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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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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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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.
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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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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.
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Group-based variant calling leveraging next-generation supercomputing for large-scale whole-genome sequencing

Kristopher A Standish1,2, Tristan M Carland3, Glenn K Lockwood4

  • 1Biomedical Sciences Graduate Program, University of California, San Diego, Gilman Drive, La Jolla, 92092, CA, USA. kstandis@ucsd.edu.

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This study optimized group-based variant calling for whole human genomes, achieving high-quality results efficiently. The findings highlight the need for integrated hardware and algorithmic solutions for big data in genomics.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) enables rapid, cost-effective whole human genome sequencing.
  • Accurate variant calling from NGS data is crucial for understanding phenotypic consequences.
  • Computational cost and varying approaches can impact variant calling accuracy.

Purpose of the Study:

  • To evaluate a group-based variant calling approach for large-scale whole human genome datasets.
  • To identify factors influencing the accuracy and efficiency of group-based variant calling.
  • To optimize computational workflows for processing extensive genomic data.

Main Methods:

  • Implemented and assessed a group-based variant calling strategy on 437 whole human genomes.
  • Investigated the impact of group size, biogeographical diversity, and computing environment.
  • Utilized parallelization and job-packing on the Gordon supercomputer for computational efficiency.

Main Results:

  • The developed workflow achieved high-quality variant calls.
  • The approach proved computationally efficient for large datasets.
  • Group size and biogeographical factors influenced variant calling outcomes.

Conclusions:

  • The group-based variant calling workflow is effective and efficient.
  • Further research should integrate computational hardware and algorithmic advancements.
  • Addressing 'big data' challenges in genomics is essential for future biomedical research.