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For continued testing and demonstration of taxMyPhage we have run all the new genomes added to the INPHARED database in the last 30 days through taxMyPhage (dsDNA phages). Running 250 genomes takes < 2 hrs.
The webversion of taxMyPhage is here or the github . Preprint is here
Analysing these ~200 genomes, demonstrates the large diversity of phages that are routinely being identified. With 50 not falling within currently defined genera
As our group often sequence a lot of bacteriophages, we often want to know if they are new genus and/or species, as quickly as possible. So after several incarnations we have developed taxMyPhage to do this as efficiently as possible. It included contributions several undergrad research projects (Maria Lestido , Moi Thomas and Deven Webster), further brain storming with Thomas and code development, Remi who made it work a lot quicker and made all the conda packages work, and taxonomy guidance and testing from Dann. We now have a standalone tool and Webversion and preprint.
What it will do
The webserver will provide taxonomy for a predicted phage and allow a download of a upper right matrix of similarity against other phages classified by ICTV . It will NOT compare against or phages in NCBI, only species that are classified by ICTV.
The standalone tools offers the same, with additional options. The standalone tool has no restriction on the number of input sequences and can be run on 1000s of sequences. If the provided genomes are not complete then inaccurate results maybe obtained, we are implementing the algorithm developed here, that normalises for sequence similarity over total genome length. Additionally the standalone tool will produce similarity matrices for multi-fasta input, allowing calculation of intergenomic similarity. Uses cases for this might be to identify representative sequences from a large dataset.
The output of taxMyPhage will produce be top right matrix (example below) and a tsv that provides the assigned taxonomy.
Example of new genus and new species
The matrix below shows the query has < 70% ANI to any currently classified phage, thus will be a new genus based on ICTV criteria of >70% being within the same genus. As it is < 70% ANI , it is also < 95% ANI , thus is also a new species. Based on the closest genomes identified in searching, these were all members of the Phapecoctavirus. Further phylogenetic analysis would be required to confirm this is the closest Genera of phages – this is just based on similarity cutoffs.
Example of an existing species and conflicts in taxonomy
Below the query >95% ANI to an existing phage so would be the same species (Muminvirus mumin) . However, there are other things to note .The query has greater than 70% >ANI with two distinct genera (others already classified phages do too). Taxonomy is not perfect , taxMyPhage cannot solve these issues but will report them , for the user then to decide what to do.
Warnings will look like this
Based on the command we use for searching for phages in Genbank for INPHARED database , I recently came across some useful further eutilities commands. Writing here for when I forget
To list the most recent genomes in the last 50 days for instance use the -days flag
esearch -db nucleotide -query "gbdiv_PHG"[prop] -days 50 | efilter -query "40000:800000 [SLEN]" -sort "Date" | esummary | xtract -pattern DocumentSummary -element AccessionVersion -element CreateDate -element UpdateDate -element Title
-element TaxIdthen with esummary and the extract , utilities to extract specific information. In this case the Accession, creation date, update date , title and TaxID . To produce an easily parsable list in tab format.
OR420333.1 2023/10/01 2023/10/01 UNVERIFIED: Macromonas phage BK-30P, complete genome OR473000.1 2023/10/01 2023/10/01 Synechococcus phage S-CREM2, complete genome OR500351.1 2023/10/01 2023/10/01 Xanthomonas phage Murka, complete genome OR257569.1 2023/10/01 2023/10/01 Ruegeria phage RpAliso, complete genome OR338916.1 2023/10/01 2023/10/01 Bacillus phage DZ1, complete genome OR339795.1 2023/10/01 2023/10/01 Escherichia phage phi456, complete genome OR296290.1 2023/10/01 2023/10/01 Escherichia phage SDYTW1-F1-2-2_3, complete genome OR157981.1 2023/10/01 2023/10/01 Staphylococcus phage BUCT_X001, complete genome OQ326580.1 2023/10/01 2023/10/01 UNVERIFIED_ORG: Phage vB_PaeM_Sem1, complete genome OR148986.1 2023/10/01 2023/10/01 Caudovirus D_HF5_2C, complete genome
More ranting to myself than anything, but others might find it useful
The “VMR” is described as
“The current Virus Metadata Resource (VMR) that provides a list of all exemplar viruses can be downloaded from the link below.”
