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Related Concept Videos

Genome Copying Errors02:46

Genome Copying Errors

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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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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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Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
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Updated: May 15, 2025

Detection of Copy Number Alterations Using Single Cell Sequencing
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CNRein: an evolution-aware deep reinforcement learning algorithm for single-cell DNA copy number calling.

Stefan Ivanovic1, Mohammed El-Kebir2,3

  • 1Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, 61801, USA.

Genome Biology
|April 8, 2025
PubMed
Summary
This summary is machine-generated.

We developed CNRein, an AI tool that accurately identifies copy number alterations (CNAs) in single cells. This method improves tumor evolution analysis by reducing errors and providing more reliable genetic profiles.

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Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Advancements in low-pass single-cell DNA sequencing allow for copy number profiling in thousands of tumor cells.
  • Measurement uncertainties in sequencing data can lead to inaccurate copy number alterations (CNAs), contradicting tumor evolutionary patterns.

Purpose of the Study:

  • To introduce CNRein, an evolution-aware computational method for accurate copy number calling in single-cell sequencing data.
  • To improve the recapitulation of true clonal structures and evolutionary trajectories in tumors.

Main Methods:

  • Developed CNRein, a deep reinforcement learning algorithm for copy number calling.
  • Validated CNRein using simulated datasets to compare its performance against existing methods.
  • Applied CNRein to real-world sequencing data from breast and ovarian cancers.

Main Results:

  • CNRein demonstrated superior accuracy in inferring copy-number profiles and reconstructing clonal structures compared to current methods in simulations.
  • CNRein generated more parsimonious genomic solutions on cancer sequencing data, consistent with single-nucleotide variant data.
  • The method showed consistent performance across different low-pass sequencing technologies for breast cancer samples.

Conclusions:

  • CNRein offers a robust and accurate approach to copy number calling in single-cell genomics.
  • This evolution-aware method enhances the reliability of tumor evolutionary analysis and genomic profiling.