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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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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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Viral genomes exhibit remarkable diversity in size, structure, and composition, influencing their replication strategies and interactions with host cells. These genomes consist of either DNA or RNA and may be linear or circular. Additionally, they can be single-stranded or double-stranded, with each configuration affecting how the virus propagates within a host. RNA viruses, for instance, generally have smaller genomes than DNA viruses, a factor that contributes to their high mutation rates and...
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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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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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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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CodingDiv: analyzing SNP-level microdiversity to discriminate between coding and noncoding regions in viral genomes.

Eric Olo Ndela1, François Enault1

  • 1Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France.

Bioinformatics (Oxford, England)
|July 14, 2023
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Summary
This summary is machine-generated.

Predicting viral genes is challenging. CodingDiv identifies protein coding regions by detecting SNP-level microdiversity in potential coding regions, aiding accurate viral gene prediction.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Virology

Background:

  • Viral gene prediction is complex due to small gene sizes and overlaps.
  • Accurate identification of all viral genes remains a significant challenge in genomics.

Purpose of the Study:

  • To introduce CodingDiv, a novel tool for predicting viral genes.
  • To detect Single Nucleotide Polymorphism (SNP)-level microdiversity in potential coding regions of viral genomes.

Main Methods:

  • CodingDiv utilizes metagenomic reads and external sequence databases.
  • It analyzes SNP patterns, distinguishing synonymous from nonsynonymous substitutions.
  • Protein coding regions are identified by a higher ratio of synonymous to nonsynonymous SNPs.

Main Results:

  • CodingDiv effectively detects microdiversity at the SNP level.
  • The tool facilitates the identification of protein coding regions within viral genomes.
  • This method improves the accuracy of viral gene prediction.

Conclusions:

  • CodingDiv offers a robust solution for the accurate prediction of viral genes.
  • The tool leverages SNP microdiversity analysis for enhanced gene identification.
  • This approach contributes to a better understanding of viral genome organization.