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Related Concept Videos

Next-generation Sequencing03:00

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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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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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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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VikNGS: a C++ variant integration kit for next generation sequencing association analysis.

Zeynep Baskurt1,2, Scott Mastromatteo1,2, Jiafen Gong1,2

  • 1Program in Genetics and Genome Biology, Research Institute, The Hospital for Sick Children, Toronto, ON M5G0A4, Canada.

Bioinformatics (Oxford, England)
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Summary

Integrating next-generation sequencing (NGS) data enhances genetic association studies. The Variant Integration Kit for NGS (VikNGS) software aggregates diverse datasets, improving power while accounting for genotype uncertainty.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Integrating next-generation sequencing (NGS) data across studies increases sample size for genetic association testing.
  • Differential genotype uncertainty between studies can lead to spurious association results if not addressed.

Purpose of the Study:

  • To develop a software package for aggregating multiple NGS datasets for genetic association analysis.
  • To enable robust analysis of rare and common variants for quantitative and binary traits, including covariate adjustment.

Main Methods:

  • Developed the Variant Integration Kit for NGS (VikNGS), a fast, cross-platform software package.
  • VikNGS facilitates the aggregation of diverse genetic datasets.
  • The package includes a graphical user interface, power simulation, and data visualization tools.

Main Results:

  • VikNGS enables the aggregation of multiple datasets for genetic association analysis.
  • The software accounts for differential genotype uncertainty across studies.
  • Facilitates analysis of rare and common variants for various traits with covariate adjustment.

Conclusions:

  • VikNGS provides a robust solution for integrating NGS data from multiple studies.
  • The software improves the power of genetic association testing while mitigating spurious results.
  • Offers a comprehensive tool for researchers analyzing large-scale genetic datasets.