Mineeva, Olga Rojas-Carulla, Mateo Ley, Ruth E Schölkopf, Bernhard Youngblut, Nicholas D 2020 <jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>Methodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates a 1% contig misassembly rate in two recent large-scale metagenome assembly publications.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>DeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modeling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED.</jats:p> </jats:sec> <jats:sec> <jats:title>Supplementary information</jats:title> <jats:p>Supplementary data are available at Bioinformatics online.</jats:p> </jats:sec> https://sonar.ch/global/documents/213370 eng info:eu-repo/semantics/altIdentifier/doi/10.1093/bioinformatics/btaa124 info:eu-repo/semantics/altIdentifier/issn/1367-4803 Bioinformatics. - Oxford University Press (OUP). - 2020, vol. 36, no. 10, p. 3011-3017 Statistics and Probability Computational Theory and Mathematics Biochemistry Molecular Biology Computational Mathematics Computer Science Applications DeepMAsED: evaluating the quality of metagenomic assemblies http://purl.org/coar/resource_type/c_6501