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Related Concept Videos

Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

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The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
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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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Wald-Wolfowitz Runs Test I01:17

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The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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Running Genomic Analyses in the Cloud.

Conrad Leonard1, Scott Wood1, Oliver Holmes1

  • 1QIMR Berghofer Medical Research Institute, Brisbane, Australia.

Studies in Health Technology and Informatics
|August 10, 2019
PubMed
Summary
This summary is machine-generated.

Genomic testing presents significant data and privacy challenges for healthcare. Cloud computing offers a scalable solution to manage large genomic datasets and complex analyses, aiding healthcare integration.

Keywords:
Genomicscloud computingnext-generation sequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Health Informatics

Background:

  • Genomic testing is increasingly adopted in healthcare.
  • Significant informatics challenges include massive data volumes (hundreds of gigabytes per test) and high computational demands (thousands of CPU hours).
  • Genomic data privacy and security are critical concerns due to potential implications for individuals and their relatives.

Purpose of the Study:

  • To address the informatics challenges associated with genomic testing in healthcare.
  • To explore the utility of cloud computing for genomic data analysis.
  • To present lessons learned from deploying genomic analyses on a cloud platform for Queensland Genomics.

Main Methods:

  • Deployment of genomic analysis pipelines on a cloud computing environment.
  • Management of large-scale genomic datasets within the cloud infrastructure.
  • Implementation of security and privacy measures for sensitive health information.

Main Results:

  • Cloud computing demonstrated scalability to handle large genomic datasets.
  • The cloud platform provided the necessary computational resources for complex genomic analyses.
  • Key lessons were identified regarding the practical implementation of cloud-based genomic services.

Conclusions:

  • Cloud computing is a viable and beneficial solution for overcoming the informatics hurdles of genomic testing.
  • Successful deployment requires careful consideration of data management, computational resources, and privacy.
  • The findings provide valuable insights for healthcare systems integrating genomic data analysis.