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DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Jul 6, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach.

Chul Jun Goh¹, Hyuk-Jung Kwon^1,2, Yoonhee Kim¹

¹Eone-Diagnomics Genome Center, Inc., 143, Gaetbeol-ro, Yeonsu-gu, Incheon 21999, Republic of Korea.

Diagnostics (Basel, Switzerland)

|January 11, 2024

Summary

This summary is machine-generated.

This study introduces a novel machine learning method to accurately detect copy number variations (CNVs) in newborns, improving the diagnosis of genetic disorders and chromosomal disabilities.

Keywords:

CNV Korean newborn genome-wide SNP array genomic wave machine learning

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Area of Science:

Genomics
Medical Genetics
Bioinformatics

Background:

Copy number variation (CNV) is a major cause of structural genomic variation and associated disorders.
Accurate analysis of neonatal CNVs is critical for managing chromosomal disabilities.
Genomic waves can compromise the precision of CNV detection.

Purpose of the Study:

To develop and validate a novel machine learning-based method for accurate neonatal CNV analysis.
To address the challenge of genomic waves affecting CNV detection accuracy.
To identify CNVs and associated genetic disorders in a large cohort of Korean newborns.

Main Methods:

Development of a new method utilizing a modified log R ratio to counteract genomic wave influences.
Application of a machine learning approach for enhanced CNV detection.
Validation using samples with known CNVs and comparison with Next-Generation Sequencing (NGS) data.

Main Results:

The new method demonstrated superior performance in identifying CNVs compared to traditional log R ratio methods.
Analysis of 16,046 Korean newborns revealed CNVs associated with 39 genetic disorders in 342 cases.
Joubert syndrome 4 was the most frequently identified CNV-related disorder.

Conclusions:

The developed method, employing a genome-wide single nucleotide polymorphism array with wave offset, is effective for neonatal CNV identification.
Accurate CNV screening facilitates the identification of disease susceptibilities and chromosomal disease etiologies.
This approach can significantly advance the diagnosis and management of CNV-related genetic disorders in newborns.