biostudies-literatureUnknownKowalski MHNHGRI NIH HHSNCI NIH HHSNINDS NIH HHSNIH HHSMost mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity that is governed by the cleavage and polyadenylation (CPA) regulatory machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 known CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a statistical framework to specifically identify perturbation-dependent changes in intronic and tandem polyadenylation, and discover modules of co-regulated polyA sites exhibiting distinct functional properties. By training a multi-task deep neural network (APARENT-Perturb) on our dataset, we delineate a <i>cis</i>-regulatory code that predicts bioRxiv : the preprint server for biology2023.02.09.527751https://www.ebi.ac.uk/biostudies/studies/S-EPMC9934614biostudies-literatureCPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators.PMC9934614U24 HG007234RM1 HG011014F30 CA287962UG3 NS132139T32 HG000044OT2 OD033760Kowalski MHSatija RChoudhary SLinder JHartman AWessels HHHao YMascio IDalgarno CKundaje AfalseCPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators.Most mammalian genes have multiple polyA sites, representing a substantial source of transcript diversity that is governed by the cleavage and polyadenylation (CPA) regulatory machinery. To better understand how these proteins govern polyA site choice we introduce CPA-Perturb-seq, a multiplexed perturbation screen dataset of 42 known CPA regulators with a 3' scRNA-seq readout that enables transcriptome-wide inference of polyA site usage. We develop a statistical framework to specifically identify perturbation-dependent changes in intronic and tandem polyadenylation, and discover modules of co-regulated polyA sites exhibiting distinct functional properties. By training a multi-task deep neural network (APARENT-Perturb) on our dataset, we delineate a <i>cis</i>-regulatory code that predicts 2023-01-01T00:00:00Z2023 Feb2026-04-28T03:15:59.727Z2026-04-28T03:08:24.383ZS-EPMC99346143679832410.1101/2023.02.09.527751