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    SCoNEs: R Package for Copy Number Variation Analysis in Tumor-Normal Paired Data
    Digital AssetAvailable

    SCoNEs: R Package for Copy Number Variation Analysis in Tumor-Normal Paired Data

    Faculty of Medicine and Health Sciences
    Core Facility
    McGill University

    SCoNEs is an R package designed to estimate Copy Number Variations (CNVs) in whole genome sequencing (WGS) data from tumor-normal paired samples. Utilizing a read depth approach, SCoNEs accurately identifies regions of genomic amplification or deletion, aiding in the comprehensive analysis of cancer genomes.

    Key Features:

    • Read Depth Analysis: Employs read depth information from WGS data to detect CNVs, providing a reliable method for identifying copy number changes.
    • Tumor-Normal Pairing: Analyzes paired tumor and normal samples to distinguish somatic CNVs from germline variations, enhancing the specificity of CNV detection.
    • User-Friendly Interface: Offers an accessible platform for researchers to perform CNV analysis without extensive computational expertise.

    Availability: SCoNEs is free and open-source, released under the MIT License. Researchers can access and contribute to its development through the GitHub repository.

    Note: All resources and documentation are provided in English.

    By leveraging read depth information in tumor-normal paired WGS data, SCoNEs offers a robust tool for accurate CNV detection, facilitating advancements in cancer genomics research.

    Canadian Centre for Computational Genomics (C3G)

    Canadian Centre for Computational Genomics (C3G)

    Faculty of Medicine and Health Sciences

    Research lab focused on advancing scientific knowledge and innovation.

    GB

    Guillaume Bourque

    Digital AssetAvailable

    SCoNEs: R Package for Copy Number Variation Analysis in Tumor-Normal Paired Data

    Faculty of Medicine and Health Sciences
    Core Facility
    McGill University

    SCoNEs is an R package designed to estimate Copy Number Variations (CNVs) in whole genome sequencing (WGS) data from tumor-normal paired samples. Utilizing a read depth approach, SCoNEs accurately identifies regions of genomic amplification or deletion, aiding in the comprehensive analysis of cancer genomes.

    Key Features:

    • Read Depth Analysis: Employs read depth information from WGS data to detect CNVs, providing a reliable method for identifying copy number changes.
    • Tumor-Normal Pairing: Analyzes paired tumor and normal samples to distinguish somatic CNVs from germline variations, enhancing the specificity of CNV detection.
    • User-Friendly Interface: Offers an accessible platform for researchers to perform CNV analysis without extensive computational expertise.

    Availability: SCoNEs is free and open-source, released under the MIT License. Researchers can access and contribute to its development through the GitHub repository.

    Note: All resources and documentation are provided in English.

    By leveraging read depth information in tumor-normal paired WGS data, SCoNEs offers a robust tool for accurate CNV detection, facilitating advancements in cancer genomics research.

    SCoNEs: R Package for Copy Number Variation Analysis in Tumor-Normal Paired Data
    Canadian Centre for Computational Genomics (C3G)

    Canadian Centre for Computational Genomics (C3G)

    Faculty of Medicine and Health Sciences

    Research lab focused on advancing scientific knowledge and innovation.

    GB

    Guillaume Bourque

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