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Related Experiment Video

Updated: May 25, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detecting false-positive signals in exome sequencing.

Karin V Fuentes Fajardo1, David Adams,

  • 1NIH Undiagnosed Diseases Program, NIH Office of Rare Diseases Research and NHGRI, Bethesda, Maryland, USA.

Human Mutation
|February 2, 2012
PubMed
Summary
This summary is machine-generated.

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Identifying disease-causing mutations is challenging due to numerous sequence variants. This study provides lists of variants and genes to exclude, improving the accuracy of exome analysis for genetic disease discovery.

Area of Science:

  • Genomics
  • Human Genetics
  • Bioinformatics

Background:

  • Affordable exome and genome sequencing have revolutionized disease gene discovery.
  • Identifying causative mutations requires filtering a large number of sequence variants.
  • Certain variants are poor candidates for disease causation due to polymorphism, alignment issues, or reference genome inaccuracies.

Purpose of the Study:

  • To identify and list variants and genes unsuitable for disease causation analysis from exome sequence data.
  • To improve the efficiency and accuracy of identifying disease-causing mutations through variant filtering.

Main Methods:

  • Analysis of exome sequence data from 118 individuals across 29 families from the NIH Undiagnosed Diseases Program (UDP).
  • Identification of variants based on criteria including high polymorphism, potential alignment errors (excess heterozygosity), and misleading reference genome information (hg18).

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Last Updated: May 25, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
11:02

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Main Results:

  • Identified 23,389 positions with excess heterozygosity, suggesting alignment errors.
  • Identified 1,009 positions where the hg18 human genome reference sequence appeared to contain a minor allele.
  • Generated supplemental lists of variants and genes for provisional exclusion in exome analysis.

Conclusions:

  • Excluding specific variants and genes with alignment errors or reference genome discrepancies can enhance the identification of disease-causing mutations.
  • The provided lists serve as a valuable resource for researchers conducting exome-based genetic studies.
  • This approach aids in refining variant interpretation for improved genetic diagnosis in undiagnosed diseases.