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Related Concept Videos

Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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DNA isolation protocols can be fast and straightforward or complex and time-consuming depending on the type and quality of DNA required for further processing. For example, plasmid DNA extraction is a bit more complicated than genomic DNA extraction because of the need for an appropriate lysis method to separate plasmid DNA from gDNA during isolation. However, for specific applications, such as long-range DNA sequencing that require a good yield of high- quality DNA samples, we need to follow...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

Updated: Jun 3, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Compressing genomic sequence fragments using SlimGene.

Christos Kozanitis1, Chris Saunders, Semyon Kruglyak

  • 1Department of Computer Science and Engineering, University of California, San Diego, California, USA. ckozanit@cs.ucsd.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|March 10, 2011
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing generates massive data. New compression methods for genomic fragments, including lossy quality values, significantly reduce data size with minimal impact on downstream analysis like SNP calling.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Compression

Background:

  • Next-generation sequencing (NGS) costs are decreasing, leading to a surge in human genome data.
  • Storing and analyzing vast amounts of sequencing data presents significant challenges.
  • Fragment-level compression is crucial for managing large genomic datasets.

Purpose of the Study:

  • To develop efficient compression schemes for genomic sequencing fragments.
  • To investigate the impact of lossy compression on quality values for downstream applications.
  • To introduce the SlimGene tool for fragment-level data compression.

Main Methods:

  • Developed domain-specific lossless compression algorithms for genomic fragments.
  • Implemented and evaluated compression performance against bzip2.
  • Investigated lossy quality value quantization for enhanced compression.
  • Assessed the impact of lossy compression on Single Nucleotide Polymorphism (SNP) calling.

Main Results:

  • Achieved over 40x lossless compression for fragments, outperforming bzip2 by 6x.
  • Attained 5x compression including quality values, with reduced running time compared to bzip2.
  • Demonstrated 14x compression using lossy quality value quantization.
  • Showed minimal impact of lossy compression on SNP calling, especially in high-coverage areas.

Conclusions:

  • Novel lossless compression schemes offer significant data reduction for genomic fragments.
  • Lossy compression of quality values provides substantial gains with negligible impact on critical downstream analyses.
  • The SlimGene tool effectively addresses the need for efficient storage and transfer of large-scale sequencing data.