SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq
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 identif...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021003524 |
| _version_ | 1831641729102512128 |
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| author | Mei-Wei Luan Jia-Lun Lin Ye-Fan Wang Yu-Xiao Liu Chuan-Le Xiao Rongling Wu Shang-Qian Xie |
| author_facet | Mei-Wei Luan Jia-Lun Lin Ye-Fan Wang Yu-Xiao Liu Chuan-Le Xiao Rongling Wu Shang-Qian Xie |
| author_sort | Mei-Wei Luan |
| collection | DOAJ |
| description | 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 https://github.com/shang-qian/SCSit. |
| first_indexed | 2024-12-19T12:29:18Z |
| format | Article |
| id | doaj.art-417de8acbd6a433e89649a9119312e06 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-19T12:29:18Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-417de8acbd6a433e89649a9119312e062022-12-21T20:21:26ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011945744580SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seqMei-Wei Luan0Jia-Lun Lin1Ye-Fan Wang2Yu-Xiao Liu3Chuan-Le Xiao4Rongling Wu5Shang-Qian Xie6Key Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants (Ministry of Education), School of Life Science, Hainan University, Haikou 570228, ChinaCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, ChinaKey Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants (Ministry of Education), School of Life Science, Hainan University, Haikou 570228, ChinaCollege of Biomedical Information and Engineering, Hainan Medical University, Haikou 571199, ChinaState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, ChinaPublic Health Sciences and Statistics and Center for Statistical Genetics, Pennsylvania State University, Hershey, PA, USAKey Laboratory of Genetics and Germplasm Innovation of Tropical Special Forest Trees and Ornamental Plants (Ministry of Education), School of Life Science, Hainan University, Haikou 570228, China; Corresponding author.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 https://github.com/shang-qian/SCSit.http://www.sciencedirect.com/science/article/pii/S2001037021003524SCSitSingle cell sequencingSPLiT-seqPreprocessing toolCell type identification |
| spellingShingle | Mei-Wei Luan Jia-Lun Lin Ye-Fan Wang Yu-Xiao Liu Chuan-Le Xiao Rongling Wu Shang-Qian Xie SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq Computational and Structural Biotechnology Journal SCSit Single cell sequencing SPLiT-seq Preprocessing tool Cell type identification |
| title | SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq |
| title_full | SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq |
| title_fullStr | SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq |
| title_full_unstemmed | SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq |
| title_short | SCSit: A high-efficiency preprocessing tool for single-cell sequencing data from SPLiT-seq |
| title_sort | scsit a high efficiency preprocessing tool for single cell sequencing data from split seq |
| topic | SCSit Single cell sequencing SPLiT-seq Preprocessing tool Cell type identification |
| url | http://www.sciencedirect.com/science/article/pii/S2001037021003524 |
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