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

The virus clustering programme vConTACT2 is a fantastic tool for applying taxonomy to large sets of viral contigs. In short, it clusters unknown viruses with those in the RefSeq database based on shared protein clusters.

To provide even more context to viral clusters though, you may wish to include more reference genomes than those in RefSeq.

To supplement the RefSeq genomes, I took all of the phage genomes on MillardLab, and removed the RefSeq genomes (to avoid duplication). The remaining genomes were processed through dedupe.sh at 95% minimum ID to remove highly similar sequences. This led to a custom subset of 7,527 genomes.

Genes were called on the 7,527 genomes using Prodigal. From this, .faa and .csv mapping files were produced so the reference genomes could be used to supplement vConTACT2 clustering.

Click HERE for the mapping (.csv) file.
Click HERE for the sequence (.faa) file.

Furthermore, a list of these genomes can be obtained from the mapping file using the following command (potentially useful when visualising the resultant network):

awk -F ',' '{print $2}' database.csv | sort | uniq

Happy clustering!

The new virome quality control software, ViromeQC, determines viral enrichment of sequenced viromes. In short, fastQ reads are aligned to ribosomal sequences using Bowtie and bacterial signature sequences using DIAMOND. These markers of bacterial contamination are used to estimate viral enrichment.

The pipeline was built using DIAMOND v.0.9.9. At the time of writing, the latest version of DIAMOND is v.0.9.29. Somewhere between these two versions, the format of DIAMOND databases changed. Therefore, if you have the latest version of DIAMOND, the pipeline will not run properly and you may see this error:

Error: Database was built with an older version of Diamond and is incompatible.

The issue is with the database:

viromeqc/index/amphora_bacteria.dmnd

To overcome this, I installed DIAMOND v.0.9.9, extracted the sequences from the database, and produced a new database using DIAMOND v.0.9.29 as follows:

/v.0.9.9/diamond getseq -d amphora_bacteria.dmnd | /v.0.9.29/diamond makedb -d new_db.dmnd

The new version of the database can be downloaded here:

http://s3.climb.ac.uk/ADM_share/crap/amphora_bacteria.dmnd

Replace the old database with the new one and viromeQC should run beautifully.

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)

The lab has now moved from Warwick Medical school to the Dept of Infection, Immunity, and Inflammation at the University of Leicester. To be more specific, I have moved with the rest of lab group still at Warwick.

After 17 years at Warwick and knowing who to speak to and where to find things, it has been an interesting experience starting at Leicester.  Not knowing how to get into or where exactly my office/lab is in the building, has provided a new experience. But also great to meet new colleagues, who have helped me find my way.

October has brought the start of a new term and the arrival of two new lab members (ok it November before posting)  Branko joins the lab to work as an ESPRC fellow working on AMR, he joins Paul who has further extended his ESPRC fellowship and will be with us for a few more months.

Slawek joins as a CENTA PhD student, who will be looking at the role of the marine VIROME in the maintaining a pool of AMR genes.

*Alex Wilcox has also joined the lab and like Branko and Paul he has gained an ESPRC fellowship

We will be running a bacteriophage genome assembly and annotation workshop at WMS on Monday 9th of January.  The course will be run on CLIMB  virtual machines, so please register for an account in advance.

Details

Date: Monday 9th January 2017

Cost: £50

Registration and payment by Monday 5th December* – registration form is open – here

Spaces: 20

Overview

Attendees may provide up to four samples of bacteriophage DNA (by 5th Dec 2016) in advance of the workshop, which will be sequenced and the data available for analysis on the 9th of Jan. During the workshop attendees will learn how to quality control their data, assemble bacteriophage genomes, annotate and prepare their genome in a format for submission to EBI.

Registration is on a first come first served basis, so register early. Spaces are limited to 20 people

MRC CLIMB infrastructure will be used for the workshop.

Prior experience:

No prior experience of genome annotation is needed.

Computing/analysis:

Analysis will be run on CLIMB. Users are encouraged to bring their own laptop – a limited number of laptops are available.

