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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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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.
Next-Generation Sequencing Methods
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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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NGS for Sequence Variants.

Shaolei Teng1

  • 1Department of Biology, Howard University, Washington, DC, 20059, USA. shaolei.teng@howard.edu.

Advances in Experimental Medicine and Biology
|November 4, 2016
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing (NGS) enables personal genome sequencing and variant detection, revolutionizing precision medicine. This chapter details NGS methods for identifying and analyzing disease-causing variants in complex diseases.

Keywords:
Association testingNext-generation sequencingPrecision medicine informaticsSequence alignmentSequence variantsVariant callingVisualization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Precision Medicine

Background:

  • Next-generation sequencing (NGS) technologies offer powerful capabilities for personal genome sequencing and genomic landscape characterization.
  • The identification of disease-causing variants is pivotal for advancing precision medicine.
  • Understanding sequence variants is crucial for diagnosing and treating complex diseases.

Purpose of the Study:

  • To provide a comprehensive overview of sequence variant detection and analysis within NGS studies.
  • To outline general methodologies for identifying diverse sequence variants from NGS data.
  • To summarize common approaches for analyzing and visualizing causal variants in the context of precision medicine informatics.

Main Methods:

  • Utilizing NGS data for high-throughput sequencing of genomes.
  • Employing bioinformatics pipelines for variant calling and annotation.
  • Applying statistical and visualization tools for analyzing sequence variants associated with complex diseases.

Main Results:

  • Demonstration of effective methods for detecting various sequence variants (e.g., SNPs, indels, structural variants) using NGS.
  • Presentation of analytical frameworks for interpreting the clinical significance of identified variants.
  • Highlighting the utility of variant analysis in precision medicine informatics for patient stratification and treatment selection.

Conclusions:

  • NGS-based variant detection and analysis are fundamental to modern precision medicine.
  • Effective informatics strategies are essential for translating genomic discoveries into clinical applications.
  • This chapter serves as a guide to understanding and applying NGS variant analysis for complex disease research.