{"doi":"10.1101/2024.02.27.582305","title":"FLYNC: A Machine Learning-Driven Framework for Discovering Long Non-Coding RNAs in <i>Drosophila melanogaster</i>","abstract":"ABSTRACT Non-coding RNAs have increasingly recognized roles in critical molecular mechanisms of disease. However, the non-coding genome of Drosophila melanogaster , one of the most powerful disease model organisms, has been understudied. Here, we present FLYNC – FLY Non-Coding discovery and classification – a novel machine learning-based model that predicts the probability of a newly identified RNA transcript being a long non-coding RNA (lncRNA). Integrated into an end-to-end bioinformatics pipeline capable of processing single-cell or bulk RNA sequencing data, FLYNC outputs potential new non-coding RNA genes. FLYNC leverages large-scale genomic and transcriptomic datasets to identify patterns and features that distinguish non-coding genes from protein-coding genes, thereby facilitating lncRNA prediction. We demonstrate the application of FLYNC to publicly available Drosophila adult head bulk transcriptome and single-cell transcriptomic data from Drosophila neural stem cell lineages and identify several novel tissue- and cell-specific lncRNAs. We have further experimentally validated the existence of a set of FLYNC positive hits by qPCR. Overall, our findings demonstrate that FLYNC serves as a robust tool for identifying lncRNAs in Drosophila melanogaster , transcending current limitations in ncRNA identification and harnessing the potential of machine learning.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":494858,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9487,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1341421,"name":"Tiago Baptista","orcid":"0000-0002-6557-369X","position":1,"is_corresponding":false},{"id":1115321,"name":"Graça S. Marques","orcid":"0000-0002-9431-3539","position":2,"is_corresponding":false},{"id":579252,"name":"Catarina C. F. Homem","orcid":"0000-0003-4243-0298","position":3,"is_corresponding":false},{"id":1341420,"name":"Ricardo F. dos Santos","orcid":"0000-0002-0244-2596","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-19T02:09:15.528853Z","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":[]}