AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.

Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline u...

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Main Authors: Abozar Ghorbani, Mahsa Rostami, Elham Ashrafi-Dehkordi, Pietro Hiram Guzzi
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0317918
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author Abozar Ghorbani
Mahsa Rostami
Elham Ashrafi-Dehkordi
Pietro Hiram Guzzi
author_facet Abozar Ghorbani
Mahsa Rostami
Elham Ashrafi-Dehkordi
Pietro Hiram Guzzi
author_sort Abozar Ghorbani
collection DOAJ
description Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design. The design_primers_with_tuning module uses a random forest classifier that optimizes parameters and provides flexibility for different experimental conditions. Quality control measures, including the evaluation of poly-X content and melting temperature, increase primer reliability. Unique to AutoPVPrimer is the visualize_primer_dimer module, which supports the visual evaluation of primer dimers-a feature missing in other tools. Primer specificity is validated via primer BLAST, which contributes to the overall efficiency of the pipeline. AutoPVPrimer has been successfully applied to the tomato mosaic virus, proving its adaptability and efficiency. The modular design allows customization by the user and extends the applicability to different plant viruses and experimental scenarios. The pipeline represents a significant advance in primer design and provides researchers with an effective tool to accelerate molecular biology experiments. Future developments aim to extend compatibility and incorporate user feedback to consolidate AutoPVPrimer as an innovative contribution to the bioinformatics toolbox and a promising resource for the advancement of plant virology research.
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institution Kabale University
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language English
publishDate 2025-01-01
publisher Public Library of Science (PLoS)
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spelling doaj-art-95e68c5963d84ae195822d7fffb8e8b92025-02-07T05:30:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031791810.1371/journal.pone.0317918AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.Abozar GhorbaniMahsa RostamiElham Ashrafi-DehkordiPietro Hiram GuzziPlant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design. The design_primers_with_tuning module uses a random forest classifier that optimizes parameters and provides flexibility for different experimental conditions. Quality control measures, including the evaluation of poly-X content and melting temperature, increase primer reliability. Unique to AutoPVPrimer is the visualize_primer_dimer module, which supports the visual evaluation of primer dimers-a feature missing in other tools. Primer specificity is validated via primer BLAST, which contributes to the overall efficiency of the pipeline. AutoPVPrimer has been successfully applied to the tomato mosaic virus, proving its adaptability and efficiency. The modular design allows customization by the user and extends the applicability to different plant viruses and experimental scenarios. The pipeline represents a significant advance in primer design and provides researchers with an effective tool to accelerate molecular biology experiments. Future developments aim to extend compatibility and incorporate user feedback to consolidate AutoPVPrimer as an innovative contribution to the bioinformatics toolbox and a promising resource for the advancement of plant virology research.https://doi.org/10.1371/journal.pone.0317918
spellingShingle Abozar Ghorbani
Mahsa Rostami
Elham Ashrafi-Dehkordi
Pietro Hiram Guzzi
AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
PLoS ONE
title AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
title_full AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
title_fullStr AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
title_full_unstemmed AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
title_short AutoPVPrimer: A comprehensive AI-Enhanced pipeline for efficient plant virus primer design and assessment.
title_sort autopvprimer a comprehensive ai enhanced pipeline for efficient plant virus primer design and assessment
url https://doi.org/10.1371/journal.pone.0317918
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AT elhamashrafidehkordi autopvprimeracomprehensiveaienhancedpipelineforefficientplantvirusprimerdesignandassessment
AT pietrohiramguzzi autopvprimeracomprehensiveaienhancedpipelineforefficientplantvirusprimerdesignandassessment