{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Kowalski MH"],"funding":["NHGRI NIH HHS","NCI NIH HHS","NINDS NIH HHS","NIH HHS"],"pubmed_abstract":["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 "],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2023.02.09.527751"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9934614"],"repository":["biostudies-literature"],"pubmed_title":["CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators."],"pmcid":["PMC9934614"],"funding_grant_id":["U24 HG007234","RM1 HG011014","F30 CA287962","UG3 NS132139","T32 HG000044","OT2 OD033760"],"pubmed_authors":["Kowalski MH","Satija R","Choudhary S","Linder J","Hartman A","Wessels HH","Hao Y","Mascio I","Dalgarno C","Kundaje A"],"additional_accession":[]},"is_claimable":false,"name":"CPA-Perturb-seq: Multiplexed single-cell characterization of alternative polyadenylation regulators.","description":"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 ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-04-28T03:15:59.727Z","creation":"2026-04-28T03:08:24.383Z"},"accession":"S-EPMC9934614","cross_references":{"pubmed":["36798324"],"doi":["10.1101/2023.02.09.527751"]}}