Discovering mathematical patterns behind HIV-1 genetic recombination: a new methodology to identify viral features

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dc.contributor.authorGuerrero Tamayo, Ana
dc.contributor.authorSanz Urquijo, Borja
dc.contributor.authorCasado, Concepción
dc.contributor.authorMoragues Tosantos, María Dolores
dc.contributor.authorOlivares, Isabel
dc.contributor.authorPastor López, Iker
dc.date.accessioned2025-07-24T08:36:28Z
dc.date.available2025-07-24T08:36:28Z
dc.date.issued2023-09-04
dc.date.updated2025-07-24T08:36:28Z
dc.description.abstractIn 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.en
dc.description.sponsorshipThis work was supported by the Research Training Grants Program, University of Deustoen
dc.identifier.citationGuerrero-Tamayo, A., Urquijo, B. S., Casado, C., Tosantos, M.-D. M., Olivares, I., & Pastor-Lopez, I. (2023). Discovering mathematical patterns behind HIV-1 genetic recombination: a new methodology to identify viral features. IEEE Access, 11, 95796-95812. https://doi.org/10.1109/ACCESS.2023.3311752
dc.identifier.doi10.1109/ACCESS.2023.3311752
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3281
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherConvolutional neural network
dc.subject.otherDeep learning
dc.subject.otherGenetic recombination
dc.subject.otherGenome mathematical pattern
dc.subject.otherGenome mathematical signature
dc.subject.otherHIV-1
dc.titleDiscovering mathematical patterns behind HIV-1 genetic recombination: a new methodology to identify viral featuresen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage95812
oaire.citation.startPage95796
oaire.citation.titleIEEE Access
oaire.citation.volume11
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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