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Related Concept Videos

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.
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...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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Updated: Jun 6, 2026

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing

Published on: October 10, 2025

Overcoming bias and systematic errors in next generation sequencing data.

Margaret A Taub1, Hector Corrada Bravo, Rafael A Irizarry

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, E3527, Baltimore, MD 21205, USA. mtaub@jhsph.edu.

Genome Medicine
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

High-throughput sequencing data require careful analysis due to technological and biological biases. Addressing these systematic errors is crucial for reliable clinical applications of sequencing technologies.

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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

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Last Updated: Jun 6, 2026

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
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Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing

Published on: October 10, 2025

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

Area of Science:

  • Genomics and Bioinformatics
  • Clinical Diagnostics

Background:

  • High-throughput technologies like microarrays and sequencing generate complex data.
  • Clinical applications necessitate robust data analysis and quality assessment methods.

Discussion:

  • New high-throughput sequencing data contain technological and biological biases.
  • Systematic errors can significantly impact downstream analysis results.
  • Identifying and adjusting for these biases is essential for reliable interpretation.

Key Insights:

  • Consistently observed biases in high-throughput sequencing data are reviewed.
  • The impact of these biases on analytical outcomes is discussed.
  • Strategies for mitigating bias in sequencing data analysis are proposed.

Outlook:

  • Further research is needed to refine bias identification and correction methods.
  • Standardized protocols for quality assessment will enhance clinical utility.
  • Overcoming data biases is key to realizing the full potential of sequencing in healthcare.