{"doi":"10.1101/2020.01.24.916015","title":"Rapid and scalable profiling of nascent RNA with fastGRO","abstract":"Abstract Genome-wide profiling of nascent RNA has become a fundamental tool to study transcription regulation. Over the past decade, next-generation sequencing has fostered development of a handful of techniques (i.e. GRO-seq, PRO-seq, TT-seq and NET-seq) that map unprocessed transcripts originating from both the coding and the noncoding portion of the genome. Unlike steady-state RNA sequencing, nascent RNA profiling mirrors the real-time activity of RNA Polymerases and provides an accurate readout of transcriptome-wide variations that occur during short time frames (i.e. response to external stimuli or rapid metabolic changes). Some species of nuclear RNAs, albeit functional, have a short half-life and can only be accurately gauged by nascent RNA techniques (i.e. lincRNAs and eRNAs). Furthermore, these techniques capture uncapped post-cleavage RNA at termination sites or promoter-associated antisense RNAs, providing a unique insight into RNAPII dynamics and processivity. Here we present a run-on assay with 4s-UTP labelling, followed by reversible biotinylation and affinity purification via streptavidin. Our protocol allows streamlined sample preparation within less than 3 days. We named the technique fastGRO (fast Global Run-On). We show that fastGRO is highly reproducible and yields a more complete and extensive coverage of nascent RNA than comparable techniques. Importantly, we demonstrate that fastGRO is scalable and can be performed with as few as 0.5×10^6 cells.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":120844,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9418,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":477634,"name":"Connor Hill","orcid":"0000-0003-4114-4513","position":1,"is_corresponding":false},{"id":479388,"name":"Mathieu Quesnel-Vallières","orcid":"0000-0001-9945-8123","position":2,"is_corresponding":false},{"id":281155,"name":"Yoseph Barash","orcid":"0000-0003-3005-5048","position":3,"is_corresponding":false},{"id":479389,"name":"Alessandro Gardini","orcid":"0000-0003-0413-2855","position":4,"is_corresponding":false},{"id":479387,"name":"Elisa Barbieri","orcid":"0009-0000-4050-6119","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-18T23:14:38.147936Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}