Philympics 2021: Prophage Predictions Perplex Programs

Abstract

Background Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Methods We present a library of gold-standard bacterial genomes with manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms and identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f 1 score from the analysis of ten different algorithms for the prediction of prophages. Results We identified strengths and weaknesses between the prophage prediction tools. Several tools exhibit exceptional f 1 scores, while others have better recall at the expense of more false positives. The tools vary greatly in runtime performance with few exhibiting all desirable qualities for large-scale analyses. Conclusions Our library of gold-standard prophage annotations and benchmarking framework provide a valuable resource for exploring strengths and weaknesses of current and future prophage annotation tools. We discuss caveats and concerns in this analysis, how those concerns may be mitigated, and avenues for future improvements. This framework will help developers identify opportunities for improvement and test updates. It will also help users in determining the tools that are best suited for their analysis.

Prophage-Derived Regions in Curtobacterium Genomes: Good Things, Small Packages

“…The search for genomic regions of prophage origin has been performed using the online server PHASTER [ 27 ] and the pipeline PhiSpy [ 28 ]. PHASTER currently seems to be the most widely used prophage prediction tool [ 29 ], and a recently published comparative study demonstrated the high levels of accuracy, precision, recall and f 1 score of PhiSpy [ 30 ]. PHASTER has identified 161 possible prophage regions, while only one of them has been defined as an intact phage; PhiSpy has found 75 prophage regions.…”

Prophages and plasmids display opposite trends in the types of accessory genes they carry

“…To predict prophages in the bacterial genomes, VIBRANT (version 1.2.1) was used for the following reasons. First, VIBRANT has comparatively high performance, as reported by a recent benchmark [44]. Second, it is a standalone tool, so it can be run on local computers.…”

The human gut virome: Composition, colonisation, interactions, and impacts on human health

“…In lysogeny, a 'temperate' phage integrates into its host's genome and replicates there as a prophage (Gardner, 1958). Approximately 30% of all phages can reproduce via this lysogenic replication strategy (Roach et al, 2021b). In the gut a large proportion of observed bacteria carry prophage genes (Waller et al, 2014;Kim and Bae, 2018).…”

“…Many computational analyses are simply unable to reliably distinguish prophage regions from the remainder of bacterial genomes (Roach et al, 2021b). This is especially problematic when considering read-based annotation of phages, where it may be impossible to determine if a read originated from a phage or a prophage region in a bacteria's genome.…”

“…Cultivating high-quality viral, phage, and prophage sequences and submitting them to the standard reference databases will prove to be incredibly important in the coming years. Current efforts are underway to mine prophage sequences from existing bacterial genomes using the best-in-class algorithms (Akhter et al, 2012;Roach et al, 2021bRoach et al, , 2022. These will help to build the knowledge base that is required for correctly classifying prophages and temperate phages in metagenome samples (Dutilh et al, 2014;Roux et al, 2015aRoux et al, , 2015bPark and Zhao, 2018;Liu et al, 2019;Zheng et al, 2019).…”