ClusterONE Web Documentation

1. Introduction

ClusterONE Web is a freely available, web-based tool for identifying and analyzing protein complexes from protein-protein interaction (PPI) networks. The tool provides an interactive interface for running ClusterONE on preloaded datasets or user-uploaded networks, along with built-in Gene Ontology (GO) enrichment analysis.

Access the web server at: https://paccanarolab.org/clusteroneweb/

2. Getting Started

To use ClusterONE Web:

• Open the web server in a compatible browser (Chrome, Firefox, Edge).
• Select a dataset from the preloaded database or upload your own.
• Use the Quick Example button to load an example dataset instantly.
• Adjust parameters as needed or use the Quick Run button to execute ClusterONE with default settings.
• Explore detected complexes and enrichment results.

3. Input Data

ClusterONE Web provides access to a database of PPI datasets from sources such as BioGRID, IntAct, MINT, and DIP, spanning multiple organisms.

Users must first select an organism from the dropdown list; they can either type to search or directly choose from the options by scrolling down.

Users can also upload their own PPI networks in TXT or CSV format, and optionally provide Gene Ontology annotation files for enrichment analysis. These files should follow a specific format, where each line consists of “id1 id2 weight”, with id1 and id2 representing the interacting proteins and weight indicating the confidence value between 0 and 1. The identifiers (id1 and id2) may be user-defined and do not need to correspond to known database identifiers such as UniProt IDs. If the weight is omitted, it is assumed to be 1, which applies to unweighted PPI networks. The columns of the input file may be separated by commas, spaces, or tabs; however, these separator characters should not be mixed within the same file.

Database version information: GOA files 2025-03-08, BioGRID version 4.4.237, and IntAct, MINT, and DIP were downloaded in September 2024.

4. Running ClusterONE

Users can run ClusterONE with the default parameters using the Quick Run button or customize the following settings by clicking on Run ClusterONE:

• Minimum size: The minimum size of clusters deemed relevant by ClusterONE. This is a hard threshold: whenever ClusterONE finds a cluster smaller than the minimum size, the cluster will be discarded immediately.
• Minimum density: The minimum density of clusters deemed relevant by ClusterONE. The density of a cluster is the total sum of edge weights within the cluster, divided by the number of theoretically possible edges within the cluster. In other words, this is the average edge weight within the cluster if missing edges are assumed to have a weight of zero. Whenever ClusterONE finds a cluster that has a smaller density than the value given here, the cluster will be discarded immediately. Increase the minimum density if you get too many clusters and they seem too sparse, or decrease it if you are not getting enough clusters.
• Max overlap: For each pair of clusters found, ClusterONE calculates a score that quantifies the overlap between them, and two clusters are merged if this overlap is larger than a given threshold.
• Penalty: Penalty value corresponding to each node. When you set this option to a specific value x, ClusterONE will assume that each node has an extra boundary weight of x when it considers the addition of the node to a cluster. It can be used to model the possibility of uncharted connections for each node, so nodes with only a single weak connection to a cluster will not be added to the cluster as the penalty value will outweigh the benefits of adding the node. The default penalty value is 2.

Clicking the Quick Example button runs ClusterONE on the Saccharomyces cerevisiae dataset from Collins et al. (Collins et al., Mol. Cell. Prot., 2007), using default parameters. The results showcase the detection and analysis of protein complexes within this well-characterized interactome.

5. Exploring Results

The main results are displayed in a table that includes the following columns:

• Complex ID
• Overlapping complexes
• Size
• Density
• Cohesiveness
• List of proteins in each complex

Users can sort, filter, search by name, and hide columns as needed. A "Filter" button, located at the top left corner of the main results page, allows users to easily customize filters. For example, selecting the column "Proteins," operator "contains," and value "SYP" retrieves complexes containing proteins matching this string. If a complex overlaps with other complexes, the corresponding complex IDs are listed in the 'Overlapping Complexes' column; otherwise, this column is left empty.

Interactive Graph Features

When you click on a Complex ID, an interactive graph displays the selected complex. Circle nodes represent proteins, while square nodes represent overlapping clusters.

If you select Highlight Overlapping Proteins, those proteins will appear enlarged and in green, making them easier to identify.

Click Actions

• Double-click on a Complex: Opens the corresponding interactive graph.
• Double-click on a Protein: Opens a new tab linking to the corresponding UniProt page.
• Single-click on a Protein: Highlights the protein and its interactions.
• Single-click on the Background: Resets all edges and nodes to their default view.

6. Functional Enrichment Analysis

Enrichment analysis identifies overrepresented GO terms in detected complexes, categorized into the three GO domains:

• Biological Process
• Molecular Function
• Cellular Component

After selecting a complex, users can click the Enrichment Analysis button to view its functional enrichment results. The plots show the top 20 over-represented terms for each GO domain.

The platform also allows users to search for specific GO terms using the Search by GO term button, enabling functional exploration of the detected complexes. The filter button allows users to filter the table by "Complex ID," "GO ID," or "Term" (GO term name).

7. Exporting & Downloading Results

ClusterONE Web allows users to download various result types available on the platform, including:

• ClusterONE results: Downloadable in CSV format for further analysis. Download button is located in the main results table.
• Publication-quality images: High-resolution visuals for each detected complex. Download button is located at the bottom right corner of the interactive graph visualization page as a camera icon.
• Enrichment analysis reports: Comprehensive reports available in PDF format. Download button is located in the Enrichment Analysis page for each complex.

These features make it easy to integrate ClusterONE Web outputs into your research workflow.

8. FAQ / Troubleshooting

9. Contact & Support

ClusterONE Web is free to use, requires no login, and uses cookies to improve user experience.

For bug reports or feature requests, contact alberto.paccanaro@rhul.ac.uk .

PaccanaroLab