ArticlePloS one2018
iCopyDAV: Integrated platform for copy number variations-Detection, annotation and visualization.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Computational strategies for copy number variation detection, disease association, and beyond.Genome biology · 2026Review
- Comparative study of tools for copy number variation detection using next-generation sequencing data.Scientific reports · 2025Article
- A copy number variation detection method based on OCSVM algorithm using multi strategies integration.Scientific reports · 2025Article
- HapCNV: A Comprehensive Framework for CNV Detection in Low-input DNA Sequencing Data.bioRxiv : the preprint server for biology · 2025Article
- CopyVAE: a variational autoencoder-based approach for copy number variation inference using single-cell transcriptomics.Bioinformatics (Oxford, England) · 2024Article
- OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data.BMC genomics · 2024Article
- On the core segmentation algorithms of copy number variation detection tools.Briefings in bioinformatics · 2024Article
- Detection of trait-associated structural variations using short-read sequencing.Cell genomics · 2023Article
- In Silico Identification and Functional Characterization of Genetic Variations across DLBCL Cell Lines.Cells · 2023Article
- Detection of copy number variations based on a local distance using next-generation sequencing data.Frontiers in genetics · 2023Article
- JAX-CNV: A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level.Genomics, proteomics & bioinformatics · 2022Article
- Progress in Methods for Copy Number Variation Profiling.International journal of molecular sciences · 2022Review
- svBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network.BioMed research international · 2022Article
- A shortest path-based approach for copy number variation detection from next-generation sequencing data.Frontiers in genetics · 2022Article
- CNV-PCC: An efficient method for detecting copy number variations from next-generation sequencing data.Frontiers in bioengineering and biotechnology · 2022Article
- CIRCNV: Detection of CNVs Based on a Circular Profile of Read Depth from Sequencing Data.Biology · 2021Article
- Copy Number Variant Detection with Low-Coverage Whole-Genome Sequencing Represents a Viable Alternative to the Conventional Array-CGH.Diagnostics (Basel, Switzerland) · 2021Article
- HBOS-CNV: A New Approach to Detect Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2021Article
- CNV-MEANN: A Neural Network and Mind Evolutionary Algorithm-Based Detection of Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2021Article
- KNNCNV: A K-Nearest Neighbor Based Method for Detection of Copy Number Variations Using NGS Data.Frontiers in cell and developmental biology · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Discovery of copy number variations (CNVs), a major category of structural variations, have dramatically changed our understanding of differences between individuals and provide an alternate paradigm for the genetic basis of human diseases. CNVs include both copy gain and copy loss events and their detection genome-wide is now possible using high-throughput, low-cost next generation sequencing (NGS) methods. However, accurate detection of CNVs from NGS data is not straightforward due to non-uniform coverage of reads resulting from various systemic biases. We have developed an integrated platform, iCopyDAV, to handle some of these issues in CNV detection in whole genome NGS data. It has a modular framework comprising five major modules: data pre-treatment, segmentation, variant calling, annotation and visualization. An important feature of iCopyDAV is the functional annotation module that enables the user to identify and prioritize CNVs encompassing various functional elements, genomic features and disease-associations. Parallelization of the segmentation algorithms makes the iCopyDAV platform even accessible on a desktop. Here we show the effect of sequencing coverage, read length, bin size, data pre-treatment and segmentation approaches on accurate detection of the complete spectrum of CNVs. Performance of iCopyDAV is evaluated on both simulated data and real data for different sequencing depths. It is an open-source integrated pipeline available at https://github.com/vogetihrsh/icopydav and as Docker's image at http://bioinf.iiit.ac.in/icopydav/.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.