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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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,...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...

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Related Experiment Video

Updated: May 30, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

New algorithm improves fine structure of the barley consensus SNP map.

Jeffrey B Endelman1

  • 1Department of Crop and Soil Sciences, Washington State University, 16650 State Route 536, Mount Vernon, WA 98273, USA. j.endelman@gmail.com

BMC Genomics
|August 12, 2011
PubMed
Summary
This summary is machine-generated.

Integrating genetic linkage maps is crucial for understanding complex genomic relationships. The new DAGGER R package simplifies and linearizes consensus graphs, creating more accurate and dense genetic maps.

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Last Updated: May 30, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Integrating information from multiple genetic linkage maps presents a significant challenge in genetics.
  • Directed graphs offer a method to visualize complex ordinal relationships between genetic markers.
  • Directed acyclic graphs (DAGs) are formed when linkage maps have no ordering conflicts, enabling linearization into a consensus map.

Purpose of the Study:

  • To develop and implement new algorithms for simplifying and linearizing consensus graphs derived from genetic linkage maps.
  • To create a user-friendly R package for generating accurate consensus genetic maps.

Main Methods:

  • Implementation of novel algorithms for graph simplification and linearization within the R computing environment.
  • Utilizing linear or quadratic programming for map generation to minimize error and maintain ordinal consistency.
  • Application of the DAGGER package to existing barley linkage maps with a large number of single nucleotide polymorphism (SNP) markers.

Main Results:

  • The DAGGER package successfully simplifies consensus graphs, accurately capturing ordinal relationships from multiple linkage maps.
  • Consensus maps generated by DAGGER exhibit reduced error (0.82 cM/interval) compared to existing maps (2.28 cM/interval) in barley.
  • Genetic simulations demonstrated that DAGGER-generated maps possess higher accuracy and marker density post-rescaling compared to individual input maps.

Conclusions:

  • The R package DAGGER provides an effective and accessible tool for integrating information from consistent genetic linkage maps.
  • DAGGER enhances the fine-structure analysis of genetic maps, as evidenced by its application to the barley hardness locus.
  • The package offers a valuable resource for geneticists and bioinformaticians working with comparative mapping.