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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.
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Related Experiment Video

Updated: Dec 24, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Comparing copy-number profiles under multi-copy amplifications and deletions.

Garance Cordonnier¹, Manuel Lafond²

¹Department of Computer Science, École polytechnique, Paris, France.

|April 18, 2020

Summary

We developed a new method to calculate the evolutionary distance between cancer cells based on their copy-number profiles (CNPs). Our approach offers more accurate cancer phylogenies than existing methods when data is clean.

Keywords:

Algorithms Cancer phylogenies Copy-number evolution NP-hardness

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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
Cancer Biology
Computational Biology

Background:

Cancer cells accumulate somatic mutations and genetic aberrations during progression.
Segmental duplications or deletions alter the copy-number profile (CNP) of genes.
Understanding evolutionary distance between cells based on CNPs is crucial.

Purpose of the Study:

To compute the evolutionary distance between cells using only their CNPs.
To develop a generalized cost framework for copy-number alteration events.
To address the NP-hard nature of computing distances with arbitrary segmental deletions.

Main Methods:

Proposed a general cost framework for copy-number alteration events.
Developed a factor 2 approximation algorithm for computing CNP distance.
Implemented the algorithm in a tool called cnp2cnp.
Evaluated cnp2cnp by reconstructing simulated cancer phylogenies.

Main Results:

Computing CNP distance is NP-hard with arbitrary segmental deletions.
cnp2cnp provides a factor 2 approximation algorithm for non-zero copy-numbers.
Experimental results show cnp2cnp yields more accurate phylogenies on error-free CNPs.
MEDICC distance shows slightly better robustness against data errors.

Conclusions:

The proposed distance metric improves cancer phylogeny reconstruction accuracy with error-free CNPs.
MEDICC distance is more robust to noisy CNP data.
Both cnp2cnp and MEDICC are superior to Euclidean distance for CNP-based phylogeny.