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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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-GWAS01:11

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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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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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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Related Experiment Video

Updated: Jul 8, 2025

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DATA RESOURCES AND ANALYSES FAIR Header Reference genome: A TRUSTworthy standard.

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  • 1Adaptive Oncology Program, Ontario Institute for Cancer Research, 661 University Avenue Suite 500, Toronto, ON M5G 0A3, Canada.

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The FAIR-bioHeaders Reference genome (FHR) standard addresses data sharing challenges in genomics. It enhances data interoperability and provenance tracking across platforms with minimal implementation effort.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Data Science

Background:

  • Lack of interoperable data standards hinders cross-platform analysis in reference genome data sharing.
  • This fragmentation leads to increased risk of data provenance loss.
  • Decentralized genomic data ecosystems require robust metadata solutions.

Approach:

  • Introduced the FAIR-bioHeaders Reference genome (FHR) metadata standard.
  • FHR is guided by Findability, Accessibility, Interoperability, and Reuse (FAIR) and TRUST principles.
  • Designed for extensibility, flexibility, and expressivity in genomic data management.

Key Points:

  • FHR provides a comprehensive set of data serialization methods and minimum data field requirements.
  • Implementation effort for FHR is designed to be low.
  • Ensures easy implementation while preserving machine and human-readable data provenance.

Conclusions:

  • FHR promotes seamless cross-platform analysis and robust data provenance.
  • Facilitates a more connected and reliable genomic data ecosystem.
  • Aimed at improving data sharing and reuse in bioinformatics research.