2026-06-14T03:42:57Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/167792024-09-27T19:13:31Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609
Repisalud
author
Guerrero-Tamayo, Ana
author
Sanz Urquijo, Borja
author
Casado, Concepcion
author
Moragues, María Dolores
author
Olivares, Isabel
author
Pastor-López, Iker
2023-12-12T12:00:04Z
2023-12-12T12:00:04Z
2023
IEEE ACCESS. 2023,11:95796-95812.
10.1109/ACCESS.2023.3311752
2169-3536
IEEE Access
http://hdl.handle.net/20.500.12105/16779
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.
eng
Convolutional neural network
Deep learning
Genetic recombination
Genome mathematical pattern
Genome mathematical signature
HIV-1
Discovering Mathematical Patterns Behind HIV-1 Genetic Recombination: A New Methodology to Identify Viral Features
research article
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DiscoveringMathematicalPatternsBehind_2023.pdf
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DiscoveringMathematicalPatternsBehind_2023.pdf.txt