Open Access

Yu-Yu Lin, Kersten Breuer, Dieter Weichenhan, Pascal Lafrenz, Antonella Sarnataro, Agata Wilk, Maryna Chepeleva, Oliver Mücke, Maximilian Schönung, Franziska Petermann, Philip Reiner Kensche, Lena Weiser, Frank Thommen, Gideon Giacomelli, Karl Nordstroem, Edahi Gonzalez-Avalos, Angelika Merkel, Helene Kretzmer, Jonas Fischer, Stephen Krämer, Murat Iskar, Stephan Wolf, Ivo Buchhalter, Manel Esteller, Christian Lawerenz, Sven Twardziok, Marc Zapatka, Volker Hovestadt, Matthias Schlesner, Marcel H. Schulz, Steve Hoffmann, Clarissa Gerhauser, Jörn Walter, Mark Hartmann, Daniel B. Lipka, Yassen Assenov, Christoph Bock, Christoph Plass, Reka Toth, Pavlo Lutsik

• 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 benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and multiple performance metrics, we identified workflows that consistently demonstrated superiorDNA 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 benchmark of targeted DNA methylation assays. Based on this experimental gold-standard assessment and multiple performance metrics, we identified workflows that consistently demonstrated superior performance and revealed major workflow development trends. To ensure the long-term utility of our benchmark, we implemented an interactive workflow execution and data presentation platform, adaptable to user-defined criteria and readily expandable to future software.…