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Related Concept Videos

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.
Sanger Sequencing01:57

Sanger Sequencing

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...
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.
Challenges of the Maxam-Gilbert Method
The...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: Jun 16, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Beyond the HapMap Genotypic Data: Prospects of Deep Resequencing Projects.

Wei Zhang1, M Eileen Dolan

  • 1Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA.

Current Bioinformatics
|September 28, 2011
PubMed
Summary

The International HapMap Project offers valuable human genetic data, but may miss rare variants. Deep resequencing projects like ENCODE and NIEHS aim to enhance this resource for future genetic association studies.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

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Last Updated: Jun 16, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genomics and Human Genetics
  • Population Genetics
  • Bioinformatics

Background:

  • The International HapMap Project provides a crucial genotypic dataset from diverse human populations.
  • This resource has been instrumental in identifying genetic variations linked to diseases, drug responses, and gene expression.

Purpose of the Study:

  • To address the limitations of the HapMap Project in capturing rare or untyped single nucleotide polymorphisms (SNPs).
  • To discuss complementary large-scale deep resequencing projects that can augment the HapMap resource.

Main Methods:

  • Review and discussion of three major deep resequencing initiatives: ENCODE, SeattleSNPs, and the NIEHS Environmental Genome Project.
  • Focus on projects that cover HapMap samples to enable integration.

Main Results:

  • Comparative studies indicate that HapMap data alone may not sufficiently represent the full spectrum of genetic variation, particularly rare SNPs.
  • Deep resequencing projects offer a more comprehensive view of genetic diversity within the studied populations.

Conclusions:

  • Integrating data from deep resequencing projects with the HapMap resource will significantly enhance future association studies.
  • This integrated approach will improve the identification of genetic factors influencing human phenotypes and disease risk.