{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lin YY"],"funding":["BMBF","Joachim Herz Foundation","German Center for Cardiovascular Research","Wilhelm Sander-Stiftung","Horizon Europe","Cardio-Pulmonary Institute","KU Leuven","German Cancer Aid","Horizon 2020","German Network for Bioinformatics Infrastructure"],"pagination":["gkaf970"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12539629"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["53(19)"],"pubmed_abstract":["DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five whole-genome profiling protocols. As an evaluation reference, we employed accurate locus-specific measurements from our previous be"],"journal":["Nucleic acids research"],"pubmed_title":["Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold standard."],"pmcid":["PMC12539629"],"funding_grant_id":["70113869","81Z0200101","01KD2207A","101058427","390649896","STG/22/024","031L0162B","2022.010.1","031L0101A","824110"],"pubmed_authors":["Plass C","Nordstroem K","Lipka DB","Lin YY","Kensche PR","Schonung M","Giacomelli G","Lafrenz P","Iskar M","Esteller M","Zapatka M","Weichenhan D","Lawerenz C","Schulz MH","Schlesner M","Merkel A","Bock C","Gerhauser C","Chepeleva M","Wolf S","Hovestadt V","Gonzalez-Avalos E","Hartmann M","Assenov Y","Kramer S","Mucke O","Toth R","Walter J","Sarnataro A","Lutsik P","Twardziok S","Breuer K","Buchhalter I","Thommen F","Kretzmer H","Fischer J","Hoffmann S","Wilk A","Petermann F","Weiser L"],"additional_accession":[]},"is_claimable":false,"name":"Pipeline Olympics: continuable benchmarking of computational workflows for DNA methylation sequencing data against an experimental gold standard.","description":"DNA methylation is a widely studied epigenetic mark and a powerful biomarker of cell type, age, environmental exposures, and disease. Whole-genome sequencing following selective conversion of unmethylated cytosines into thymines via bisulfite treatment or enzymatic methods remains the reference method for DNA methylation profiling genome-wide. While numerous software tools facilitate processing of DNA methylation sequencing reads, a comprehensive benchmarking study has been lacking. In this study, we systematically compared complete computational workflows for processing DNA methylation sequencing data using a dedicated benchmarking dataset generated with five whole-genome profiling protocols. As an evaluation reference, we employed accurate locus-specific measurements from our previous be","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-05-10T03:23:35.345Z","creation":"2026-05-10T03:12:29.433Z"},"accession":"S-EPMC12539629","cross_references":{"pubmed":["41118575"],"doi":["10.1093/nar/gkaf970"]}}