A free CLIMB account is also needed. Register here

DNA samples

DNA samples must be received by Monday 5th December for them to be sequenced in time for the course. It is not necessary to send phage samples to attend the course. The workshop is a genome annotation training workshop- sequencing of isolates is a bonus so that attendees can annotate their own genomes.

DNA samples must be sent in a 96 well plate. A minimum of 10 ul of DNA at 10 ng /ul DNA is required. Larger volumes are fine, but must be at concentration of 10 ng / ul.

DNA must be column purified prior to sending, Zymo DNA purification/columns are recommended. Concentration must be determined by use of fluorescent detection system (eg Qubit), not Nanodrop .

Phage do not have to be CsCl purified prior to DNA extraction. The method we use for extraction can be found here https://peerj.com/articles/2055/ , then run through a DNA clean-up column

Please contact Andrew Millard on a.d.millard@warwick.ac.uk after registration, prior to sending any samples. DO NOT JUST SEND SAMPLES

1. Contact a.d.millard@warwick.ac.uk prior to sending
2. Label the plate so that your name can be read on the side of the plate.
3. Complete the form that will be sent when you contact a.d.millard@warwick.ac.uk
4. Sample names are to be alphanumeric only.

We lost all our content . But hopefully it will be back soon ….

And then Branko  fixed it 🙂

Given a recent increase in the number of bacteriophage genome sequenced- Nathan ( @NathanMB3) has updated the all-v-all  comparison with more genomes (~5500 in total).Image at bottom of page

After reading the recent paper  “MASH:fast genome and metagenome distance and estimation using MinHash” and meeting Nathan Brown at the University of Leicester, we discussed using MASH for identification of  phage genomes and comparison thereof.  The authors of the genome biology paper had included viruses in the microbial comparison in Figure 3 . Here we just focused on bacteriophage genomes.

For rapid identification of phage genomes we first constructed a database of phage genomes that were public. This included all phage genomes from the NCBI (ftp://ftp.ncbi.nlm.nih.gov/genomes/Viruses/all.fna.tar.gz) , which were then filtered to remove eukaryotic viruses. In addition phage genomes were collected from the phagesdb.org website. A sketch was made for all of these phages and collated, the mash database of this can be downloaded here.

We are using this database to rapidly identify newly sequenced phage isolates. This has worked well with the 100+ novel phages isolated so far and gives very similar results to blastn if there similar phage already in the database (it’s just quicker). We have found it to be good starting point for further comparative genomics.

Using this database we then constructed an all-versus-all comparison of phage genomes. The advantage of MASH is that it allows this to be done in an extremely rapid manner.  MASH outputs a text file with a Jaccard distance for each pair of genomes.  The Jaccard distance is a measure of dissimilarity between genomes (on a scale of 0 to 1, where 0 is nearly identical and 1 is completely different), which we then plotted on a heatmap comparing all phages genomes.  To do that we used the NeatMap package (Rajaram and Oono, BMC Bioinformatics 2011) in R to first arrange the phage genomes along the axes using an nMDS clustering algorithm with the Jaccard distances (Taguchi and Oono, Bioinformatics 2005) and then plot a heatmap.  The ordering of the phage genomes and the hierarchical clustering shown on each axis are based on the nMDS results and are not the same as a phylogenetic/genomic tree.

Regardless, the clustered phage genomes shown by the green squares on the heatmap are – for the most part – related to each other according to existing phage taxonomy.  This confirms that MASH coupled with nMDS clustering of Jaccard distances from MASH gives a good approximation of structure in the global phage population sequenced to date.  Further analysis may reveal new patterns in the global phage population structure and illustrate the bias in phage sampling and sequencing. The dense green boxes in the top right are comprised of mycobacterium phages- which are by far the most numerically abundant in the database.

Below in Figure 1

Figure 1 “The known phage universe”. All versus all comparison of phage genomes.

The script for the clustering and production of heatmap can be found here 

The list of phage on each axis  is here

Below is the updated version with a larger number of genomes than in 2016. Not much has really changed, most phages still have limited similarity to other phages!  just more of them .  Still, many more phage genomes need to be sampled