Phables Citation

Phables is published in Bioinformatics at DOI: 10.1093/bioinformatics/btad586.

If you use Phables in your work, please cite Phables as,

Vijini Mallawaarachchi, Michael J Roach, Przemyslaw Decewicz, Bhavya Papudeshi, Sarah K Giles, Susanna R Grigson, George Bouras, Ryan D Hesse, Laura K Inglis, Abbey L K Hutton, Elizabeth A Dinsdale, Robert A Edwards, Phables: from fragmented assemblies to high-quality bacteriophage genomes, Bioinformatics, Volume 39, Issue 10, October 2023, btad586, https://doi.org/10.1093/bioinformatics/btad586

@article{10.1093/bioinformatics/btad586,
    author = {Mallawaarachchi, Vijini and Roach, Michael J and Decewicz, Przemyslaw and Papudeshi, Bhavya and Giles, Sarah K and Grigson, Susanna R and Bouras, George and Hesse, Ryan D and Inglis, Laura K and Hutton, Abbey L K and Dinsdale, Elizabeth A and Edwards, Robert A},
    title = "{Phables: from fragmented assemblies to high-quality bacteriophage genomes}",
    journal = {Bioinformatics},
    volume = {39},
    number = {10},
    pages = {btad586},
    year = {2023},
    month = {09},
    abstract = "{Microbial communities have a profound impact on both human health and various environments. Viruses infecting bacteria, known as bacteriophages or phages, play a key role in modulating bacterial communities within environments. High-quality phage genome sequences are essential for advancing our understanding of phage biology, enabling comparative genomics studies and developing phage-based diagnostic tools. Most available viral identification tools consider individual sequences to determine whether they are of viral origin. As a result of challenges in viral assembly, fragmentation of genomes can occur, and existing tools may recover incomplete genome fragments. Therefore, the identification and characterization of novel phage genomes remain a challenge, leading to the need of improved approaches for phage genome recovery.We introduce Phables, a new computational method to resolve phage genomes from fragmented viral metagenome assemblies. Phables identifies phage-like components in the assembly graph, models each component as a flow network, and uses graph algorithms and flow decomposition techniques to identify genomic paths. Experimental results of viral metagenomic samples obtained from different environments show that Phables recovers on average over 49\\% more high-quality phage genomes compared to existing viral identification tools. Furthermore, Phables can resolve variant phage genomes with over 99\\% average nucleotide identity, a distinction that existing tools are unable to make.Phables is available on GitHub at https://github.com/Vini2/phables.}",
    issn = {1367-4811},
    doi = {10.1093/bioinformatics/btad586},
    url = {https://doi.org/10.1093/bioinformatics/btad586},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/doi/10.1093/bioinformatics/btad586/51972145/btad586.pdf},
}

Also, please cite the following tools/databases used by Phables.

  • Roach MJ, Pierce-Ward NT, Suchecki R, Mallawaarachchi V, Papudeshi B, et al. Ten simple rules and a template for creating workflows-as-applications. PLOS Computational Biology 18(12) (2022): e1010705. https://doi.org/10.1371/journal.pcbi.1010705
  • Terzian P, Olo Ndela E, Galiez C, Lossouarn J, Pérez Bucio RE, Mom R, Toussaint A, Petit MA, Enault F. PHROG: families of prokaryotic virus proteins clustered using remote homology. NAR Genomics and Bioinformatics, Volume 3, Issue 3, lqab067 (2021). https://doi.org/10.1093/nargab/lqab067
  • Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol 35, 1026–1028 (2017). https://doi.org/10.1038/nbt.3988
  • Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics, 34:3094-3100 (2018). https://doi.org/10.1093/bioinformatics/bty191
  • Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools, Bioinformatics, Volume 25, Issue 16, Pages 2078–2079 (2009). https://doi.org/10.1093/bioinformatics/btp352
  • Woodcroft BJ, Newell R, CoverM: Read coverage calculator for metagenomics (2017). https://github.com/wwood/CoverM
  • Roach, M. J., Hart, B. J., Beecroft, S. J., Papudeshi, B., Inglis, L. K., Grigson, S. R., Mallawaarachchi, V., Bouras, G., & Edwards, R. A. Koverage: Read-coverage analysis for massive (meta)genomics datasets. Journal of Open Source Software, 9(94), 6235, (2024). https://doi.org/10.21105/joss.06235
  • Hagberg AA, Schult DA, and Swart PJ. Exploring network structure, dynamics, and function using NetworkX. In Proceedings of the 7th Python in Science Conference (SciPy2008), Gäel Varoquaux, Travis Vaught, and Jarrod Millman (Eds), (Pasadena, CA USA), pp. 11–15 (2008).
  • Gurobi Optimization. https://www.gurobi.com/.