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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.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
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Related Experiment Video

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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An adaptive method of defining negative mutation status for multi-sample comparison using next-generation sequencing.

Nicholas Hutson1, Fenglin Zhan1,2, James Graham1

  • 1Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

BMC Medical Genomics
|December 3, 2021
PubMed
Summary
This summary is machine-generated.

A new mutation-specific negative (MSN) method accurately distinguishes negative from unknown mutation statuses in cancer genomics. This adaptive approach improves data availability by reducing false negatives in next-generation sequencing (NGS) data.

Keywords:
Genetic testingLiquid biopsyNegative statusNext-generation sequencingPersonalized medicineTumor heterogeneity

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Multi-sample comparison is crucial in cancer genomics using next-generation sequencing (NGS).
  • Determining true negative mutation status is challenging due to potential false negatives from low sequencing coverage.
  • Current universal minimum coverage (UMC) methods use arbitrary thresholds, leading to misclassification.

Purpose of the Study:

  • To develop an adaptive method for improved discrimination between negative and unknown mutation statuses in NGS data.
  • To address limitations of universal minimum coverage thresholds in multi-sample cancer genomics.
  • To enhance the accuracy and data yield in mutation status determination.

Main Methods:

  • Proposed an adaptive mutation-specific negative (MSN) method for classifying mutation statuses.
  • Compared MSN against the universal minimum coverage (UMC) method using simulated and real datasets.
  • MSN assesses negative status by comparing non-positive samples against all known positive samples.

Main Results:

  • The MSN method accurately assigned negative statuses across varying tumor cell fractions in simulated data.
  • MSN provided more accurate negative status assessments on a real dual-platform single-cell sequencing dataset.
  • MSN yielded three times more available data compared to UMC by reducing "unknown" calls.

Conclusions:

  • Developed a novel adaptive method (MSN) for distinguishing unknown from negative statuses in multi-sample NGS data.
  • MSN offers superior accuracy in negative status determination over conventional UMC methods.
  • The MSN method significantly increases usable data by minimizing unnecessary "unknown" classifications.