility this dataset compare multiple algorithms’ ability. In general, more are detected higher molarity, indicating our constructs performed as expected. systematic differences observed on or algorithm-specific characteristics. Fusion-sequence specific indicate for applications where sequences being investigated, additional may be added provide quantitative is sequence interest. To knowledge, first specifically leverages cancer gene-fusions. The proposed method designing allows granular performance fusion-detection community can leverage augment further analytical tools frameworks from