Por favor, use este identificador para citar o enlazar este Item:http://hdl.handle.net/20.500.12105/16779
Título
Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: A New Methodology to Identify Viral Features
Autor(es)
Guerrero-Tamayo, Ana | Sanz Urquijo, Borja | Casado, Concepcion ISCIII | Moragues, María Dolores | Olivares, Isabel ISCIII | Pastor-López, Iker
Fecha de publicación
2023
Cita
IEEE ACCESS. 2023,11:95796-95812.
Idioma
Inglés
Tipo de documento
research article
Resumen
In this article, we introduce a novel methodology for characterizing viral genetic features: the Unified Methodology of recombinant virus Identification (UMI). Our methodology converts genomic sequences into spectrograms, applies transfer learning using a pre-trained Convolutional Neural Network (CNN), and employs interpretability tools to identify the genomic regions relevant for characterizing a viral sequence as recombinant. The UMI methodology does not necessitate multiple sequence alignment or manual adjustments. As a result, it operates much faster, has low computational demands, and is capable of handling substantial amounts of data. To validate this, we applied UMI to one extensively studied and documented case: HIV-1 genetic recombination. We worked with all identified HIV-1 complete sequences (13554 sequences up to 2020), searching for mathematical patterns, signatures, that characterize an HIV-1 sequence as recombinant. CNN’s hit rate (test accuracy) is 94%, with consistent and differentiated decision areas in each category. Using interpretability tools, we verified that the hot zones were similar for sequences of the same subtype and phylogenetic proximity. The leading areas for classifying a sequence as recombinant or non-recombinant are coincident with genomic regions that play a key role in genetic recombination processes. By applying UMI methodology we found that there is indeed a genome mathematical pattern that assesses an HIV-1 sequence as recombinant. In addition, we located its position. Considering expert knowledge, our results showed a substantial, robust and biologically-consistent hit rate. This type of solution can successfully guide the location and subsequent characterization of relevant areas, avoiding the heavy analysis of multiple sequence alignment and manual adjustments.
Palabras clave
Convolutional neural network | Deep learning | Genetic recombination | Genome mathematical pattern | Genome mathematical signature | HIV-1
Versión en línea
DOI
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