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Related Concept Videos

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
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Updated: May 8, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

BlackOPs: increasing confidence in variant detection through mappability filtering.

Christopher R Cabanski1, Matthew D Wilkerson, Matthew Soloway

  • 1Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC 27599, USA, The Genome Institute at Washington University, St. Louis, MO 63108, USA, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Computer Science, University of Kentucky, Lexington, KY 40506, USA, Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA and Division of Medical Oncology, Department of Internal Medicine, University of North Carolina, Chapel Hill, NC 27599, USA.

Nucleic Acids Research
|August 13, 2013
PubMed
Summary
This summary is machine-generated.

High-throughput sequencing struggles with distinguishing true variants from technical artifacts. BlackOPs tool generates a sample-specific blacklist of mismapping errors, significantly reducing false positive variant calls.

Related Experiment Videos

Last Updated: May 8, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • High-throughput sequencing (HTS) is crucial for genomic analysis, but distinguishing true biological variants from technical artifacts remains a significant challenge.
  • Sequence mismapping, where reads are incorrectly aligned to the reference genome, is a major source of artifact variants that can be mistaken for genuine genetic differences.

Purpose of the Study:

  • To develop an open-source tool, BlackOPs, for simulating HTS data and identifying mismapping-induced artifact variants.
  • To create sample-specific blacklists of artifact positions and alleles to improve variant calling accuracy in genomic studies.

Main Methods:

  • BlackOPs simulates RNA-seq and whole exome DNA sequencing data from a reference genome.
  • Simulated sequences are aligned using custom parameters to mimic experimental conditions.
  • Variants are detected, and a blacklist of mismapping-caused artifact positions and alleles is generated.

Main Results:

  • Blacklists contain thousands of artifact variants that are computationally indistinguishable from true variants.
  • These artifact positions are specific to alignment algorithms and read lengths, highlighting the need for experimental setup-specific filtering.
  • Filtering against BlackOPs-generated blacklists demonstrably reduced false positive variant calls in an RNA-seq glioblastoma dataset.

Conclusions:

  • Accounting for mapping-caused variants, tailored to specific experimental setups, significantly reduces false positives in HTS data analysis.
  • The BlackOPs tool provides a method to generate crucial blacklists, thereby improving the accuracy and reliability of genome characterization.
  • This approach enhances the precision of variant identification, leading to more robust genomic discoveries.