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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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Related Experiment Video

Updated: Feb 25, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

ReMixT: clone-specific genomic structure estimation in cancer.

Andrew W McPherson^1,2, Andrew Roth^3,4, Gavin Ha^5,6

¹Department of Molecular Oncology, BC Cancer Agency, 675 West 10th Avenue, Vancouver, BC, Canada.

|July 29, 2017

Summary

This summary is machine-generated.

This study introduces ReMixT, a new computational method to accurately analyze complex tumor genomes. ReMixT effectively separates cancer cell signals from normal cells, improving the understanding of tumor evolution and genetic alterations.

Keywords:

Cancer genomics Copy number variation DNA sequencing Genomic rearrangement Tumour heterogeneity

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Area of Science:

Genomics
Computational Biology
Cancer Research

Background:

Tumors arise from somatic evolution, leading to multiple malignant cell clones.
These clones exhibit distinct chromosomal rearrangements and copy number variations.
Whole genome sequencing can obscure clone-specific genetic signals due to signal mixing.

Purpose of the Study:

To develop a computational method for deconvoluting mixed genomic signals in tumors.
To accurately estimate clone-specific genotypes and chromosomal aberrations.
To improve the analysis of tumor heterogeneity and evolution.

Main Methods:

Introduction of ReMixT, a novel computational tool.
ReMixT unmixes tumor and normal cellular signals.
Joint prediction of mixture proportions, clone-specific copy number, and breakpoint locations.

Main Results:

ReMixT successfully separates mixed signals from tumor and normal cells.
The method accurately estimates clone-specific genomic alterations.
Identification of clone-specific breakpoints is improved.

Conclusions:

ReMixT provides a robust solution for analyzing complex tumor genomes.
The software enhances the understanding of clonal evolution in cancer.
ReMixT is a free, open-source tool available for the research community.