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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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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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DNA Microarrays02:34

DNA Microarrays

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Related Experiment Video

Updated: Apr 18, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A nonhomogeneous hidden markov model for gene mapping based on next-generation sequencing data.

Fatemeh Zamanzad Ghavidel1, Jürgen Claesen, Tomasz Burzykowski

  • 1Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University , Diepenbeek, Belgium .

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|January 23, 2015
PubMed
Summary

Mapping quantitative trait loci (QTL) is advanced by a new nonhomogeneous hidden Markov model. This model accounts for SNP distance, improving gene mapping for polygenetic traits like ethanol tolerance in yeast.

Keywords:
next-generation sequencingnonhomogeneous hidden Markov modelquantitative trait loci analysissingle-nucleotide polymorphisms

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical modeling

Background:

  • Quantitative trait loci (QTL) mapping is crucial for understanding polygenetic traits.
  • Current methods often assume equal marker distribution, ignoring inter-marker distances.
  • Accurate gene mapping relies on understanding co-segregation between genes and molecular markers.

Purpose of the Study:

  • To develop an improved statistical model for QTL analysis.
  • To incorporate the influence of distance between single-nucleotide polymorphisms (SNPs) into gene mapping.
  • To enhance the discovery of genomic loci associated with complex traits.

Main Methods:

  • Utilized a nonhomogeneous hidden Markov model (HMM).
  • The HMM incorporates a transition matrix dependent on distance-varying covariates.
  • Applied next-generation sequencing data and SNP analysis.

Main Results:

  • The proposed nonhomogeneous HMM improves upon standard HMMs by considering SNP distances.
  • Demonstrated the model's utility in identifying QTL associated with ethanol tolerance in yeast.
  • Provides a more nuanced approach to gene mapping by accounting for genomic structure.

Conclusions:

  • The nonhomogeneous HMM offers a more accurate method for QTL analysis.
  • Accounting for SNP distances enhances the precision of gene mapping.
  • This approach has significant implications for genetic research and breeding programs.