Despite the rapid advance in single-cell RNA sequencing (scRNA-seq) technologies within the last decade, single-cell transcriptome analysis workflows have primarily used gene expression data while isoform sequence analysis at the single-cell level still remains fairly limited. Detection and discovery of isoforms in single cells is difficult because of the inherent technical shortcomings of scRNA-seq data, and existing transcriptome assembly methods are mainly designed for bulk RNA samples. To address this challenge, we developed RNA-Bloom, an assembly algorithm that leverages the rich information content aggregated from multiple single-cell transcriptomes to reconstruct cell-specific isoforms. Assembly with RNA-Bloom can be either reference-guided or reference-free, thus enabling unbiased discovery of novel isoforms or foreign transcripts. We compared both assembly strategies of RNA-Bloom against five state-of-the-art reference-free and reference-based transcriptome assembly methods. In our benchmarks on a simulated 384-cell data set, reference-free RNA-Bloom reconstructed 37.9%–38.3% more isoforms than the best reference-free assembler, whereas reference-guided RNA-Bloom reconstructed 4.1%–11.6% more isoforms than reference-based assemblers. When applied to a real 3840-cell data set consisting of more than 4 billion reads, RNA-Bloom reconstructed 9.7%–25.0% more isoforms than the best competing reference-based and reference-free approaches evaluated. We expect RNA-Bloom to boost the utility of scRNA-seq data beyond gene expression analysis, expanding what is informatically accessible now.

中文翻译：

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RNA-Bloom 使单细胞转录组的无参考和参考引导的序列组装成为可能。

尽管在过去十年中单细胞 RNA 测序 (scRNA-seq) 技术取得了快速发展，但单细胞转录组分析工作流程主要使用基因表达数据，而单细胞水平的同种型序列分析仍然相当有限。由于scRNA-seq数据固有的技术缺陷，单细胞中异构体的检测和发现很困难，现有的转录组组装方法主要是为大量RNA样本设计的。为了应对这一挑战，我们开发了 RNA-Bloom，这是一种组装算法，它利用从多个单细胞转录组聚合的丰富信息内容来重建细胞特异性亚型。使用 RNA-Bloom 进行组装可以是参考指导的或无参考的，从而能够无偏见地发现新的同种型或外来转录本。我们将 RNA-Bloom 的两种组装策略与五种最先进的无参考和基于参考的转录组组装方法进行了比较。在我们对模拟 384 细胞数据集的基准测试中，无参考的 RNA-Bloom 比最好的无参考组装器重建了 37.9%–38.3% 的异构体，而参考引导的 RNA-Bloom 重建的异构体比最佳无参考组装器多 4.1%–11.6%基于参考的汇编器。当应用于包含超过 40 亿个读数的真实 3840 细胞数据集时，RNA-Bloom 重建的同种型比评估的最佳基于参考和无参考的竞争方法多 9.7%–25.0%。我们希望 RNA-Bloom 能够在基因表达分析之外提高 scRNA-seq 数据的效用，扩展现在信息上可访问的内容。