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SCAPTURE: a deep learning-embedded pipeline that captures polyadenylation information from 3' tag-based RNA-seq of single cells

By Guo-Wei Li, Fang Nan, Guo-Hua Yuan, Bin Tian, Li Yang

Posted 19 Mar 2021
bioRxiv DOI: 10.1101/2021.03.17.435782

Single-cell RNA-seq (scRNA-seq) profiles gene expression with a resolution that empowers depiction of cell atlas in complex systems. Here, we developed a stepwise computational pipeline SCAPTURE to identify, evaluate, and quantify cleavage and polyadenylation sites (PASs) from 3' tag-based scRNA-seq. SCAPTURE detects PASs de novo in single cells with high sensitivity and accuracy, enabling detection of previously unannotated PASs. Quantified alternative PAS transcripts refine cell identities, enriching information extracted from scRNA-seq.

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