Steric-blocking oligonucleotides (SBOs) are short, single-stranded nucleic acids designed to modulate gene expression by binding to RNA transcripts and blocking access from cellular machinery such as splicing factors. SBOs have the potential to bind to near-complementary sites in the transcriptome, causing off-target effects. In this study, we used RNA-seq to evaluate the off-target differential splicing events of 81 SBOs and differential expression events of 46 SBOs. Our results suggest that differential splicing events are predominantly hybridization driven, whereas differential expression events are more common and driven by other mechanisms (including spurious experimental variation). We further evaluated the performance of in silico screens for off-target splicing events, and found an edit distance cutoff of three to result in a sensitivity of 14% and false discovery rate (FDR) of 99%. A machine learning model incorporating splicing predictions substantially improved the ability to prioritize low edit distance hits, increasing sensitivity from 4% to 26% at a fixed FDR of 90%. Despite these large improvements in performance, this approach does not detect the majority of events at an FDR <99%. Our results suggest that in silico methods are currently of limited use for predicting the off-target effects of SBOs, and experimental screening by RNA-seq should be the preferred approach.

中文翻译：

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空间阻断寡核苷酸的转录组范围内的脱靶效应

空间阻断寡核苷酸 (SBO) 是短的单链核酸，旨在通过与 RNA 转录物结合并阻断剪接因子等细胞机制的进入来调节基因表达。SBO 有可能与转录组中接近互补的位点结合，从而引起脱靶效应。在本研究中，我们使用RNA-seq评估了81个SBO的脱靶差异剪接事件和46个SBO的差异表达事件。我们的结果表明差异剪接事件主要是杂交驱动的，而差异表达事件更常见并由其他机制（包括虚假实验变异）驱动。我们进一步评估了计算机屏幕对脱靶剪接事件的性能，发现编辑距离截止值为 3 时，灵敏度为 14%，错误发现率 (FDR) 为 99%。结合剪接预测的机器学习模型大大提高了优先考虑低编辑距离命中的能力，在固定 FDR 为 90% 时将灵敏度从 4% 提高到 26%。尽管性能有了很大改进，但这种方法无法检测到 FDR <99% 的大多数事件。我们的结果表明，计算机方法目前在预测 SBO 的脱靶效应方面用途有限，通过 RNA-seq 进行实验筛选应该是首选方法。