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Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT
We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530667/ https://www.ncbi.nlm.nih.gov/pubmed/36181683 http://dx.doi.org/10.1016/j.xpro.2022.101729 |
_version_ | 1784801734549307392 |
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author | Hsu, Che-Wei Shahan, Rachel Nolan, Trevor M. Benfey, Philip N. Ohler, Uwe |
author_facet | Hsu, Che-Wei Shahan, Rachel Nolan, Trevor M. Benfey, Philip N. Ohler, Uwe |
author_sort | Hsu, Che-Wei |
collection | PubMed |
description | We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than the popular tool Cell Ranger. COPILOT then offers non-arbitrary background noise removal by comparing distributions of low-quality and high-quality cells. Together, this protocol streamlines the processing workflow and provides an easy entry for new scRNA-seq users. For complete details on the use and execution of this protocol, please refer to Shahan et al. (2022). |
format | Online Article Text |
id | pubmed-9530667 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-95306672022-10-05 Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT Hsu, Che-Wei Shahan, Rachel Nolan, Trevor M. Benfey, Philip N. Ohler, Uwe STAR Protoc Protocol We describe a protocol to perform fast and non-arbitrary quality control of single-cell RNA sequencing (scRNA-seq) raw data using scKB and COPILOT. scKB is a wrapper script of kallisto and bustools for accelerated alignment and transcript count matrix generation, which runs significantly faster than the popular tool Cell Ranger. COPILOT then offers non-arbitrary background noise removal by comparing distributions of low-quality and high-quality cells. Together, this protocol streamlines the processing workflow and provides an easy entry for new scRNA-seq users. For complete details on the use and execution of this protocol, please refer to Shahan et al. (2022). Elsevier 2022-09-30 /pmc/articles/PMC9530667/ /pubmed/36181683 http://dx.doi.org/10.1016/j.xpro.2022.101729 Text en © 2022. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Protocol Hsu, Che-Wei Shahan, Rachel Nolan, Trevor M. Benfey, Philip N. Ohler, Uwe Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT |
title | Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT |
title_full | Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT |
title_fullStr | Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT |
title_full_unstemmed | Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT |
title_short | Protocol for fast scRNA-seq raw data processing using scKB and non-arbitrary quality control with COPILOT |
title_sort | protocol for fast scrna-seq raw data processing using sckb and non-arbitrary quality control with copilot |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530667/ https://www.ncbi.nlm.nih.gov/pubmed/36181683 http://dx.doi.org/10.1016/j.xpro.2022.101729 |
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