%AHealey, Hope [Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA]%ABassham, Susan [Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA]%ACresko, William [Institute of Ecology and Evolution, University of Oregon, Eugene, OR 97403, USA, Presidential Initiative in Data Science, University of Oregon, Eugene, OR 97403, USA]%ASanchez Alvarado, ed., A.%BJournal Name: Genetics; Journal Volume: 220; Journal Issue: 3; Related Information: CHORUS Timestamp: 2022-03-21 06:09:07 %D2022%IOxford University Press; None %JJournal Name: Genetics; Journal Volume: 220; Journal Issue: 3; Related Information: CHORUS Timestamp: 2022-03-21 06:09:07 %K %MOSTI ID: 10364150 %PMedium: X %TSingle-cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis %XAbstract

Single-cell RNA sequencing is a powerful technique that continues to expand across various biological applications. However, incomplete 3′-UTR annotations can impede single-cell analysis resulting in genes that are partially or completely uncounted. Performing single-cell RNA sequencing with incomplete 3′-UTR annotations can hinder the identification of cell identities and gene expression patterns and lead to erroneous biological inferences. We demonstrate that performing single-cell isoform sequencing in tandem with single-cell RNA sequencing can rapidly improve 3′-UTR annotations. Using threespine stickleback fish (Gasterosteus aculeatus), we show that gene models resulting from a minimal embryonic single-cell isoform sequencing dataset retained 26.1% greater single-cell RNA sequencing reads than gene models from Ensembl alone. Furthermore, pooling our single-cell sequencing isoforms with a previously published adult bulk Iso-Seq dataset from stickleback, and merging the annotation with the Ensembl gene models, resulted in a marginal improvement (+0.8%) over the single-cell isoform sequencing only dataset. In addition, isoforms identified by single-cell isoform sequencing included thousands of new splicing variants. The improved gene models obtained using single-cell isoform sequencing led to successful identification of cell types and increased the reads identified of many genes in our single-cell RNA sequencing stickleback dataset. Our work illuminates single-cell isoform sequencing as a cost-effective and efficient mechanism to rapidly annotate genomes for single-cell RNA sequencing.

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