Documentation

Overview

ViroNEXT is an AI-assisted, fully automated bioinformatics pipeline for unbiased viral detection in next-generation sequencing data. It combines reference-based classification for known viruses with machine-learning-based prediction to detect highly divergent or previously uncharacterized viral candidates.

Publication Information

Manuscript title

ViroNEXT: A multilayer hybrid system for high-precision detection of known and divergent viruses in metagenomics

Short title

Metagenomic pipeline for viral detection of known and unknown viruses

Authors

Markus Santhosh Braun^†, Gibran Horemheb^† Rubio Quintanares, Martin Machyna, Janice Durschang, Csaba Miskey, Xiang-Jun Lu, Pilar Ramos, Maike Herrmann, Pauline Dianne Santos, Leona Enke Johannes Weydt, Liam Childs, Guillermo M. Ruiz-Palacios, Walter Ian Lipkin, Johannes Blümel^†, Renate König^†
† equal contribution

Correspondence

Renate König

Keywords

Virus detection, Viral metagenomics, Metagenomic sequencing, Pathogen discovery, Machine learning, Bioinformatics

DOI

to be added

Submitting Data

Upload Illumina short-read sequencing data in compressed FASTQ format (.fastq.gz). Single-end and paired-end reads are accepted.

File names must follow the Illumina-style naming pattern: <sample_name>_<R1/R2>_001.fastq.gz

For paired-end data, upload both files for each sample, for example sampleA_R1_001.fastq.gz and sampleA_R2_001.fastq.gz.

A run can contain either single-end reads or paired-end reads, but not a mixture of both. Submit mixed data types as separate jobs.

1. Open the home page.
2. Drag files into the upload area, or click the upload area to choose files.
3. Enter an email address.
4. Click Upload data.

Keep the generated job URL. You need it to follow progress and access results.

Files are uploaded directly in resumable chunks. Before any file data is sent, the server checks available storage against the compressed input size, projected pipeline working space and a reserved safety margin. A submission that does not fit is rejected immediately with an explanatory message.

Interrupted uploads resume from the last confirmed chunk when the same files are selected again in this browser. Incomplete upload sessions are removed after 24 hours.

How the Pipeline Works

ViroNEXT uses two complementary detection branches:

• Reference-based classification: detects known viruses through alignment, taxonomic re-classification, coverage filtering, consensus generation, and assignment.
• Machine-learning-based classification: evaluates assembled contigs for divergent or novel viral candidates.

Job Status

Jobs move through these states:

• Queued: the job is waiting for the worker.
• Running: the analysis pipeline is active.
• Archiving: downloadable results are being prepared.
• Finished: the analysis completed successfully.
• Failed: the analysis stopped before completion.
• Expired: the retention period ended and stored files were removed.

Results and Reports

Results are summarized in a single interactive HTML report. The report includes detected viruses, classification and filtering results, genome coverage statistics, high-quality read assignment proportions, abundance visualizations, coverage profiles, and quality-control summaries.

Highly divergent viral candidates are reported separately with contig length, VirHunter statistics, mapped read counts, and, when available, DIAMOND-based taxonomic classification. Candidate contig sequences can be copied for downstream analysis such as manual BLAST searches, phylogeny, or wet-lab validation.

When a job has finished, the status page lists downloadable result files. If a ZIP archive is ready, all files can be downloaded together. Otherwise, individual result files can be downloaded from the same page.

Report Interpretation

For guidance on evaluating classifications, mapping quality, and coverage metrics, see How to interpret reports.

Retention

Results are available for 7 days after the job finishes. After that, job data is deleted and the job URL returns an expired response.

Usage Statistics

When a job is submitted, the service records the submitting IP address and derives approximate country, state/region and city information for aggregated usage statistics. The lookup runs locally; the address is not sent to a geolocation API.

City-level IP geolocation is approximate and may identify a network provider location rather than the user's physical location. Location estimates use IP Geolocation by DB-IP.

Citation

Please cite Braun et al. xxx, xxxx in any publication that makes use of analyses inspired by ViroNEXT.

Final citation and BibTeX entry: to be added.

Downloads

The pipeline uses reference databases and machine-learning models for viral detection. These databases are available for download for local execution or further analysis.

Reference Databases

Machine Learning Models

Get Help

In case of questions, contact Paul-Ehrlich Institute at bioinformatics [at] pei [dot] de.

Source Code

The project is available on GitHub: https://github.com/brama108/vironext.

Local execution is possible using the GitHub repository. The local workflow uses the same pipeline and can be run outside the web interface after installation of the required environments.

License, release version, container or conda environment, and reproducible example dataset: to be added.

Limitations

ViroNEXT is intended for research use only. It should not be used as the sole basis for clinical decision making.