2018
DOI: 10.1038/nbt.4259
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Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells

Abstract: Full-length RNA sequencing (RNA-Seq) has been applied to bulk tissue, cell lines and sorted cells to characterize transcriptomes 1-11 , but applying this technology to single cells has proven to be difficult, with less than ten single-cell transcriptomes having been analyzed thus far 12,13. Although single splicing events have been described for ≤200 single cells with statistical confidence 14,15 , full-length mRNA analyses for hundreds of cells have not been reported. Singlecell short-read 3′ sequencing enabl… Show more

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Cited by 283 publications
(301 citation statements)
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(48 reference statements)
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“…Even in the domain of single-cell RNA-seq, which is currently thriving on short singlereads for molecule counting, long-read formats are beginning to be applied, aiming to capture the richness of isoform variation and regulation on a per-cell and per-cell-type basis 47 . That said, short-read transcriptomes will surely continue to play a prominent role for short RNA class substrates, for intractably degraded RNAs, and, increasingly, in biological settings where a few long-read transcriptomes can provide a reference against which larger numbers of companion short-read samples can be quantified.…”
Section: Discussionmentioning
confidence: 99%
“…Even in the domain of single-cell RNA-seq, which is currently thriving on short singlereads for molecule counting, long-read formats are beginning to be applied, aiming to capture the richness of isoform variation and regulation on a per-cell and per-cell-type basis 47 . That said, short-read transcriptomes will surely continue to play a prominent role for short RNA class substrates, for intractably degraded RNAs, and, increasingly, in biological settings where a few long-read transcriptomes can provide a reference against which larger numbers of companion short-read samples can be quantified.…”
Section: Discussionmentioning
confidence: 99%
“…Despite the benefits, the combination of UMIs with long-read sequencing is relatively unexplored, and only recently has it been applied with PacBio sequencing, but without profiling the error of the generated consensus sequences 23,24 . For ONT sequencing the raw error rate of 5-25% 25 has, until now, made it difficult to efficiently extract UMI sequences and confidently determine the true UMI sequences necessary for read binning.…”
Section: Introductionmentioning
confidence: 99%
“…While there have been many studies using long read RNA sequencing for transcriptome discovery 5,6,7,8,9 , the tools used for processing long read data suffer from limitations that severely reduce the sensitivity and specificity of transcriptome exploration. These strategies either rely on orthogonal information which biases gene discovery and are only available for a small number of species 10 (Talon 11 , TAPIS 6 , SQANTI 12 ) or on algorithms with serious theoretical limitations 13,14 .…”
Section: Introductionmentioning
confidence: 99%