Table of Contents

• What is CNV Sequencing?
• Why Choose CNV Sequencing over Microarrays?
• Applications of CNV Sequencing
• CNV Sequencing Workflow
• CNV Sequencing Bioinformatics Analysis
• Sample Requirements
• Why Choose CD Genomics for CNV Sequencing?

What is CNV Sequencing?

Copy Number Variation (CNV) is a prevalent form of structural variation in the human genome, involving duplications or deletions of DNA segments ranging from thousands to millions of base pairs. CNVs are critical biomarkers strongly linked to developmental delays, autism spectrum disorder (ASD), congenital malformations, and various cancers. While large CNVs (>100 kb) are often associated with rare genetic disorders, smaller CNVs (<100 kb) play a significant role in population diversity and complex disease susceptibility.

Our CNV Sequencing service leverages Low-Pass Whole Genome Sequencing (WGS). By sequencing the genome at a coverage depth typically between 0.1x and 5x, we obtain a representative fraction of the genome sufficient for structural analysis. Advanced computational algorithms analyze read depth across genomic "bins" to identify regions with statistically significant deviations—indicating duplications (high depth) or deletions (low depth). This method offers a streamlined, genome-wide alternative to targeted approaches.

Copy number variation between two human individuals . (Chao Xie ,et al., 2009)

Why Choose CNV Sequencing over Microarrays?

While Chromosomal Microarray Analysis (CMA) has historically been the standard for CNV detection, it is limited by fixed probe design and lower throughput. Low-Pass WGS has emerged as the superior alternative, offering higher resolution and unbiased genome-wide coverage at a comparable or lower cost.

Key Advantages of Low-Coverage WGS:

• Excellent Sensitivity: Reliably detects large CNVs (>100 kb) and medium-sized variants (5–10 kb) often missed by arrays.
• Cost-Effectiveness: As sequencing costs drop, Low-Pass WGS provides more data per dollar than array-based methods.
• Higher Data Quality: precise breakpoint determination with fewer false positives compared to hybridization-based arrays.
• Scalability: Ideal for large-scale clinical research cohorts requiring consistent, standardized structural variant detection.

Comparison of aCGH and CNV-seq for Detecting Copy Number Variations. (Chao Xie ,et al., 2009)

Applications of CNV Sequencing

CNV Sequencing is a powerful tool in medical research and diagnostics with applications including:

• Genetic Disorders: CNV Sequencing aids in detecting CNVs that contribute to genetic disorders such as neurodevelopmental diseases, autism, and intellectual disabilities, allowing for early diagnosis and targeted therapies.
• Prenatal Diagnostics: It helps identify chromosomal abnormalities, such as deletions or duplications, in prenatal samples, offering critical insights for prenatal care.
• Cancer Genomics: CNV Sequencing is widely used to identify somatic copy number alterations in cancer, including amplifications of oncogenes and deletions of tumor suppressor genes. This information is crucial for understanding tumorigenesis, prognosis, and treatment planning.
• Pharmacogenomics: CNV Sequencing can detect CNVs in drug-metabolizing genes, providing insights into drug efficacy, potential adverse reactions, and the tailoring of personalized treatment regimens.
• Infectious Diseases: It can be used to investigate microbial genome variations, particularly in studying antibiotic resistance or virulence factors.

CNV Sequencing Workflow

CD Genomics follows a rigorous Quality Control (QC) pipeline to ensure data integrity.

1

Sample QC: Purity and concentration verification.

2

Library Prep: Fragmentation and indexing.

3

Sequencing: High-throughput Illumina sequencing (PE150).

4

Bioinformatics: Data filtering, mapping, and variant calling.

Overview of the workflow for CNV sequencing services.

CNV Sequencing Bioinformatics Analysis

CD Genomics offers comprehensive and flexible bioinformatics analysis services, from basic data processing to advanced custom analyses. Our solutions help you deeply explore genomic variations and functions.

