PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies

By Sajia Akhter, Ramy Karam Aziz, Robert A. Edwards. Nucleic Acids Research, 2012

486 citations recorded by the source corpus.

Prophages are phages in lysogeny that are integrated into, and replicated as part of, the host bacterial genome. These mobile elements can have tremendous impact on their bacterial hosts’ genomes and phenotypes, which may lead to strain emergence and diversification, increased virulence or antibiotic resistance. However, finding prophages in microbial genomes remains a problem with no definitive solution. The majority of existing tools rely on detecting genomic regions enriched in protein-coding genes with known phage homologs, which hinders the de novo discovery of phage regions. In this study, a weighted phage detection algorithm, PhiSpy was developed based on seven distinctive characteristics of prophages, i.e. protein length, transcription strand directionality, customized AT and GC skew, the abundance of unique phage words, phage insertion points and the similarity of phage proteins. The first five characteristics are capable of identifying prophages without any sequence similarity with known phage genes. PhiSpy locates prophages by ranking genomic regions enriched in distinctive phage traits, which leads to the successful prediction of 94% of prophages in 50 complete bacterial genomes with a 6% false-negative rate and a 0.66% false-positive rate.

Subfields: Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9c77d195db61dda2be6bc4cbf7e403f9f2a36b34

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Citations in the corpus

RASTtk: A modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes (2015, background): “PhiSpy uses a combination of several independent heuristic methods to identify regions in the genome, which may be derived from phages or mobile elements.”
VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data (2017, methodology): “These provirus finding programs include Phage_Finder [9], Prophinder [10], PHAST [11], and PhiSpy [12].”
Computational approaches to predict bacteriophage–host relationships (2015, methodology): “For example, 12-mer oligonucleotides have previously been used to define a library of ‘phage words’ that uniquely identify prophages in bacterial genomes (Akhter, Aziz and Edwards 2012).”
Lysogeny in nature: mechanisms, impact and ecology of temperate phages (2017, background): “…temperate phages: Prophage sequences can be identified from whole or draft microbial isolate genomes (Lima-Mendez et al., 2008; Zhou et al., 2011; Akhter et al., 2012; Roux et al., 2015a); theoretically from single-cell genomes (Roux et al., 2014; Labonte et al., 2015b); or from microbial…”
PHASTER: a better, faster version of the PHAST phage search tool (2016, background): “PhiSpy, in addition to performing sequence similarity-based searches, analyzes several other sequence-based statistics to help identify novel phages that are not represented in existing phage databases.”
Identifying viruses from metagenomic data using deep learning (2020, background): “…[13], VirSorter [4], JGI Earth Virome Pipeline [19], MARVEL [6], and most provirus ﬁ nding tools, Prophinder [15], Phage-Finder [14], PhiSpy [16], and PHASTER [17], determine viral sequences mainly by comparing genes in the query sequence against viral gene databases, and see if there is…”