Phigaro: high-throughput prophage sequence annotation

Abstract

Summary Phigaro is a standalone command-line application that is able to detect prophage regions taking raw genome and metagenome assemblies as an input. It also produces dynamic annotated ‘prophage genome maps’ and marks possible transposon insertion spots inside prophages. It is applicable for mining prophage regions from large metagenomic datasets. Availability and implementation Source code for Phigaro is freely available for download at https://github.com/bobeobibo/phigaro along with test data. The code is written in Python. Supplementary information Supplementary data are available at Bioinformatics online.

CheckV assesses the quality and completeness of metagenome-assembled viral genomes

“…Inverted terminal repeats are a hallmark of transposons 30 but have also been observed in complete viral genomes 31 and phages 32 . Lastly, complete proviruses are identified by a viral region flanked by host DNA on both sides and are commonly found in microbial (meta)genomes 10 , 11 , 23 , 24 . While these are well-established approaches, false positives have also been observed 33 and so, to mitigate this, CheckV reports a confidence level for putative complete genomes based on the estimated completeness from the AAI- or HMM-based approaches: high confidence (≥90% completeness), medium confidence (80–90% completeness) or low confidence (<80% completeness).…”

“…For comparison, we evaluated four other tools to identify host–provirus boundaries, including VIBRANT 11 , VirSorter 10 , PhiSpy 23 and Phigaro 24 . Compared to these four tools, CheckV displayed consistently higher sensitivity but, in particular, when fragments were short or host contamination was low (Fig.…”

“… 23 ) and Phigaro v.2.2.5 (ref. 24 ). All tools were run with default options with the exception of VIBRANT and VirSorter, which were run with the flag ‘--virome’ to increase sensitivity.…”

“…With regard to host contamination on proviruses, existing approaches either remove viral contigs containing a high fraction of microbial genes 5 or predict host–virus boundaries 10 , 11 , 23 , 24 . The former approach allows for a small number of microbial genes while the latter may fail to identify a host region or misidentify the true boundary.…”

Cenote-Taker 2 Democratizes Virus Discovery and Sequence Annotation

“…Several tools have been developed to detect virus sequences in complex datasets. Strategies include detection of hallmark genes conserved within known virus families (but absent in cellular genomes) 4,5 , detection of short nucleotide sequences believed to be enriched in viruses 6 (or other machine learning approaches 7,8 ), or the ratio of genes common to virus genomes versus genes common to non-viral sequences 9 . Each of these tools has pitfalls that can lead to false positives or false negatives and some tools are limited by minimum sequence length or are only geared to detect a limited range of virus families.…”