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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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DNA replication is a well-evolved process that copies millions of base pairs with high fidelity during each cell division. Occasionally a wrong base or a long stretch of wrong bases may get added to the daughter strands. If the errors are left unchecked, cells might accumulate several mutations that might endanger their  survival. Therefore, the copying errors are checked and repaired at three levels.
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Related Experiment Video

Updated: Dec 15, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
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Copy number evolution with weighted aberrations in cancer.

Ron Zeira1, Benjamin J Raphael1

  • 1Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.

Bioinformatics (Oxford, England)
|July 14, 2020
PubMed
Summary

This study introduces a weighted copy number distance (CND) model to accurately track cancer evolution by accounting for varying copy number aberration (CNA) event probabilities, improving phylogenetic reconstruction and CNA rate estimation.

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Area of Science:

  • Genomics
  • Cancer Biology
  • Computational Biology

Background:

  • Copy number aberrations (CNAs) are common somatic mutations in cancer, involving genome deletions or amplifications.
  • Modeling cancer's copy number evolution is challenging due to overlapping CNAs.
  • The standard copy number distance (CND) model assumes all CNA events have equal weight.

Purpose of the Study:

  • To develop a weighted CND model that incorporates varying probabilities for CNA events.
  • To improve the accuracy of phylogenetic reconstruction in cancer.
  • To enable better estimation of CNA rates from genomic data.

Main Methods:

  • Introduced a weighted CND model accounting for CNA event length, position, and type.
  • Developed an efficient algorithm utilizing the totally unimodular property of the constraint matrix.
  • Applied the model to simulated data, ultra-low-coverage single-cell DNA sequencing data, and a pan-cancer dataset.

Main Results:

  • The weighted CND model demonstrated improved phylogenetic reconstruction accuracy with probabilistic CNAs.
  • Successfully derived phylogenies from challenging ultra-low-coverage single-cell DNA sequencing data.
  • Enabled accurate estimation of CNA rates across a large pan-cancer dataset.

Conclusions:

  • The weighted CND offers a more realistic approach to modeling cancer copy number evolution.
  • This method enhances the analysis of genomic data for cancer research and clinical applications.
  • The developed algorithm and code are publicly available for further research.