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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

Single Nucleotide Polymorphisms-SNPs

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: Jun 3, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

HapCNV: A Comprehensive Framework for CNV Detection in Low-input DNA Sequencing Data.

Xuanxuan Yu¹, Fei Qin², Shiwei Liu³

¹Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.

Biorxiv : the Preprint Server for Biology

|January 7, 2025

Summary

This summary is machine-generated.

HapCNV is a new statistical framework for detecting copy number variants (CNVs) in single-cell sequencing data from haploid organisms. It improves accuracy by using a novel pseudo-reference method, outperforming existing approaches.

Keywords:

Copy number variation Haploid Low-input sequencing Pseudo-reference sequence Single-cell DNA sequencing

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Last Updated: Jun 3, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

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Published on: February 17, 2017

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

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Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Array Comparative Genomic Hybridization Array CGH for Detection of Genomic Copy Number Variants

Published on: February 21, 2015

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Copy number variants (CNVs) are crucial in understanding genetic disorders and disease susceptibility.
Low-input sequencing data, especially from single or few cells, presents challenges in CNV detection due to amplification biases.
Existing CNV detection methods are often designed for diploid genomes and may not perform optimally for haploid organisms.

Purpose of the Study:

To develop a comprehensive statistical framework for normalizing data and detecting CNVs in haploid single- or low-cell DNA sequencing data.
To address the limitations of existing CNV detection methods in handling haploid genomes and low-input sequencing data.
To introduce a novel pseudo-reference approach for unbiased reference selection in CNV analysis.

Main Methods:

Developed HapCNV, a statistical framework for data normalization and CNV detection.
Implemented a preliminary cell clustering method to construct genomic location-specific pseudo-references.
Utilized simulations and a real *P. falciparum* dataset to evaluate performance.

Main Results:

HapCNV demonstrated superior accuracy in CNV detection compared to existing methods, particularly for short CNVs.
The novel pseudo-reference approach effectively preserves common CNVs and reduces bias.
Validated superior performance in detecting known CNVs in *P. falciparum*.

Conclusions:

HapCNV offers a novel and effective approach for CNV detection in haploid low-input sequencing datasets.
The method shows promise for applications in both haploid and diploid organisms.
Facilitates advancements in understanding genetic variations in various organisms.