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A Single-Molecule Long-Read Survey of Human Transcriptomes using LoopSeq Synthetic Long Read Sequencing

Indira Wu, Hee Shin Kim, Tuval Ben-Yehezkel
doi: https://doi.org/10.1101/532135
Indira Wu
1Loop Genomics, San Jose, CA, 95138
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Hee Shin Kim
1Loop Genomics, San Jose, CA, 95138
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Tuval Ben-Yehezkel
1Loop Genomics, San Jose, CA, 95138
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  • For correspondence: tuval@loopgenomics.com
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Abstract

State-of-the-art short-read transcriptome sequencing methods employ unique molecular identifier (UMI) to accurately classify and count mRNA transcripts. A fundamental limitation of UMI-based short-read transcriptome sequencing is that each read typically covers a small fraction of the transcript sequence. Efforts to accurately characterize splicing isoforms, arguably the largest source of variation in Human gene expression, using short read sequencing have therefore largely relied on computational predictions of transcript isoforms based on indirect observations. Here we describe a transcript counting, synthetic long read method for sequencing whole transcriptomes using short read sequencing platforms and no additional hardware. The method enables full-length mRNA sequence reconstruction at single-nucleotide resolutions with high-throughput, low error rates and UMI based transcript counting using any Illumina sequencer. We describe results from whole transcriptome sequencing from total RNA extracted from 3 human tissue samples: brain, liver, and blood. Reconstructed transcript sequences are characterized and annotated using SQANTI, an analysis pipeline for assessing the sequence quality of long-read transcriptomes. Our results demonstrate that LoopSeq synthetic long-read sequencing can reconstruct contigs up to 3,900nt full-length transcripts using tissue extracted RNA, as well as identify novel splice variants of known junction donors and acceptors.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 12, 2019.
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A Single-Molecule Long-Read Survey of Human Transcriptomes using LoopSeq Synthetic Long Read Sequencing
Indira Wu, Hee Shin Kim, Tuval Ben-Yehezkel
bioRxiv 532135; doi: https://doi.org/10.1101/532135
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A Single-Molecule Long-Read Survey of Human Transcriptomes using LoopSeq Synthetic Long Read Sequencing
Indira Wu, Hee Shin Kim, Tuval Ben-Yehezkel
bioRxiv 532135; doi: https://doi.org/10.1101/532135

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