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Related Concept Videos

Genome Copying Errors02:46

Genome Copying Errors

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: May 14, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

BayesHammer: Bayesian clustering for error correction in single-cell sequencing.

Sergey I Nikolenko1, Anton I Korobeynikov, Max A Alekseyev

  • 1Algorithmic Biology Laboratory, Academic University, St, Petersburg, Russia. sergey@logic.pdmi.ras.ru

BMC Genomics
|February 2, 2013
PubMed
Summary
This summary is machine-generated.

We developed BAYESHAMMER, a novel tool for DNA sequencing error correction. It significantly improves accuracy and speed for both single-cell and multi-cell sequencing data, outperforming existing methods.

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Detection of Copy Number Alterations Using Single Cell Sequencing
09:45

Detection of Copy Number Alterations Using Single Cell Sequencing

Published on: February 17, 2017

Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Error correction in DNA sequencing is challenging, particularly for single-cell projects with uneven read coverage.
  • Existing tools often perform poorly on single-cell data, and current single-cell methods are overly simplistic.

Purpose of the Study:

  • To introduce BAYESHAMMER, a new error correction tool utilizing novel algorithms for improved sequencing data accuracy.
  • To demonstrate BAYESHAMMER's effectiveness on both single-cell and multi-cell sequencing data.

Main Methods:

  • Development of novel algorithms based on Hamming graphs and Bayesian subclustering.
  • Benchmarking BAYESHAMMER using k-mer counts and genome assembly with the SPADES assembler.

Main Results:

  • BAYESHAMMER shows improved performance over existing error correction tools for multi-cell sequencing data.
  • The tool demonstrates significantly faster processing speeds on real-world datasets.
  • Effectiveness validated through k-mer counts and genome assembly metrics.

Conclusions:

  • BAYESHAMMER offers a robust and efficient solution for DNA sequencing error correction.
  • The tool advances capabilities for both single-cell and multi-cell genomics research.