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Isoform-level quantification for single-cell RNA sequencing
MOTIVATION: RNA expression at isoform level is biologically more informative than at gene level and can potentially reveal cellular subsets and corresponding biomarkers that are not visible at gene level. However, due to the strong 3ʹ bias sequencing protocol, mRNA quantification for high-throughput...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826380/ https://www.ncbi.nlm.nih.gov/pubmed/34864849 http://dx.doi.org/10.1093/bioinformatics/btab807 |
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author | Pan, Lu Dinh, Huy Q Pawitan, Yudi Vu, Trung Nghia |
author_facet | Pan, Lu Dinh, Huy Q Pawitan, Yudi Vu, Trung Nghia |
author_sort | Pan, Lu |
collection | PubMed |
description | MOTIVATION: RNA expression at isoform level is biologically more informative than at gene level and can potentially reveal cellular subsets and corresponding biomarkers that are not visible at gene level. However, due to the strong 3ʹ bias sequencing protocol, mRNA quantification for high-throughput single-cell RNA sequencing such as Chromium Single Cell 3ʹ 10× Genomics is currently performed at the gene level. RESULTS: We have developed an isoform-level quantification method for high-throughput single-cell RNA sequencing by exploiting the concepts of transcription clusters and isoform paralogs. The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression. The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods. AVAILABILITY AND IMPLEMENTATION: Implementation of Scasa including source code, documentation, tutorials and test data supporting this study is available at Github: https://github.com/eudoraleer/scasa and Zenodo: https://doi.org/10.5281/zenodo.5712503. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-8826380 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-88263802022-02-09 Isoform-level quantification for single-cell RNA sequencing Pan, Lu Dinh, Huy Q Pawitan, Yudi Vu, Trung Nghia Bioinformatics Original Papers MOTIVATION: RNA expression at isoform level is biologically more informative than at gene level and can potentially reveal cellular subsets and corresponding biomarkers that are not visible at gene level. However, due to the strong 3ʹ bias sequencing protocol, mRNA quantification for high-throughput single-cell RNA sequencing such as Chromium Single Cell 3ʹ 10× Genomics is currently performed at the gene level. RESULTS: We have developed an isoform-level quantification method for high-throughput single-cell RNA sequencing by exploiting the concepts of transcription clusters and isoform paralogs. The method, called Scasa, compares well in simulations against competing approaches including Alevin, Cellranger, Kallisto, Salmon, Terminus and STARsolo at both isoform- and gene-level expression. The reanalysis of a CITE-Seq dataset with isoform-based Scasa reveals a subgroup of CD14 monocytes missed by gene-based methods. AVAILABILITY AND IMPLEMENTATION: Implementation of Scasa including source code, documentation, tutorials and test data supporting this study is available at Github: https://github.com/eudoraleer/scasa and Zenodo: https://doi.org/10.5281/zenodo.5712503. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-12-02 /pmc/articles/PMC8826380/ /pubmed/34864849 http://dx.doi.org/10.1093/bioinformatics/btab807 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Pan, Lu Dinh, Huy Q Pawitan, Yudi Vu, Trung Nghia Isoform-level quantification for single-cell RNA sequencing |
title | Isoform-level quantification for single-cell RNA sequencing |
title_full | Isoform-level quantification for single-cell RNA sequencing |
title_fullStr | Isoform-level quantification for single-cell RNA sequencing |
title_full_unstemmed | Isoform-level quantification for single-cell RNA sequencing |
title_short | Isoform-level quantification for single-cell RNA sequencing |
title_sort | isoform-level quantification for single-cell rna sequencing |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8826380/ https://www.ncbi.nlm.nih.gov/pubmed/34864849 http://dx.doi.org/10.1093/bioinformatics/btab807 |
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