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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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Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

epiAneufinder identifies copy number alterations from single-cell ATAC-seq data.

Akshaya Ramakrishnan¹, Aikaterini Symeonidi^2,3, Patrick Hanel^1,4

¹Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Nature Communications

|September 20, 2023

Summary

EpiAneufinder analyzes single-cell ATAC-seq data to detect copy number alterations (CNAs) in individual cells. This method reveals intratumor heterogeneity and clonal evolution without extra experiments.

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Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Single-cell ATAC-seq (scATAC-seq) is a key technology for mapping open chromatin regions in individual cells.
Understanding copy number alterations (CNAs) is crucial for cancer research and understanding genomic instability.
Existing methods often require separate experiments to profile CNAs at the single-cell level.

Purpose of the Study:

To introduce epiAneufinder, a novel algorithm for inferring genome-wide CNAs from scATAC-seq data.
To enable the study of CNA heterogeneity within cell populations at single-cell resolution.
To provide a method for exploring genomic variation without additional experimental costs.

Main Methods:

Developed epiAneufinder, an algorithm leveraging scATAC-seq read count data.
Applied epiAneufinder to various cancer scATAC-seq datasets.
Validated CNA profiles against single-cell whole genome sequencing data.

Main Results:

EpiAneufinder successfully identified intratumor clonal heterogeneity in single cells based on CNA profiles.
The CNA profiles inferred by epiAneufinder were highly concordant with those from single-cell whole genome sequencing.
Demonstrated the capability of scATAC-seq data to yield CNA information.

Conclusions:

EpiAneufinder effectively extracts single-cell CNA information from scATAC-seq data.
This approach allows for the study of previously inaccessible genomic variation.
Facilitates deeper insights into cancer evolution and heterogeneity using existing scATAC-seq datasets.