Most of it is and is really useful … apart from the accessions that don’t point to phages and are for bacterial genomes – without warning or indication :
“Bartogtaviriformidae Bartonegtaviriform Bartonegtaviriform andersoni E Bartonella gene transfer agent BaGTA BX897699 Partial genome dsDNA bacteria “
with accession BX897699 pointing to the genome of a GTA in Bartonella … why GTAs are classified as viruses is slightly confusing (but heyho, argument for another day)
But all these accessions point to bacterial genomes ..
BX897699.1Bartonella henselae strain Houston-1, complete genome: 1931047bp CP001357.1 Brachyspira hyodysenteriae WA1, complete genome: 3000694bp CP001312.1 Rhodobacter capsulatus SB 1003, complete genome: 3738958bp CP000830.1 Dinoroseobacter shibae DFL 12, complete genome: 3789584b CP000031.2 Ruegeria pomeroyi DSS-3, complete genome: 4109437bp CP015418.1 Rhodovulum sulfidophilum DSM 1374, complete genome: 4132586bp
If you were trying to use the VMR to automatically extract all representative viral genomes that infect bacteria … then there is need to check they are actually the bacteriophages (or GTAs!) and not the entirely bacterial genome
With the influx of phage genomic data, there have been several changes to bacteriophage taxonomy. See the paper “A Roadmap for Genome-Based Phage Taxonomy” by Evelien Adriaenssens and Dann Turner who have led these efforts with their work with ICTV. As a result, the classical phage families of Podoviridae, Siphoviridae and Myoviridae are: kaput, have shuffled off their mortal coil, given up the ghost, or any other preferred phrase… generally, they are no more. Some will be upset by this, I am sure. But personally, I am more than happy to see them go. While they were useful for a period of time, the ability to rapidly sequence most phage genomes and put phages “in boxes” based on their genomic content, rather than what they looked like, offers so much resolution to describe differences. For Podoviridae, Siphoviridae and Myoviridae aficionados, the morphotypes of Myovirus, Podovirus and Siphovirus live on.
As a result of updates to phage taxonomy, 100s of new genera have now been created. For our own work, we are interested in rapidly identifying which genus newly isolated phages fall within. Consequently, we have collected genomes of all (dsDNA) phages classified by ICTV and organised them into directories based on genus. Extracted the common marker terL for each phage that can then be used for input into alignments for creating phylogenies, to determine how related a new phage may be. Additionally, we have run VIRIDIC on each genus.
We are hoping to have an automated process available that will rapidly identify if a new phage genome falls within a known phage genus, based on currently ICTV guidelines. As others are probably comparing phage genomes to know taxa, we have made all the data downloadable as a single file called ICTV_genera.tar.gz, which can be downloaded here as it may well be useful to others.
It contains 1653 directories (Genera)
Each directory contains:
To get the data use :
wget http://warwick.s3.climb.ac.uk/inphared/ICTV_genera.tar.gz
tar -xvf ICTV_genera.tar.gz If you find this useful, consider citing Cook, et al 2021. INfrastructure for a PHAge REference Database: Identification of Large-Scale Biases in the Current Collection of Cultured Phage Genomes. PHAGE. https://doi.org/10.1089/phage.2021.0007
Ryan has recently been testing the short read eliminator kit from circulomics to enrich for long read for nanopore sequencing of viromes. The input for virome samples was various liquids produced or excreted by cows ….often smelly and sticky, generally not much fun to extract the viral fraction from.
Given the nature of the samples getting large amounts of DNA, let alone HMW DNA from the small sample volumes is not possible. Resorting to MDA amplification to produce enough DNA for the nanopore sequencing. To enrich for longer reads Ryan tried multiple samples without any enrichment with the short read eliminator kit.
The results of which look encouraging
It shifts the median read length from 1.9 kb to 6.9 kb, which still aren’t huge reads. But given average size of phage genomes and the low input issues, it might make a difference to the final assembly. Initial results of assembly of a small number of samples look encouraging, with ~1000 predicted complete phage genomes as predicted from checkV. This is in line with similar number of genomes we have previously assembled from a single seawater sample, where we found a 650 kb phage genome.
We still have to determine how this SRE kit might exclude some phages that have small genomes. As we have previously observed differences in the population of smaller phage genomes when comparing nanopore and illumina sequencing (Cook et al 2021 Microbiome).
