VirClust is now oppened for community feedback

This is a new and higly improved version of VirClust. Testing is in progress. Feedback from the user community is more than welcomed. Please email me at liliana.cristina.moraru( at )uol.de if you experience any problems or you have suggestions for improvements.

The stand-alone version is in work at the moment and not yet available for download.

Whats is VirClust?

VirClust is a bioinformatics tool which can be used for:

• virus clustering

• protein annotation

• core protein calculation

At its core is the grouping of viral proteins into clusters of three different levels:

• at the first level, proteins are grouped based on their reciprocal BLASTP similarities into protein clusters, or PCs.

• at the second level, PCs are grouped based on their Hidden Markov Model (HMM) similarities into protein superclusters, or PSCs.

• at the third, still experimental level, PSCs are grouped based on their HMM similarities into protein super-superclusters, or PSSC.

More about the how it works can be read here DOI: 10.1101/2021.06.14.448304.

How to cite the use of VirClust?

If you are using VirClust, please cite the following pre-print publication: • Moraru, Cristina (2021): VirClust, a tool for hierarchical clustering, core gene detection and annotation of (prokaryotic) viruses. In BioRxiv. DOI: 10.1101/2021.06.14.448304.

Additionally, if you are performing viral protein annotations using VirClust, please also cite the respective databases used for the annotations, see VirClust manuscript for the complete citations

Developer - Cristina Moraru, PhD

Senior Scientist, Department of The Biology of Geological Processes

Institute for Chemistry and Biology of the Marine Environment

Carl-von-Ossietzky –Str. 9 -11, D-26111 Oldenburg, Germany

Email: liliana.cristina.moraru( at )uni-oldenburg.de

CREATE NEW PROJECT

Minimum number of genomes:

• 3 for genome clustering (steps 4-7)

• 1 for only protein clusters and annotations

Accepted input formats: .fasta, .fna or .fa

Sequence names should contain at least one letter.

** no multifasta files here

* Mandatory for new projects

LOAD EXISTING PROJECT

Info board

Each project you create is given a project ID and can be accessed at a later time point, as long as you have performed any calculations (basically, pressed the “Run” button in the next tab). VirClust calculations can take a long time and the browser can disconnect from the server. Save the project ID, to be able to access the results later.

PROTEIN CLUSTERING

Step 1A. Genomes to Proteins

Download results

Step 2A. Proteins to Protein Clusters (PCs)

Remove matches if

Download results

GENOME CLUSTERING

Step 3A. Order genomes hierarchically

Download results

Plot intergenomic similarities

Download results

Step 4A. Calculate stats and split in genome clusters (VGCs)

Download results

*The clustering distance is minimum 0.1 and maximum 1. The higher the value, the lower the number of clusters resulted. At a value of 1, all genomes will belong to the same cluster.

*Known issues: If the chosen clustering distance results in each genome forming its own VGC, then the output PDF will be empty. To solve this problem: increase the clustering distance progressively and recalculate steps 5 and 6.

Output genome clustering PDF

Other options

Download results

CORE PROTEINS

Step 5A. Calculate core proteins for each VGC, based on PCs

Download results

PROTEIN ANNOTATIONS

Step 6A. Annotate proteins

Query the InterPro database using InterProScan
Query the pVOGs database using hhsearch
Query the VOGDB database using hhsearch
Query the PHROGS database using hhsearch
Query the Efam database using hmmscan
Query the Efam-XC database using hmmscan
Query the NCBI database using BLASTP

Merge annotation tables

PROTEIN CLUSTERING

Step 1B. PCs to Protein Superclusters (PSCs)

Keep matches if ...
conditional 1 is true

AND

OR
conditional 2 is true:

AND

AND

Download results

GENOME CLUSTERING

Step 2B. Order genomes hierarchically

Download results

Plot intergenomic similarities

Download results

Step 3B. Calculate stats and split in genome clusters (VGCs)

Download results

*The clustering distance is minimum 0.1 and maximum 1. The higher the value, the lower the number of clusters resulted. At a value of 1, all genomes will belong to the same cluster.

*Known issues: If the chosen clustering distance results in each genome forming its own VGC, then the output PDF will be empty. To solve this problem: increase the clustering distance progressively and recalculate steps 5 and 6.

Output genome clustering PDF

Other options

Download results

CORE PROTEINS

Step 4B. Calculate core proteins for each VGC, based on PSCs
Download results

PROTEIN ANNOTATIONS

Step 5B. Annotate proteins

Query the InterPro database using InterProScan
Query the pVOGs database using hhsearch
Query the VOGDB database using hhsearch
Query the PHROGS database using hhsearch
Query the Efam database using hmmscan
Query the Efam-XC database using hmmscan
Query the NCBI database using BLASTP

Merge annotation tables

PROTEIN CLUSTERING

Step 1C. PSCs to Protein Super-superclusters (PSSCs)

Keep matches if ...
conditional 1 is true

AND

OR
conditional 2 is true:

AND

AND

Download results

GENOME CLUSTERING

Step 2C. Order genomes hierarchically

Download results

Plot intergenomic similarities

Download results

Step 3C. Calculate stats and split in genome clusters (VGCs)

Download results

*The clustering distance is minimum 0.1 and maximum 1. The higher the value, the lower the number of clusters resulted. At a value of 1, all genomes will belong to the same cluster.

*Known issues: If the chosen clustering distance results in each genome forming its own VGC, then the output PDF will be empty. To solve this problem: increase the clustering distance progressively and recalculate steps 5 and 6.

Output clustering PDF

Other options

Download results

Step 4C. CORE PROTEINS

Calculate core proteins for each VGC, based on PSSCs
Download results

PROTEIN ANNOTATIONS

Step 5C. Annotate proteins

Query the InterPro database using InterProScan
Query the pVOGs database using hhsearch
Query the VOGDB database using hhsearch
Query the PHROGS database using hhsearch
Query the Efam database using hmmscan
Query the Efam-XC database using hmmscan
Query the NCBI database using BLASTP

Merge annotation tables

Developer - Cristina Moraru, PhD

Senior Scientist, Department of The Biology of Geological Processes

Institute for Chemistry and Biology of the Marine Environment

Carl-von-Ossietzky –Str. 9 -11, D-26111 Oldenburg, Germany

Email: liliana.cristina.moraru( at )uni-oldenburg.de