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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
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Comparing Copy Number Variations and SNPs02:26

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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Sanger Sequencing01:57

Sanger Sequencing

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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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RNA-seq03:21

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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. 
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Updated: Jan 4, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Hidden Markov Models in Bioinformatics: SNV Inference from Next Generation Sequence.

Jiawen Bian1, Xiaobo Zhou2

  • 1School of Mathematics and Physics, China University of Geosciences, Wuhan, 430074, China.

Methods in Molecular Biology (Clifton, N.J.)
|February 23, 2017
PubMed
Summary

Next-generation sequencing (NGS) aids genomic research, but struggles with detecting single nucleotide variants (SNVs) in regulatory regions. A novel Hidden Markov Model (HMM) improves SNV detection by integrating read quality metrics.

Keywords:
Hidden Markov modelLow sequencing depthNext generation sequencingPosterior probabilitySingle nucleotide variation

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Next-generation sequencing (NGS) offers powerful genomic exploration capabilities.
  • Hidden Markov models (HMMs) are widely used in bioinformatics for pattern recognition, including detecting regulatory elements.
  • NGS faces challenges in accurately detecting single nucleotide variants (SNVs) within genomic regulatory regions due to lower sequence coverage and base quality.

Purpose of the Study:

  • To develop a specific Hidden Markov Model (HMM) tailored for improved single nucleotide variant (SNV) detection in genomic regulatory regions using NGS data.
  • To enhance the accuracy of genotype inference at each genomic position by leveraging NGS read quality information.

Main Methods:

  • Development of a specialized Hidden Markov Model (HMM).
  • Incorporation of mapping quality and base quality of NGS reads into the HMM's emission probability.
  • Detailed presentation of the algorithm's procedure and implementation for genotype inference.

Main Results:

  • The developed HMM demonstrates improved capability for inferring genotypes at genomic positions.
  • Integration of read quality metrics enhances SNV detection accuracy in challenging genomic regions.

Conclusions:

  • The specialized HMM provides a robust method for overcoming NGS limitations in regulatory region variant detection.
  • This approach advances genomic research by enabling more reliable identification of mutations, potentially aiding cancer gene mutation studies.