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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Related Experiment Video

Updated: May 29, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

Efficient storage of high throughput DNA sequencing data using reference-based compression.

Markus Hsi-Yang Fritz1, Rasko Leinonen, Guy Cochrane

  • 1European Molecular Biology Laboratory's European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom.

Genome Research
|January 20, 2011
PubMed
Summary
This summary is machine-generated.

New DNA sequencing data storage methods offer significant cost savings. A novel reference-based compression technique efficiently stores DNA sequences by encoding differences from a reference genome, addressing growing data volumes.

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Ultra-long Read Sequencing for Whole Genomic DNA Analysis
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Last Updated: May 29, 2026

Methyl-binding DNA capture Sequencing for Patient Tissues
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Published on: October 31, 2016

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome
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G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Published on: March 22, 2018

Ultra-long Read Sequencing for Whole Genomic DNA Analysis
10:34

Ultra-long Read Sequencing for Whole Genomic DNA Analysis

Published on: March 15, 2019

Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • DNA sequencing generates vast amounts of data, leading to escalating storage costs.
  • The rapid increase in DNA sequencing output outpaces advancements in disk storage capacity.

Purpose of the Study:

  • To introduce a novel reference-based compression method for efficient DNA sequence data storage.
  • To address the challenge of managing exponentially growing DNA sequence data volumes.

Main Methods:

  • Developed a reference-based compression algorithm for resequencing experiments targeting well-studied genomes.
  • Aligned new DNA sequences to a reference genome and encoded the differences for storage.
  • Incorporated controlled data loss for quality information and unaligned sequences to enhance compression efficiency.

Main Results:

  • Observed exponential efficiency gains in compression as read lengths increased.
  • Demonstrated that the magnitude of compression efficiency is tunable by adjusting stored quality information.
  • Showcased the method's effectiveness in reducing storage requirements for DNA sequence data.

Conclusions:

  • The proposed compression method offers a tunable solution for managing large-scale DNA sequence data.
  • Controlled loss of quality scores and unaligned sequences can significantly minimize storage costs.
  • This approach provides a viable strategy to mitigate the storage challenges posed by increasing DNA sequencing volumes.