Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.
Genome Biol. 2021 Aug 5;22(1):219. doi: 10.1186/s13059-021-02434-8.
Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN is a general method that can be applied to bulk or single-cell data, but has particular utility for single-cell analysis due to that data's unique challenges and opportunities for discovery. SICILIAN's precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, and increases agreement between biological replicates. SICILIAN detects unannotated splicing in single cells, enabling the discovery of novel splicing regulation through single-cell analysis workflows.
目前,10x Chromium 等 scRNA-seq 平台的拼接连接点调用不够精确,但这对理解单细胞生物学至关重要。在这里,我们介绍了 SICILIAN,这是一种新的方法,它可以为拼接比对器的拼接连接点分配统计置信度,从而提高精度。SICILIAN 是一种通用的方法,可应用于批量或单细胞数据,但由于该数据具有独特的发现挑战和机遇,因此特别适用于单细胞分析。SICILIAN 精确的拼接检测在模拟数据上具有很高的准确性,提高了匹配的单细胞和批量数据集之间的一致性,并增加了生物学重复之间的一致性。SICILIAN 在单细胞中检测到未注释的剪接,通过单细胞分析工作流程可以发现新的剪接调控。