DeepMAsED: evaluating the quality of metagenomic assemblies

Mineeva, Olga; Rojas-Carulla, Mateo; Ley, Ruth E; Schölkopf, Bernhard; Youngblut, Nicholas D

doi:10.1093/bioinformatics/btaa124

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Journal article

DeepMAsED: evaluating the quality of metagenomic assemblies

Mineeva, Olga Department of Computer Science, ETH Zürich, Zürich 8092, Switzerland
Rojas-Carulla, Mateo Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany
Ley, Ruth E Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen 72076, Germany
Schölkopf, Bernhard Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany
Youngblut, Nicholas D ORCID Department of Microbiome Science, Max Planck Institute for Developmental Biology, Tübingen 72076, Germany

2020-2-25

Published in:

Bioinformatics. - Oxford University Press (OUP). - 2020, vol. 36, no. 10, p. 3011-3017

Computational Theory and Mathematics

English Abstract

Motivation
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 Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large-scale metagenome assemblies.

Results
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.

Conclusions
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.

Availability and implementation
DeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED.

Supplementary information
Supplementary data are available at Bioinformatics online.

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