BioinformaticsBioinformaticsPeer-reviewed journal article
Phables: from fragmented assemblies to high-quality bacteriophage genomes
Vijini MallawaarachchiiD, Michael J. RoachiD, Przemysław DecewicziD, Bhavya Nalagampalli PapudeshiiD, Sarah K. GilesiD, Susanna R. GrigsoniD, George Spyro BourasiD, Ryan D. HesseiD, Laura K. InglisiD, Abbey L. K. HuttoniD, Elizabeth Ann DinsdaleiD, Robert Alan EdwardsiD
- Citations
- 49
- FWCI
- 7.93
- Field percentile
- top 1%
Abstract
MotivationMicrobial communities influence both human health and different 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 the challenges in viral assembly, fragmentation of genomes can occur, leading to the need for new approaches in viral identification. Therefore, the identification and characterisation of novel phages remain a challenge. ResultsWe 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. Availability and ImplementationPhables is available on GitHub at https://github.com/Vini2/phables. Contactvijini.mallawaarachchi@flinders.edu.au
- organism: bacteria (other)
- assay: metagenomics
- research focus: variant calling
- approach: statistical model
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