Published May 30, 2022
| Version https://github.com/ohickl/binny_manuscript
Journal article
Open
binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets
Authors/Creators
- 1. Bioinformatics Core, Luxembourg Centre for Systems Biomedicine, University of Luxembourg
- 2. Systems Ecology, Luxembourg Centre for Systems Biomedicine, University of Luxembourg
- 3. Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam
Description
Repository for the publication 'binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets'. Contains relevant data used in benchmarking binny on synthetic (CAMI challenges 1 and 2)1,2 and real-world (105 of the metagenomes from the MetaBAT2 publication)3 data sets against other binning methods. For the synthetic benchmarks AMBER4 outputs are provided. For each sample used in the benchmarks, the average contig depth files provided to binning methods (except VAMB5) are provided.
The repository with scripts used in the publication can be found here: https://github.com/ohickl/binny_manuscript.
The latest binny release can be found at https://github.com/a-h-b/binny.
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all_avg_contig_depths.zip
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Additional details
References
- Sczyrba, A., Hofmann, P., Belmann, P. et al. Critical Assessment of Metagenome Interpretation—a benchmark of metagenomics software. Nat Methods 14, 1063–1071 (2017), https://doi.org/10.1038/nmeth.4458
- Meyer, F., Fritz, A. et al. Critical Assessment of Metagenome Interpretation - the second round of challenges. bioRxiv, 2021.07.12.451567 (2021), https://doi.org/10.1101/2021.07.12.451567
- Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ, 7:e7359 (2019), https://doi.org/10.7717/peerj.7359
- Meyer, F. et al. AMBER: Assessment of Metagenome BinnERs. GigaScience, giy069 (2018), doi:10.1093/gigascience/giy069
- Nissen, J.N., Johansen, J., Allesøe, R.L. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol 39, 555–560 (2021), https://doi.org/10.1038/s41587-020-00777-4