*As i found this recently- here is Ryan talking about some of his previous work on PromethION sequencing of viromes
Recently PHROGs was released by Terzian et al (https://doi.org/10.1093/nargab/lqab067 ). Full details are provided on their webpages and publication. Briefly their curated dataset provides tens of thousands of PHROGs with a standardised annotation attributed to each PHROG. All of this is available through their searchable website and can also be downloaded.
For first pass phage genome annotation this seems like a great resources. We standardly use Prokka for annotation of phage genomes, that allows custom hmm databases to be used for annotation. Unfortunately the HMMs provided directly by the PHROGs team don`t sit neatly into Prokka and allow the annotation linked to the PHROG to appear in the final annotation, because of differences in formats.
However, as they provided all their data in an easily downloadable form. We have taken this and reformatted to produce HMMs with the annotations included so it plays nicely with HMMER3 as part of Prokka . We have produced a single file that can but put in /opt/prokka/db/hmm directory of Prokka. Thanks to Thomas Sicheritz-Pontén for helping with sorting out getting the correct annotation into the 38,000 HMMs …
A single file containing all HMMs that can be directly added to Prokka , can be downloaded here. Warning its 3 Gb when unzipped. Thanks to Terzian et al who did all the hard work on producing the original PHROGs and curated annotation and making it available , we have just reformatted it for our own use and anybody else that might want to use it with prokka..
To get it running within prokka. Locate the installation of prokka
$prokka –listdb
In my case this results in output of /usr/local/bioinf/prokka/db
and [08:43:23] * HMMs: all_VOG HAMAP
telling us there are already some HMMs databases called all_VOG & HAMAP
Within /usr/local/bioinf/prokka/db is the a directory called hmm
Thus, the full path is /usr/local/bioinf/prokka/db/hmm
The downloaded database needs to be copied into /usr/local/bioinf/prokka/db/hmm
Then run $prokka –setupdb
Running the command $prokka –listdb
[08:43:23] * HMMs: all_phrogs all_VOG HAMAP
all_phrogs will now be used by prokka. If you only want to use the PHROGs database, consider using the prokka flag of –hmms and specify /usr/local/bioinf/prokka/db/hmm/all_phrogs
Full details on adding databases are explained on the Prokka github page
What started off sometime in 2019 as search for a number, too put into an introduction of a paper ends up a few years later with hopefully a useful paper. That number was how many complete phage genomes are currently publicly available via public databases are currently available. At the time, NCBI virus had not been released (https://www.ncbi.nlm.nih.gov/labs/virus/vssi/), which contains some of this information. Myself and Nathan Brown wrote a quick script that used the esearch/efetch factilies to extract phage genomes. Then applied several filtering steps to extract “complete” phage genomes with lots of manual filtering. We started providing this data on the website for download. After requests from people of how to cite this list and some reminding from Branko Rihtman, we have finally got to a pre-print. Ryan Cook has tidied up the code a lot and parsed lots of informtion that can be extracted from the genbank files.
In extracting this informaiton we found many things
There is big bias in the hosts that phage are isolated on – most phages are isolated on a small number of host bacteria
Far more lytic phage genomes than temperate – with most temperate phage genomes coming from an even smaller number of hosts
The number of putative antibiotic resistance genes is different for lytic versus temperate phages and host
Jumbo phages are not always rare – again dependenent on the host
Even for hosts where large numbers of phage have been isolated, we are a long way from sampling the number predicted phage species t
All the data can be accessed via github https://github.com/RyanCook94/
And the paper on https://www.biorxiv.org/content/10.1101/2021.05.01.442102v1.article-metrics
Having recently sequenced several coliphages, we have wanted to compare them to all other coliphages. To do this, we have downloaded all complete (or near complete) bacteriophages genomes [see here]. We then filtered these genomes based on their GenBank description to pull out all phages that have Escherichia, E.coli or coliphage in their description. Having done this we then used an all v all comparison of using MASH, to construct a matrix of similarity. Then visualised this using the heatmaply.
This can be seen below. An interactive webpage of the image is available here
Looking closely at the clusters it is clear to see that phage with genus form discrete clusters eg top right of the plot is T4virus (and other genera in the Tevenvirinae subfamily)