• CNV Detection:
  • Description: Read depth (RD) is pulled from the final BAM file and analyzed statistically across genomic regions (bins).
  • Purpose: To find areas with extra or missing DNA copies (CNVs).
  • Tools: Using CNVnator.
  • Output: A list of initial CNV calls.
• Result Filtering and Annotation:
  • Description: Initial CNV calls are filtered based on quality scores and tagged with genomic feature info.
  • Purpose: To make the results more reliable and give biological meaning.
  • Tools: Using AWK, BEDTools, AnnotSV
  • Output: A final, cleaned-up list of annotated CNVs.
• Validation & Visualization:
  • Description: Final CNV calls are checked for accuracy and biological sense.
  • Purpose: By looking at the supporting read depth data and genomic context visually.
  • Tools: Using IGV, CNVnator, R/ggplot2
  • Output: Validation plots and figures.

For custom bioinformatics analysis or specific research needs, please reach out to our experts. We provide professional advice and support tailored to your project.

Pipeline for bioinformatics analysis in whole genome sequencing and CNV detection.

Sample Requirements

• All DNA samples must undergo purity and concentration testing to ensure sequencing quality.
• If you have questions regarding sample preparation or require a custom plan, feel free to contact us anytime for expert assistance.

Why Choose CD Genomics for CNV Sequencing?

From high-sensitivity detection to clinically actionable insights, CD Genomics delivers precise, end-to-end CNV sequencing solutions powered by optimized low-pass WGS and advanced bioinformatics. Whether you're investigating neurodevelopmental disorders or profiling cancer genomics, our team ensures reliable, publication-ready data with dedicated scientific consultation.

• Precise Interpretation: In-depth analysis guided by interpretation protocols, comprehensive case management system, powerful NGS annotation/interpretation engine with proprietary databases, customizable reports.
• Efficient Detection: Rapid turnaround, high efficiency, and accurate results.
• Granular Analysis: Capable of detecting microdeletions/microduplications ≥50kb/100kb and aneuploidies.
• Premium Service: Professional pre-sales, during-project, and post-sales support, plus personalized data mining for research.
• Competitive Pricing & Scalability: Cost-effective solutions for projects of all sizes.
• Dedicated Support: Personalized project management and scientific consultation.

References:

1. Xie, C., Tammi, M.T. CNV-seq: a new method to detect copy number variation using high-throughput sequencing. BMC Bioinformatics 10, 80 (2009). https://doi.org/10.1186/1471-2105-10-80
2. Zhou, X., Chen, Y., Li, J. et al. Whole-genome sequencing analysis of CNV using low-coverage and paired-end strategies is efficient and outperforms array-based CNV analysis. J Med Genet 55, 735–743 (2018). https://doi.org/10.1136/jmedgenet-2018-105272
3. Abyzov, A., Urban, A.E., Snyder, M. et al. CNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res 21, 974–984 (2011). https://doi.org/10.1101/gr.114876.110
4. Pirooznia, M., Goes, F.S. & Zandi, P.P. Whole-genome CNV analysis: advances in computational approaches. Front Genet 6, 138 (2015).
5. Chen, Y., Han, X., et al. Copy number variation sequencing for the products of conception: What is the optimal testing strategy. Clinica Chemical Acta 557, 117884 (2024). https://doi.org/10.1016/j.cca.2024.117884

Demo Results

Partial results are shown below:

Partial analysis output showing Copy Ratio (log2) variations across chromosomes.

CNV Seq FAQs

1. How does CNV-seq detect copy number variations in genomes using next-generation sequencing technologies?

CNV-seq detects CNVs using next-generation sequencing (NGS) through the following steps:

a. DNA Fragmentation: The genome is fragmented into smaller pieces, and short DNA reads are generated through high-throughput sequencing technologies.

b. Read Mapping: The generated reads are aligned to a reference genome and read depth (coverage) at each genomic position is calculated.

c. Sliding Window Analysis: CNVs are identified by comparing the read depth in sliding windows across the genome. Variations in the number of reads in specific regions suggest the presence of deletions (lower coverage) or duplications (higher coverage).

d. Statistical Modeling: A statistical model assesses the significance of observed variations, adjusting for biases such as sequencing errors, and calculates the likelihood that these variations represent true CNVs rather than random fluctuations.

