{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Goni E"],"funding":["\"la Caixa\" Foundation (Caixa Foundation)","EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions)","Worldwide Cancer Research","European Research Council","Departamento de Educación, Gobierno de Navarra (Department of Education, Government of Navarra)"],"pagination":["9709"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11550465"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Long non-coding RNAs (lncRNAs) play fundamental roles in cellular processes and pathologies, regulating gene expression at multiple levels. Despite being highly cell type-specific, their study at single-cell (sc) level is challenging due to their less accurate annotation and low expression compared to protein-coding genes. Here, we systematically benchmark different preprocessing methods and develop a computational framework, named ELATUS, based on the combination of the pseudoaligner Kallisto with selective functional filtering. ELATUS enhances the detection of functional lncRNAs from scRNA-seq data, detecting their expression with higher concordance than standard methods with the ATAC-seq profiles in single-cell multiome data. Interestingly, the better results of ELATUS are due to its ad"],"journal":["Nature communications"],"pubmed_title":["Uncovering functional lncRNAs by scRNA-seq with ELATUS."],"pmcid":["PMC11550465"],"funding_grant_id":["898356","LCF/PR/HR21/00176","20-0204","0011-0537-2020-000038","771425"],"pubmed_authors":["Hernaez M","Santisteban M","Gonzalez J","Abad A","Fortes P","Huarte M","Mas AM","Goni E"],"additional_accession":[]},"is_claimable":false,"name":"Uncovering functional lncRNAs by scRNA-seq with ELATUS.","description":"Long non-coding RNAs (lncRNAs) play fundamental roles in cellular processes and pathologies, regulating gene expression at multiple levels. Despite being highly cell type-specific, their study at single-cell (sc) level is challenging due to their less accurate annotation and low expression compared to protein-coding genes. Here, we systematically benchmark different preprocessing methods and develop a computational framework, named ELATUS, based on the combination of the pseudoaligner Kallisto with selective functional filtering. ELATUS enhances the detection of functional lncRNAs from scRNA-seq data, detecting their expression with higher concordance than standard methods with the ATAC-seq profiles in single-cell multiome data. Interestingly, the better results of ELATUS are due to its ad","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-03T03:17:37.254Z","creation":"2025-04-06T22:42:42.419Z"},"accession":"S-EPMC11550465","cross_references":{"pubmed":["39521797"],"doi":["10.1038/s41467-024-54005-7"]}}