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Related Concept Videos

Next-generation Sequencing03:00

Next-generation Sequencing

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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....
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RNA-seq03:21

RNA-seq

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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...
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Related Experiment Video

Updated: Mar 19, 2026

Targeted DNA Methylation Analysis by Next-generation Sequencing
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Pattern Recognition on Read Positioning in Next Generation Sequencing.

Boseon Byeon1, Igor Kovalchuk1

  • 1Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta, T1K 3M4, Canada.

Plos One
|June 15, 2016
PubMed
Summary
This summary is machine-generated.

Next-generation sequencing (NGS) read start positions are biased by nucleotide distribution, not random. Correcting this sequencing bias requires analyzing multi-nucleotide patterns, not just mono-nucleotides.

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Area of Science:

  • Genomics
  • Bioinformatics

Background:

  • Next-generation sequencing (NGS) relies on random DNA/cDNA cleavage for short read generation.
  • Previous studies indicate potential sequencing bias in NGS reads.

Purpose of the Study:

  • To investigate sequencing bias in NGS data across organisms with varying GC content.
  • To identify patterns influencing NGS read start positioning.
  • To evaluate methods for correcting sequencing bias.

Main Methods:

  • Analysis of NGS data from four diverse organisms (Plasmodium falciparum, Arabidopsis thaliana, Homo sapiens, Streptomyces coelicolor).
  • Application of machine learning techniques to identify read start positioning patterns.
  • Assessment of mono-nucleotide versus multi-nucleotide distributions in relation to bias.

Main Results:

  • A pattern was identified where NGS read starts are positioned in regions with local nucleotide distributions differing from global distributions.
  • Mono-nucleotide distribution analysis underestimates the extent of sequencing bias.
  • Multi-nucleotide (di-, tri-, tetra-nucleotides) distributions largely explain the observed read positioning bias.

Conclusions:

  • Sequencing bias in NGS is significantly influenced by multi-nucleotide distributions.
  • Effective correction of NGS sequencing bias necessitates considering multi-nucleotide patterns.
  • Mono-nucleotide based bias correction may be insufficient for accurate NGS data analysis.