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VirusPredictor: XGBoost-based software to predict virus-related sequences in human data.

Guangchen Liu1,2,3, Xun Chen1, Yihui Luan2

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We developed VirusPredictor, a machine learning tool to identify unknown viral sequences in patient data. This software accurately classifies sequences, aiding in the discovery of novel infectious viruses and endogenous retroviruses.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Machine Learning

Background:

  • Identifying novel viruses without reference genomes is challenging due to unmappable sequences in high-throughput data.
  • Existing software lacks specialized capabilities for accurate viral sequence prediction in human samples.

Purpose of the Study:

  • To develop and validate a machine learning method for predicting viral sequences, including uncharacterized viruses and endogenous retroviruses (ERVs), from human data.
  • To create a user-friendly software tool, VirusPredictor, for accurate classification of unmappable sequences.

Main Methods:

  • Developed a two-step XGBoost classification model utilizing an in-house viral genome database.
  • The first step classifies sequences into infectious virus, ERV, or non-ERV human categories.
  • The second step further classifies infectious viral sequences into six taxonomic subgroups.

Main Results:

  • Prediction accuracy increased with sequence length, reaching 0.98 for sequences >2000 bp.
  • Classification accuracy for infectious viruses ranged from 0.92 to >0.98 based on sequence length.
  • VirusPredictor demonstrated high accuracy when applied to real genomic and metagenomic datasets.
  • This study is the first to classify ERVs within infectious viral sequence prediction and combine virus subgroup predictions.

Conclusions:

  • VirusPredictor accurately predicts the origin of unmappable sequences in human data, including novel viruses and ERVs.
  • Longer sequences (ideally >850 bp) improve prediction accuracy; de novo assembly of short reads is recommended.
  • VirusPredictor is a valuable open-source tool for advancing viral discovery and diagnostics.