S

    Citations

    If you use aviary then please be aware that you are using a great number of other programs and aviary wrapping around them. You should cite all of these tools as well, or whichever tools you know that you are using. To make this easy for you we have provided the following list of citations for you to use in alphabetical order. This list will be updated as new modules are added to aviary.

    QC

    • NanoPack: De Coster, W., D’Hert, S., Schultz, D. T., Cruts, M. & Van Broeckhoven, C. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics 34, 2666–2669 (2018). https://doi.org/10.1093/bioinformatics/bty149

    Assembly

    • Flye: Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nature Biotechnology 37, 540–546 (2019). https://doi.org/10.1038/s41587-019-0072-8
    • Circlator: Hunt, M. et al. Circlator: automated circularization of genome assemblies using long sequencing reads. Genome Biology 16, 294 (2015). https://doi.org/10.1186/s13059-015-0849-0
    • Racon: Vaser, R., Sović, I., Nagarajan, N. & Šikić, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res 27, 737–746 (2017). https://doi.org/10.1101/gr.214270.116
    • Pilon: Walker, B. J. et al. Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement. PLOS ONE 9, e112963 (2014). https://doi.org/10.1371/journal.pone.0112963
    • metaSPAdes: Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017). metaSPAdes: a new versatile metagenomic assembler. Genome research, 27(5), 824-834. https://doi.org/10.1101/gr.213959.116
    • Unicycler: Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: Resolving bacterial genome assemblies from short and long sequencing reads. PLOS Computational Biology 13, e1005595 (2017). https://doi.org/10.1371/journal.pcbi.1005595
    • MEGAHIT: Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015). https://doi.org/10.1093/bioinformatics/btv033

    Read mapping

    • Minimap2: Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018). https://doi.org/10.1093/bioinformatics/bty191
    • samtools: Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). https://doi.org/10.1093/bioinformatics/btp352

    Binning

    • CONCOCT: Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat Methods 11, 1144–1146 (2014). https://doi.org/10.1038/nmeth.3103
    • VAMB: Nissen, J. N. et al. Improved metagenome binning and assembly using deep variational autoencoders. Nature Biotechnology 1–6 (2021) doi:10.1038/s41587-020-00777-4. https://doi.org/10.1038/s41587-020-00777-4
    • MetaBAT: Kang, D. D., Froula, J., Egan, R. & Wang, Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ 3, e1165 (2015). https://doi.org/10.7717/peerj.1165
    • MetaBAT2: Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, (2019). https://doi.org/10.7717/peerj.7359
    • DAS Tool: Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nature Microbiology 3, 836–843 (2018). https://doi.org/10.1038/s41564-018-0171-1
    • SemiBin2: Pan, S., Zhao, X. M., & Coelho, L. P. (2023). SemiBin2: self-supervised contrastive learning leads to better MAGs for short-and long-read sequencing, Bioinformatics, Volume 39, Issue Supplement_1, June 2023, Pages i21–i29. https://doi.org/10.1093/bioinformatics/btad209
    • MaxBin 2.0: Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016). https://doi.org/10.1093/bioinformatics/btv638

    Annotation

    • CheckM2: Chklovski, A., Parks, D. H., Woodcroft, B. J., & Tyson, G. W. (2023). CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. Nature Methods, 1-10. https://doi.org/10.1038/s41592-023-01940-w
    • CheckM: Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015). https://doi.org/10.1101/gr.186072.114
    • eggNOG mapper 2: Cantalapiedra, C. P., Hernández-Plaza, A., Letunic, I., Bork, P., & Huerta-Cepas, J. (2021). eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Molecular biology and evolution, 38(12), 5825-5829. https://doi.org/10.1093/molbev/msab293
    • GTDB-Tk 2: Chaumeil, P. A., Mussig, A. J., Hugenholtz, P., & Parks, D. H. (2022). GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics, 38(23), 5315-5316. https://doi.org/10.1093/bioinformatics/btac672
    • GraftM: Boyd, J. A., Woodcroft, B. J. & Tyson, G. W. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Research 46, e59 (2018). https://doi.org/10.1093/nar/gky174

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