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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%...

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Updated: Dec 20, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

SCOPE: A Normalization and Copy-Number Estimation Method for Single-Cell DNA Sequencing.

Rujin Wang¹, Dan-Yu Lin², Yuchao Jiang³

¹Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA.

|May 22, 2020

Summary

This summary is machine-generated.

SCOPE is a new method for analyzing noisy whole-genome single-cell DNA sequencing data. It accurately estimates copy-number profiles and reconstructs cancer subclonal structures.

Keywords:

cancer genomics copy number aberration copy number variation normalization single-cell DNA sequencing tumor heterogeneity

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Whole-genome single-cell DNA sequencing (scDNA-seq) provides cellular-level copy-number profiling.
scDNA-seq data is often noisy, posing challenges for accurate analysis.

Purpose of the Study:

To develop and evaluate SCOPE, a novel method for normalizing and estimating copy-number profiles from noisy scDNA-seq data.
To accurately reconstruct subclonal structures in cancer genomics using scDNA-seq data.

Main Methods:

SCOPE utilizes a Poisson latent factor model for normalization, incorporating negative control cells to estimate bias.
An expectation-maximization algorithm is integrated for direct ploidy estimation, accounting for copy-number aberrations.
A cross-sample segmentation procedure identifies shared breakpoints across cells within the same genetic background.

Main Results:

SCOPE demonstrates accurate copy-number estimates on diverse scDNA-seq datasets.
The method successfully reconstructs subclonal architectures in cancer genomics.
SCOPE provides a robust approach for analyzing complex single-cell genomic data.

Conclusions:

SCOPE offers a significant advancement in analyzing noisy scDNA-seq data.
The method enables precise characterization of cellular copy-number profiles and subclonal heterogeneity.
SCOPE is a valuable tool for cancer genomics research and potentially other fields utilizing scDNA-seq.