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Related Concept Videos

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%...
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,...
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...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Related Experiment Video

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Mining SNPs from DNA sequence data; computational approaches to SNP discovery and analysis.

Jan van Oeveren1, Antoine Janssen

  • 1Division of Bioinformatics, Keygene, Wageningen, NV, The Netherlands.

Methods in Molecular Biology (Clifton, N.J.)
|September 22, 2009
PubMed
Summary

Identifying single nucleotide polymorphisms (SNPs), the most common genetic variations, is crucial for molecular markers. This study details bioinformatics tools and pipelines for mining SNPs from DNA sequences, especially with new high-throughput sequencing technologies.

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Published on: June 23, 2012

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation.
  • SNPs are fundamental for molecular markers and require identification before use in assays.
  • Public databases offer extensive DNA sequences for SNP discovery, but high-throughput sequencing accelerates this process.

Purpose of the Study:

  • To discuss the bioinformatics tools essential for analyzing DNA sequence data for SNP mining.
  • To present a general approach for the consecutive steps involved in the SNP mining process.
  • To introduce commonly used SNP discovery pipelines.

Main Methods:

  • Alignment of multiple sequence fragments from different genotypes to identify sequence variants.
  • Mining publicly available expressed sequence tags (ESTs) and genomic sequences.
  • Utilizing high-throughput sequencing data from next-generation sequencing machines (e.g., Roche GS/454, Illumina GA/Solexa, SOLiD).

Main Results:

  • Nucleotide mismatches in aligned sequences indicate potential SNPs or insertions/deletions.
  • Public databases provide a valuable resource for SNP mining without extensive de novo sequencing.
  • Next-generation sequencing enables efficient sequencing of polymorphic genotypes for SNP identification.

Conclusions:

  • Bioinformatics tools and pipelines are critical for effective SNP mining from diverse DNA sequence data.
  • The integration of public databases and high-throughput sequencing significantly enhances SNP discovery.
  • This work provides a framework for researchers to identify SNPs for various genetic applications.