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

Abstract

The libraries generated by high-throughput single cell RNA-sequencing (scRNA-seq) platforms such as the Chromium from 10× 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 10× 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.

The establishment of variant surface glycoprotein monoallelic expression revealed by single-cell RNA-seq of Trypanosoma brucei in the tsetse fly salivary glands

“…Single cell barcoding libraries were sequenced on the Illumina NextSeq platform at the Institut Pasteur using a Mid-output 150 cycle cartridge. Sequencing of single cell libraries at PLOS PATHOGENS BGI was performed on the MGIseq-2000 following conversion to DNBseq libraries [74]. Details of sequencing outputs including read lengths and number of reads generated can be found in S5 Table.…”

Single-cell transcriptomics of alloreactive CD4+ T cells over time reveals divergent fates during gut graft-versus-host disease

“…scRNA-Seq data processing FASTQ files were processed using "cellranger count" pipeline from Cell Ranger version 2.1.0 and 3.0.2 (10× Genomics) with 10× mouse genome 1.2.0 release as a reference. For BGI FASTQ files (mGVHD3), it was made compatible with Cell Ranger by reformatting file names and FASTQ headers using code from https://github.com/IMB-Computational-Genomics-Lab/BGIvsIllumina_scRNASeq (branch: master; commit ID: 89092f6) (49).…”

Relative matching using low coverage sequencing

“…We subjected the samples to DNA sequencing of paired-end 150bp reads using the Beijing Genome Institute (BGI) CompleteGenomics T10 DNBseq technology. Previous studies showed that the accuracy of this technology is similar to Illumina sequencing (Kim et al 2020; Senabouth et al 2020). We computationally downsampled the sequence reads to obtain 1×LCS by randomly selecting 10 7 paired-end sequence reads from the FASTQ file of each sample.…”