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Related Concept Videos

RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Next-generation Sequencing03:00

Next-generation Sequencing

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.
Sanger Sequencing01:57

Sanger Sequencing

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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Related Experiment Video

Updated: May 22, 2026

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

Exome sequencing generates high quality data in non-target regions.

Yan Guo1, Jirong Long, Jing He

  • 1Center for Quantitative Sciences, Vanderbilt Ingram Cancer Center, Nashville, TN, USA.

BMC Genomics
|May 22, 2012
PubMed
Summary

Whole exome sequencing (WES) generates high-quality genetic data outside targeted regions. This valuable information, often overlooked, significantly increases the number of detectable single nucleotide polymorphisms (SNPs) for genetic studies.

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Last Updated: May 22, 2026

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Area of Science:

  • Genomics
  • Human Genetics
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) enables cost-efficient exome sequencing for disease variant detection.
  • Exome sequencing often yields DNA fragments outside targeted regions, with this data typically disregarded.
  • This underutilized data represents a missed opportunity for comprehensive genetic analysis.

Purpose of the Study:

  • To evaluate the quality and quantity of single nucleotide polymorphisms (SNPs) detected in exome sequencing data outside of targeted regions.
  • To determine if this 'off-target' data can yield high-quality genotypes comparable to those within target regions.
  • To assess the potential of incorporating off-target data into genetic epidemiology studies.

Main Methods:

  • Whole exome sequencing was performed on 22 subjects using Agilent SureSelect and 6 subjects using Illumina TrueSeq capture reagents.
  • Sequencing data from 6 subjects from the 1000 Genomes Project Pilot 3 study were also analyzed.
  • SNP quality was assessed by comparing genotypes to SNP chips/Hapmap, evaluating transition-transversion (Ti/Tv) ratios, and checking against dbSNP.

Main Results:

  • High-quality SNPs were identified outside target regions across all tested platforms (Agilent, Illumina, 1000 Genomes).
  • Agilent SureSelect data yielded a 129% increase in high-quality SNPs outside target regions (84,049 vs. 65,231).
  • Illumina TrueSeq data showed a 232% increase (222,171 vs. 95,818), and 1000 Genomes data a 461% increase (7,139 vs. 1,548).

Conclusions:

  • Exome sequencing data contains a substantial amount of high-quality genotypes outside of targeted exonic regions.
  • This off-target data significantly expands the number of detectable SNPs, offering a richer resource for genetic analysis.
  • The findings advocate for the inclusion of off-target exome sequencing data in genetic epidemiology studies to enhance discovery potential.