eCite Digital Repository

Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing

Citation

Senabouth, A and Andersen, S and Shi, Q and Shi, L and Jiang, F and Zhang, W and Wing, K and Daniszewski, M and Lukowski, SW and Hung, SSC and Fink, L and Beckhouse, A and Pebay, A and Hewitt, AW and Powell, JE, Comparative performance of the BGI and Illumina sequencing technology for single-cell RNA-sequencing, NAR Genomics and Bioinformatics, 2, (2) pp. 1-9. ISSN 2631-9268 (2020) [Refereed Article]


Preview
PDF
5Mb
  

Copyright Statement

Copyright 2020 The Authors. Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/

DOI: doi:10.1093/nargab/lqaa034

Abstract

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10x Genomics require considerable amounts of sequencing, typically due to the large number of cells. The ability to use these data to address biological questions is directly impacted by the quality of the sequence data. Here we have compared the performance of the Illumina NextSeq 500 and NovaSeq 6000 against the BGI MGISEQ-2000 platform using identical Single Cell 3' libraries consisting of over 70 000 cells generated on the 10x Genomics Chromium platform. Our results demonstrate a highly comparable performance between the NovaSeq 6000 and MGISEQ-2000 in sequencing quality, and the detection of genes, cell barcodes, Unique Molecular Identifiers. The performance of the NextSeq 500 was also similarly comparable to the MGISEQ-2000 based on the same metrics. Data generated by both sequencing platforms yielded similar analytical outcomes for general single-cell analysis. The performance of the NextSeq 500 and MGISEQ-2000 were also comparable for the deconvolution of multiplexed cell pools via variant calling, and detection of guide RNA (gRNA) from a pooled CRISPR single-cell screen. Our study provides a benchmark for high-capacity sequencing platforms applied to high-throughput scRNA-seq libraries.

Item Details

Item Type:Refereed Article
Research Division:Biological Sciences
Research Group:Bioinformatics and computational biology
Research Field:Sequence analysis
Objective Division:Health
Objective Group:Clinical health
Objective Field:Diagnosis of human diseases and conditions
UTAS Author:Wing, K (Mr Kristof Wing)
UTAS Author:Hewitt, AW (Professor Alex Hewitt)
ID Code:152827
Year Published:2020
Web of Science® Times Cited:17
Deposited By:Medicine
Deposited On:2022-08-25
Last Modified:2022-09-13
Downloads:1 View Download Statistics

Repository Staff Only: item control page