2. What are the limitations of using CNV-seq for detecting copy number variations?

a. Coverage-Dependent Sensitivity: The accuracy of CNV detection is dependent on sequencing depth. Low coverage can lead to false negatives, particularly for smaller CNVs.

b. Computational Complexity: CNV-seq involves complex bioinformatics pipelines for data analysis, which require significant computational resources and expertise in bioinformatics tools.

c. Potential for False Positives: Sequencing errors, mapping biases, and uneven coverage can lead to false positives, especially in regions with high repetitive sequences or low sequence complexity.

3. How can CNV-seq be used to identify genetic variations associated with complex diseases such as cancer and autism?

CNV-seq can be used to identify genetic variations linked to complex diseases in the following ways:

a. Cancer Genomics: CNV-seq is invaluable for identifying somatic CNVs in cancer genomes. These CNVs can reveal critical oncogenes and tumor suppressor genes involved in cancer development, metastasis, and response to treatment, providing essential information for targeted therapies and prognostic assessments.

b. Neurodevelopmental Disorders: CNV-seq helps detect CNVs that affect neurodevelopmental genes, which are often implicated in diseases such as autism, intellectual disability, and schizophrenia. Identifying these CNVs aids in understanding the genetic architecture of these disorders and facilitates early diagnosis.

c. Disease Pathogenesis: By comparing CNV profiles between healthy and diseased individuals, CNV-seq can identify genetic variations that contribute to disease susceptibility, progression, and response to treatments, making it a powerful tool for biomarker discovery.

d. Precision Medicine: CNV-seq enables the identification of CNVs that influence an individual's response to specific treatments, allowing for the development of personalized medicine strategies.

CNV Seq Case Studies

Source: Ghorbani Tajani A, Sharma A, Blouin N & Bisha B (2024). Genome sequence, antibiotic resistance genes, and plasmids in a monophasic variant of Salmonella typhimurium isolated from retail pork. Microbiology Resource Announcements. DOI: https://doi.org/10.1128/mra.00754-23

1. Background

Antimicrobial resistance (AMR) in foodborne pathogens such as Salmonella typhimurium is a growing global public health concern. Accurate detection of resistance genes and mobile genetic elements like plasmids is critical for surveillance, outbreak tracking, and risk assessment in the food supply chain. In this case, researchers characterized a monophasic S. typhimurium isolate obtained from retail pork in Wyoming, USA, using high accuracy whole-genome sequencing.

2. Methods

• Sample Processing & Sequencing: The Salmonella isolate was subjected to Illumina NovaSeq 6000 sequencing to generate a high-coverage whole-genome dataset (~5.32 Mb).
• Bioinformatics Workflow: Raw reads were quality-controlled, assembled, and analyzed to identify antibiotic resistance genes (ARGs) and plasmid replicons using standard genomic pipelines.
• CD Genomics Advantage: Leveraging our certified low-pass WGS and advanced bioinformatics, we offer robust detection and annotation of ARGs and structural genomic elements across complex bacterial genomes.

3. Results

• The assembled genome had a total length of ~5,320,119 bp with 51.06 % GC.
• Multiple antibiotic resistance genes were detected, including blaTEM-1 and aac(6′)-IIc, which are associated with β-lactam and aminoglycoside resistance.
• Several plasmid replicons (e.g., IncHI2, p0111) were identified, indicating the potential for horizontal gene transfer of AMR determinants.

4. Conclusions

This case study demonstrates how high-resolution whole-genome sequencing empowers detection of clinically relevant resistance genes and plasmids in foodborne pathogens, providing actionable genomic insights for surveillance and risk mitigation. Compared with traditional genetic profiling methods, CD Genomics' comprehensive WGS workflow delivers higher sensitivity, broader genomic coverage, and detailed structural variant detection, enabling researchers and regulatory labs to understand AMR mechanisms more clearly and make data-driven decisions.

Key Benefits Highlighted:

✔ Whole-genome resolution enables fine-scale detection of ARGs and plasmids.

✔ High-throughput sequencing ensures rapid turnaround time.

✔ our advanced bioinformatics supports confident annotation and interpretation of structural variation.