Comparison of single-cell long-read and short-read transcriptome sequencing via cDNA molecule matching: quality evaluation of the MAS-ISO-seq approach
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09.2025
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01A - Journal article
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NAR Genomics and Bioinformatics
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7
Issue / Number
3
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lqaf089
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Oxford University Press
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Abstract
Single-cell RNA sequencing is used for profiling gene expression differences between cells. It can be performed with short reads, which provide high-throughput and high-quality information at the gene level, or with long reads, which provide isoform resolution via preserving full-length transcripts. It is, however, unclear how comparable the data is between the two methods. We investigate the types of bias introduced at the library preparation and the bioinformatic processing steps on the transcripts recovered from long- and short-read sequencing, and the effects of filtering, enabled by sequencing of full-length transcripts, on gene expression. For each sample, we sequenced the same 10x Genomics 3′ complementary DNA (cDNA) using Illumina short reads and Pacific Biosciences long reads and cross-compared each molecule matched through cell barcode and unique molecular identifier. We find that both methods render highly comparable results and recover a large proportion of cells and transcripts. However, platform-dependent cDNA library processing and data analysis steps introduce biases. Short-read sequencing provided higher sequencing depth, but long-read sequencing allowed for retaining transcripts shorter than 500 bp and for removal of degraded cDNA contaminated by template switching oligos. Filtering of artefacts, identifiable only from full-length transcripts, reduces gene count correlation between the two methods.
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2631-9268
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English
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Yes
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Published
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Gold
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Zajac, N., Zhang, Q., Bratus-Neuenschwander, A., Qi, W., Bolck, H., Karakulak, T., Oltra, T., Moch, H., Kahraman, A., & Rehrauer, H. (2025). Comparison of single-cell long-read and short-read transcriptome sequencing via cDNA molecule matching: quality evaluation of the MAS-ISO-seq approach. NAR Genomics and Bioinformatics, 7(3), lqaf089. https://doi.org/10.1093/nargab/lqaf089