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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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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Fundamental Attribution Error01:14

Fundamental Attribution Error

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Analysis of error profiles in deep next-generation sequencing data.

Xiaotu Ma1, Ying Shao2, Liqing Tian2

  • 1Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA. Xiaotu.Ma@stjude.org.

Genome Biology
|March 15, 2019
PubMed
Summary

This study reveals that next-generation sequencing (NGS) errors can be significantly reduced computationally, enabling the detection of low-frequency genetic variants crucial for cancer diagnostics. Our findings pave the way for more accurate deep sequencing applications.

Keywords:
Deep sequencingDetectionError rateHotspot mutationSubclonalSubstitution

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Sequencing errors in deep next-generation sequencing (NGS) complicate the detection of low-frequency genetic variants essential for cancer diagnosis and surveillance.
  • A comprehensive understanding of errors introduced throughout the conventional NGS workflow is lacking.

Purpose of the Study:

  • To systematically investigate and characterize sequencing error sources in conventional NGS workflows.
  • To quantify the impact of sample handling, library preparation, PCR enrichment, and sequencing on error rates.

Main Methods:

  • Utilized current NGS technology and multiple deep sequencing datasets.
  • Evaluated read-specific error distributions to identify and quantify substitution errors.
  • Analyzed error profiles based on nucleotide substitution types and sequence context.

Main Results:

  • Substitution error rates can be computationally suppressed to 10-5–10-4, a 10- to 100-fold improvement over current literature.
  • Identified varying error rates by substitution type, with C>T/G>A errors showing sequence context dependency.
  • Target-enrichment PCR increased the overall error rate approximately 6-fold; sample-specific effects were noted for C>A/G>T errors.
  • Demonstrated the potential to detect over 70% of hotspot variants at 0.01%–0.1% frequency using in silico error suppression.

Conclusions:

  • Presented the first comprehensive analysis of sequencing error sources in conventional NGS workflows.
  • Error profiles identified offer new avenues for experimental and computational improvements in NGS analysis accuracy.
  • Enhanced precision in deep sequencing is achievable through a better understanding and mitigation of sequencing errors.