PhageTerm: a tool for fast and accurate determination of phage termini and packaging mechanism using next-generation sequencing data

By Julian R. Garneau, F. Depardieu, L. Fortier, David Bikard, M. Monot. Scientific Reports, 2017

617 citations recorded by the source corpus.

The worrying rise of antibiotic resistance in pathogenic bacteria is leading to a renewed interest in bacteriophages as a treatment option. Novel sequencing technologies enable description of an increasing number of phage genomes, a critical piece of information to understand their life cycle, phage-host interactions, and evolution. In this work, we demonstrate how it is possible to recover more information from sequencing data than just the phage genome. We developed a theoretical and statistical framework to determine DNA termini and phage packaging mechanisms using NGS data. Our method relies on the detection of biases in the number of reads, which are observable at natural DNA termini compared with the rest of the phage genome. We implemented our method with the creation of the software PhageTerm and validated it using a set of phages with well-established packaging mechanisms representative of the termini diversity, i.e. 5′cos (Lambda), 3′cos (HK97), pac (P1), headful without a pac site (T4), DTR (T7) and host fragment (Mu). In addition, we determined the termini of nine Clostridium difficile phages and six phages whose sequences were retrieved from the Sequence Read Archive. PhageTerm is freely available (https://sourceforge.net/projects/phageterm), as a Galaxy ToolShed and on a Galaxy-based server (https://galaxy.pasteur.fr).

Subfields: Ecology, Infectious Diseases, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7908e8204f506b11bb9634a0dda6d321758d0aec

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

1. D. Vik
2. Ahmed A. Zayed
3. Ben Bolduc
4. M. C. Gazitúa
5. Simon Roux

Citations in the corpus

• DRAM for distilling microbial metabolism to automate the curation of microbiome function (2020, background): “Further, there are now numerous examples of metabolic genes in ‘viromes’ that are more likely to be microbial DNA contamination (39), which is even a greater concern in metagenomic files where the resultant viruses can include prophages whose ends are challenging to delineate (61,96).”