{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,4]],"date-time":"2025-01-04T05:27:10Z","timestamp":1735968430581,"version":"3.32.0"},"reference-count":20,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Quant. Biol."],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Single\u2010cell RNA sequencing (scRNA\u2010seq) technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell\u2010type composition as well as cell\u2010state heterogeneity for specific biological processes. Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples. Therefore, benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA\u2010seq data. However, recent comparing efforts were constrained in sequencing platforms or analyzing pipelines. There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity, precision, and so on.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We downloaded public scRNA\u2010seq data that was generated by two distinct sequencers, NovaSeq 6000 and MGISEQ 2000. Then data was processed through the Drop\u2010seq\u2010tools, UMI\u2010tools and Cell Ranger pipeline respectively. We calculated multiple measurements based on the expression profiles of the six platform\u2010pipeline combinations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We found that all three pipelines had comparable performance, the Cell Ranger pipeline achieved the best performance in precision while UMI\u2010tools prevailed in terms of sensitivity and marker calling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our work provided an insight into the selection of scRNA\u2010seq data processing tools for two sequencing platforms as well as a framework to evaluate platform\u2010pipeline combinations.<\/jats:p><\/jats:sec>","DOI":"10.15302\/j-qb-022-0295","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T08:29:26Z","timestamp":1679473766000},"page":"333-340","source":"Crossref","is-referenced-by-count":2,"title":["Comparative analysis of NovaSeq 6000 and MGISEQ 2000 single\u2010cell RNA sequencing data"],"prefix":"10.1002","volume":"10","author":[{"given":"Weiran","family":"Chen","sequence":"first","affiliation":[{"name":"<!--1--> Bio\u2010Med Big Data Center Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Shanghai 200031 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md","family":"Wahiduzzaman","sequence":"additional","affiliation":[{"name":"<!--1--> Bio\u2010Med Big Data Center Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Shanghai 200031 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Li","sequence":"additional","affiliation":[{"name":"<!--1--> Bio\u2010Med Big Data Center Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Shanghai 200031 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixue","family":"Li","sequence":"additional","affiliation":[{"name":"<!--1--> Bio\u2010Med Big Data Center Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Shanghai 200031 China"},{"name":"<!--2--> School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyong","family":"Zheng","sequence":"additional","affiliation":[{"name":"<!--1--> Bio\u2010Med Big Data Center Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health Chinese Academy of Sciences Shanghai 200031 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"<!--1--> Bio\u2010Med Big Data Center Key Laboratory of Computational Biology Shanghai Institute of 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