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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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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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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Next-generation Sequencing03:00

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Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Combinatorial algorithms for structural variation detection in high-throughput sequenced genomes.

Fereydoun Hormozdiari1, Can Alkan, Evan E Eichler

  • 1School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6.

Genome Research
|May 19, 2009
PubMed
Summary
This summary is machine-generated.

Detecting large structural variants (SVs) in the human genome is crucial for understanding diseases. New algorithms are presented to accurately identify these genomic variations using next-generation sequencing (NGS) data.

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Last Updated: Jun 23, 2026

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Published on: February 3, 2023

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10:36

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Published on: June 23, 2012

Area of Science:

  • Genomics
  • Bioinformatics
  • Human Genetics

Background:

  • Human genome sequencing reveals common structural variants (SVs) beyond single nucleotide polymorphisms and indels.
  • Previous methods for SV detection were limited by traditional sequencing technologies and small sample sizes.
  • The advent of next-generation sequencing (NGS) enables large-scale genomic variation analysis, including in disease contexts.

Purpose of the Study:

  • To develop novel algorithms for accurate identification of large structural variants (>5 Kbp) from NGS data.
  • To address the challenges posed by short read lengths and error rates in NGS data for SV detection.
  • To provide a versatile computational framework applicable to various sequencing platforms.

Main Methods:

  • Formulation of combinatorial approaches for SV detection.
  • Development of efficient algorithms based on these formulations.
  • Application of algorithms to whole-genome shotgun sequencing data from individuals.

Main Results:

  • The developed algorithms are fast, reliable, and accurate for SV detection.
  • The methods are compatible with multiple NGS technologies (e.g., Illumina, 454, SOLiD) and traditional sequencing.
  • Successful identification of SVs in individual human genomes sequenced using Illumina technology.

Conclusions:

  • Efficient and reliable algorithms for structural variant detection from NGS data have been developed.
  • These algorithms facilitate comprehensive genomic variation analysis across diverse populations and disease studies.
  • The presented methods advance the field of human genomics by enabling robust SV identification.