Specific splice junction detection in single cells with SICILIAN

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's precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, increases agreement between biological replicates, and reliably detects un-annotated splicing in single cells, enabling the discovery of novel splicing regulation. Main text:Alternative splicing is essential for the specialized functions of eukaryotic cells, necessary for development 1 , and a greater contributor to genetic disease burden than mutations 2 . Despite the importance of splicing and massive RNA-seq data generated on single cells, the extent to which the diversity of RNA splicing in single cells is regulated and functional versus transcriptional noise remains contentious 3 .Given the resolution and massive number of available single-cell RNA-seq (scRNA-seq) datasets, precise quantification of splicing in single cells has great promise for discovering regulatory and functional splicing biology. While there are a number of methods developed for isoform quantification at the single-cell level 4,5 , a problem that has not been addressed is the precise discovery of splice junctions. There is a great need for precise junction detection: spliced aligners are designed for bulk RNA-seq and, in addition, generate many artifacts 6-10 , which will be referred to in this paper as "false positives". The problem is exacerbated in scRNA-seq analysis due to the high-level and single-cell-specific biochemical noise and multiple testing errors arising from the analysis of thousands of cells. This problem is typically addressed through the use of simple filters on junction calls, which remove many true positives, especially when the data is sparse such as scRNA-seq. Because of these challenges, there is debate

The SpliZ generalizes “Percent Spliced In” to reveal regulated splicing at single-cell resolution

“…Together these data support the use of the SZS to rapidly identify regulated splicing events during cellular differentiation and importance of including statistically supported un-annotated splicing events in the SZS computation 17 .…”

“…Here we applied the SZS to the output of SICILIAN 17 , a statistical post-filter on STAR 18 that removes false positive junction calls and enables discovery of unannotated splicing contributing to cell type-specific regulation.…”

“…The SpliZ can be applied to compiled junctional read counts from any single cell data (e.g. STAR-aligned 17 and SICILIAN 17,18 -processed plate or droplet-based scRNA-seq data) and, even more generally, to bulk data, though that is not the focus of this work. The SpliZ integrates all non-constitutive spliced reads on a per-gene basis to detect deviant splicing patterns in single cells under low and biased sampling (Fig.…”

Molecular topography of an entire nervous system

“…Differential splicing has been reported between individual neuron types in C. elegans (Moresco and Koelle, 2004;Norris et al, 2014;Thompson et al, 2019;Tomioka et al, 2016). However, despite recent progress (Dehghannasiri et al, 2020;Patrick et al, 2020) the strong 3' bias of the 10x Genomics scRNA-Seq method limits its use for distinguishing alternatively spliced transcripts (Arzalluz-Luqueángeles and Conesa, 2018). We thus took advantage of the bulk RNA-Seq profiles of specific neuron types to look for evidence of differential patterns of alternative splicing between C. elegans neurons.…”

High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs

“…The spliced RNA junction software SICILIAN 24 was used to sensitively and accurately identify leader-body junctions within each sample. Modifications to standard SICILIAN protocol were to the combined index and STAR mapping parameters described above ( Supplemental Table 2 ).…”