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Related Experiment Video

Updated: Feb 12, 2026

The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform
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iCopyDAV: Integrated platform for copy number variations-Detection, annotation and visualization.

Prashanthi Dharanipragada1, Sriharsha Vogeti1, Nita Parekh1

  • 1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, India.

Plos One
|April 6, 2018
PubMed
Summary
This summary is machine-generated.

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We developed iCopyDAV, an integrated platform for accurate detection of copy number variations (CNVs) from next-generation sequencing data. This tool addresses biases in sequencing data, improving the identification of genetic variations linked to human diseases.

Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • Copy number variations (CNVs) are significant structural variations impacting human individuality and disease.
  • Next-generation sequencing (NGS) enables genome-wide CNV detection, but accuracy is challenged by systemic biases causing non-uniform read coverage.

Purpose of the Study:

  • To develop an integrated platform, iCopyDAV, for robust CNV detection in whole-genome NGS data.
  • To address challenges in CNV detection caused by sequencing biases and improve the accuracy of identifying copy gain and loss events.

Main Methods:

  • Developed iCopyDAV, a modular platform with modules for data pre-treatment, segmentation, variant calling, annotation, and visualization.
  • Incorporated a functional annotation module for prioritizing CNVs based on functional elements, genomic features, and disease associations.

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  • Utilized parallelization for segmentation algorithms to enhance desktop accessibility.

    • Main Results:

    • Demonstrated the impact of sequencing coverage, read length, bin size, and pre-treatment/segmentation approaches on CNV detection accuracy.
    • Evaluated iCopyDAV performance on both simulated and real NGS data across various sequencing depths.
    • Showcased iCopyDAV's ability to detect the complete spectrum of CNVs accurately.

    Conclusions:

    • iCopyDAV provides an effective, open-source solution for accurate CNV detection from whole-genome NGS data.
    • The platform's modular design and functional annotation capabilities aid in identifying disease-relevant CNVs.
    • iCopyDAV is accessible via GitHub and Docker, facilitating its use in research.