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Related Concept Videos

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...
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...

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Related Experiment Video

Updated: May 25, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

DNA methylome analysis using short bisulfite sequencing data.

Felix Krueger1, Benjamin Kreck, Andre Franke

  • 1Bioinformatics Group, The Babraham Institute, Cambridge, UK.

Nature Methods
|February 1, 2012
PubMed
Summary
This summary is machine-generated.

Analyzing whole-genome bisulfite sequencing (WGBS) data presents significant challenges. This study addresses common issues in mapping and analyzing WGBS data, offering recommendations for accurate methylation level inference.

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DNA Methylation: Bisulphite Modification and Analysis
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DNA Methylation: Bisulphite Modification and Analysis

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Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

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Related Experiment Videos

Last Updated: May 25, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
08:38

Targeted DNA Methylation Analysis by Next-generation Sequencing

Published on: February 24, 2015

DNA Methylation: Bisulphite Modification and Analysis
12:34

DNA Methylation: Bisulphite Modification and Analysis

Published on: October 21, 2011

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution
13:47

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Area of Science:

  • Epigenetics
  • Genomics
  • Bioinformatics

Background:

  • Bisulfite conversion followed by next-generation sequencing (BS-seq) is a standard method for whole-genome methylation analysis.
  • Accurate analysis of BS-seq data, particularly mapping, remains a significant challenge in genomics.

Purpose of the Study:

  • To summarize the key challenges encountered in BS-seq data mapping for both base- and color-space data.
  • To investigate the impact of sequencing errors and contaminants on methylation level estimations.
  • To provide recommendations for optimal BS-seq data analysis.

Main Methods:

  • Review and summarization of BS-seq mapping challenges.
  • Exploration of sequencing error and contaminant effects on methylation inference.
  • Analysis of base- and color-space BS-seq data.

Main Results:

  • BS-seq data mapping presents distinct challenges for different sequencing data formats.
  • Sequencing errors and contaminants can significantly skew inferred DNA methylation levels.
  • Specific analytical approaches are recommended for accurate BS-seq data interpretation.

Conclusions:

  • Addressing mapping complexities and sequencing artifacts is crucial for reliable methylome analysis.
  • Adopting recommended analytical strategies enhances the accuracy of methylation level determination from BS-seq data.
  • This work provides valuable insights for researchers utilizing BS-seq for epigenomic studies.