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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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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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Comparing Copy Number Variations and SNPs02:26

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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.
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RNA-seq03:21

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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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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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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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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Structural variation detection using next-generation sequencing data: A comparative technical review.

Peiyong Guan1, Wing-Kin Sung2

  • 1School of Computing, National University of Singapore, 117543, Singapore.

Methods (San Diego, Calif.)
|February 5, 2016
PubMed
Summary
This summary is machine-generated.

Accurately detecting structural variations (SVs) in genomes is vital for understanding disease. This review details SV calling methods, highlighting how library properties like insert size impact detection sensitivity and accuracy.

Keywords:
Next-generation sequencingStructural variation

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Structural variations (SVs) are large genomic mutations impacting phenotypes, health, and disease.
  • Next-generation sequencing (NGS) enables base-pair resolution SV detection, but methods vary in accuracy.
  • Inconsistent predictions and challenges in complex regions necessitate careful SV caller selection.

Purpose of the Study:

  • To review existing structural variation calling methods and pipelines.
  • To analyze factors influencing SV detection sensitivity and specificity.
  • To guide researchers in selecting appropriate SV callers for biological studies.

Main Methods:

  • Review of current next-generation sequencing-based structural variation detection techniques.
  • Analysis of SV caller performance based on SV and library properties.
  • Simulation studies to evaluate the impact of library insert size on SV detection sensitivity.

Main Results:

  • SV detection methods exhibit variable sensitivity and specificity due to differing features and data noise.
  • Genome complexity, such as repeat regions, and sequencing errors affect SV calling accuracy.
  • Library insert size significantly influences the sensitivity of various SV callers.

Conclusions:

  • Understanding SV caller methodologies and limitations is crucial for accurate genomic profiling.
  • Library properties are key determinants of SV detection performance.
  • Informed selection of SV callers enhances the reliability of genomic studies and disease research.