SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq,Computational and Structural Biotechnology Journal

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SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq
Computational and Structural Biotechnology Journal ( IF 4.4 ) Pub Date : 2021-08-14 , DOI: 10.1016/j.csbj.2021.08.021
Mei-Wei Luan ₁ , Jia-Lun Lin ₂ , Ye-Fan Wang ₁ , Yu-Xiao Liu ₂ , Chuan-Le Xiao ₃ , Rongling Wu ₄ , Shang-Qian Xie ₁

Affiliation

SPLiT-seq provides a low-cost platform to generate single-cell data by labeling the cellular origin of RNA through four rounds of combinatorial barcoding. However, an automatic and rapid method for preprocessing and classifying single-cell sequencing (SCS) data from SPLiT-seq, which directly identified and labeled combinatorial barcoding reads and distinguished special cell sequencing data, is currently lacking. Here, we develop a high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq (SCSit), which can directly identify combinatorial barcodes and UMI of cell types and obtain more labeled reads, and remarkably enhance the retained data from SCS due to the exact alignment of insertion and deletion. Compared with the original method used in SPLiT-seq, the consistency of identified reads from SCSit increases to 97%, and mapped reads are twice than the original. Furthermore, the runtime of SCSit is less than 10% of the original. It can accurately and rapidly analyze SPLiT-seq raw data and obtain labeled reads, as well as effectively improve the single-cell data from SPLiT-seq platform. The data and source of SCSit are available on the GitHub website .

中文翻译：

SCSit：SPLiT-seq 单细胞测序数据的高效预处理工具

SPLiT-seq 提供了一个低成本平台，通过四轮组合条形码标记 RNA 的细胞起源，生成单细胞数据。然而，目前缺乏一种自动、快速的方法对来自 SPLiT-seq 的单细胞测序（SCS）数据进行预处理和分类，直接识别和标记组合条形码读数并区分特殊的细胞测序数据。在这里，我们开发了一种高效的SPLiT-seq（SCSit）单细胞测序数据预处理工具，可以直接识别细胞类型的组合条形码和UMI并获得更多标记的reads，并显着增强SCS的保留数据插入和删除的精确对齐。与SPLiT-seq中使用的原始方法相比，SCSit中识别的reads的一致性提高到97％，并且映射的reads是原始的两倍。此外，SCSit的运行时间还不到原来的10%。它可以准确、快速地分析SPLiT-seq原始数据并获得标记的reads，并有效改进SPLiT-seq平台的单细胞数据。 SCSit的数据和来源可以在GitHub网站上找到。

更新日期：2021-08-14

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