ArticleGigaScience2022
Accurate and fast clade assignment via deep learning and frequency chaos game representation.
Article in GigaScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A unified benchmark of supervised and retrieval-based methods for viral genomic sequence classification.Scientific reports · 2026Article
- EIOFX-DT: Leveraging graph centrality metrics for feature extraction and classification of viral genetic sequences.Biotechnology reports (Amsterdam, Netherlands) · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Vertex Assignment for Frequency Chaos Game Representation of Proteins Affects Classification Performance.Computational and structural biotechnology journal · 2026Article
- Identification of mathematical patterns in genomic spectrograms linked to variant classification in complete SARS-CoV-2 sequences.Scientific reports · 2025Article
- A composite universal DNA signature for the tree of life.Nature ecology & evolution · 2025Article
- PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences.BMC bioinformatics · 2025Article
- Positional frequency chaos game representation for machine learning-based classification of crop lncRNAs.bioRxiv : the preprint server for biology · 2025Article
- WaveSeekerNet: accurate prediction of influenza A virus subtypes and host source using attention-based deep learning.GigaScience · 2025Article
- CGRclust: Chaos Game Representation for twin contrastive clustering of unlabelled DNA sequences.BMC genomics · 2024Article
- Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures.PloS one · 2024Article
- Accurate and fast clade assignment via deep learning and frequency chaos game representation.GigaScience · 2022Article
- In silico identification of multiple conserved motifs within the control region of Culicidae mitogenomes.Scientific reports · 2022Article
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Authors and funding
5 authors.
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Abstract
backgroundSince the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade.
resultsIn this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants.
conclusionsBy combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AVAILABILITY: The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.
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