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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.
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Maxam-Gilbert Sequencing01:05

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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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Next-generation sequencing and the evolution of data sharing.

Nara Lygia de Macena Sobreira1, Ada Hamosh1

  • 1McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

American Journal of Medical Genetics. Part A
|May 7, 2021
PubMed
Summary
This summary is machine-generated.

Sharing phenotypic and genomic data accelerates rare disease gene discovery. Evolving data sharing methods provide faster, comprehensive answers for patients and researchers.

Keywords:
GeneMatcherVariantMatcherdata sharingmodel organismswhole exome sequencing

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Area of Science:

  • Genomics
  • Rare Diseases
  • Bioinformatics

Background:

  • Disease gene identification typically requires multiple affected individuals with shared phenotypes and candidate gene variants.
  • Phenotypic and genomic data sharing platforms have enabled crucial connections for discovering new disease genes and improving rare disease recognition.

Purpose of the Study:

  • To highlight the evolution and impact of data sharing in disease gene identification.
  • To emphasize the benefits of data sharing for rare disease research and patient care.

Main Methods:

  • Review of data sharing initiatives and their progression.
  • Analysis of the shift from gene-based to variant-level and phenotypic data sharing.

Main Results:

  • Data sharing has successfully facilitated novel disease gene discoveries.
  • Enhanced characterization and recognition of rare diseases have been achieved through collaborative data efforts.
  • The trend shows an increasing integration of phenotypic information with genomic data.

Conclusions:

  • Continued expansion of data sharing initiatives is anticipated.
  • These advancements promise faster and more comprehensive diagnostic answers for clinicians, researchers, and patients.
  • Collaborative data sharing is essential for advancing rare disease research and improving patient outcomes.