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VirGrapher: a graph-based viral identifier for long sequences from metagenomes
Yan Miao1, Zhenyuan Sun1, Chenjing Ma1
1College of Computer and Control Engineering, Northeast Forestry University, Hexing Road, 150040, Heilongjiang Province, China.
Briefings in Bioinformatics
|February 12, 2024
Summary
VirGrapher improves viral sequence identification by analyzing long viral DNA sequences as graphs. This novel approach captures relationships between subsequences, outperforming existing methods in accuracy.
Area of Science:
- Virology
- Bioinformatics
- Computational Biology
Background:
- Viruses are abundant and crucial in microbial ecosystems.
- Metagenomics involves analyzing all genetic material from environmental samples.
- Accurate viral identification in metagenomes is essential for ecological studies.
Purpose of the Study:
- To develop a method for improved identification of long viral sequences in metagenomic data.
- To address the limitations of existing deep learning methods that fragment long sequences.
Main Methods:
- VirGrapher represents long viral sequences as graphs.
- It employs a Graph Convolutional Network (GCN) model to learn relationships between sequence subsequences.
- A GCN-based node embedding model is utilized for sequence representation.
Main Results:
- VirGrapher demonstrated superior performance in identifying long viral sequences.
- The method achieved higher AUC values and accuracy compared to three benchmark methods on a validation set.
Conclusions:
- VirGrapher effectively captures inter-subsequence relationships for enhanced viral sequence identification.
- This graph-based approach offers a significant advancement over traditional subsequence analysis in metagenomics.
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