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 genome annotations that include 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 to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. Results We identified different strengths and weaknesses between the prophage prediction tools. Several tools exhibit exceptional f1 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.

Systematic analysis of prophage elements in actinobacterial genomes reveals a remarkable phylogenetic diversity

“…Using VIBRANT, we were able to identify a large cryptic prophage element in the Corynebacterium glutamicum strain ATCC 13,032, which is in line with previous experimental results 27,28 . In contrast, Virsorter2 25 completely missed this sequence and has recently been found to be less effective 29 than other prophage prediction tools, including VIBRANT 26 , PhiSpy 30 , and Phigaro 31 . Moreover, we also validated if our findings are comparable with the previously published studies on the discovered prophages present in M. abscessus genomes 16,32,33 .…”

“…99.5% for Acinetobacter baumanii 35 and > 90% in Streptococcus pyogenes 8 ) and it cannot be ruled out that several elements might have been missed in our analysis. However, the prediction tool VIBRANT was among the top performing prediction tools in terms of accuracy and precision in recent comparative analysis conducted on a set of manually curated genomes 29 . On average, prophage elements were predicted to account for 1% of the genomes, with some cases going up to 10%, which is in line with previous studies 9 .…”

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

“…To predict prophages in the bacterial genomes, VIBRANT (v. 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 stand-alone tool, so it can be run on local computers.…”

The Promise and Pitfalls of Prophages

“…The need to improve our knowledge of prophage diversity and the role they play in microbial dynamics has been underscored in the literature for two decades (Casjens 2003;Bobay, Touchon, and Rocha 2014;Touchon, Bernheim, and Rocha 2016;Howard-Varona et al 2017;Luque and Silveira 2020;Dutilh et al 2014). Numerous software solutions can identify prophage regions in bacterial genomes, but most utilise homology-based methods to find proteins that look like other phages (Roach et al 2021). We developed PhiSpy, the first comprehensive approach to identify prophage regions from characteristic genomic features in addition to homology to known prophage genes, and have continuously updated and improved it over the last decade (Akhter, Aziz, and Edwards 2012;McNair et al 2019).…”