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单细胞特异性剪接接头检测技术 SICILIAN

Specific splice junction detection in single cells with SICILIAN.

机构信息

Department of Biochemistry, Stanford University, Stanford, CA, 94305, USA.

Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA.

出版信息

Genome Biol. 2021 Aug 5;22(1):219. doi: 10.1186/s13059-021-02434-8.

Abstract

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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ffe0/8340526/f308ea24793d/13059_2021_2434_Fig1_HTML.jpg

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