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Related Concept Videos

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...
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%...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

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Related Experiment Video

Updated: May 29, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter

Heng Li1

  • 1Medical Population Genetics Program, Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA. hengli@broadinstitute.org

Bioinformatics (Oxford, England)
|September 10, 2011
PubMed
Summary

This study introduces a new statistical framework for DNA sequence analysis that directly uses sequencing data, bypassing the need for accurate genotypes. This method achieves comparable accuracy for various genetic analyses, including single nucleotide polymorphism (SNP) calling and somatic mutation discovery.

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

Published on: April 4, 2018

Related Experiment Videos

Last Updated: May 29, 2026

Infinium Assay for Large-scale SNP Genotyping Applications
13:33

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Traditional DNA sequence analysis methods depend on accurate sequences or genotypes.
  • Next-generation sequencing (NGS) applications often yield data with inherent uncertainty, hindering conventional analysis.
  • There is a need for robust methods to analyze sequence data with missing or uncertain genotype information.

Purpose of the Study:

  • To develop a statistical framework for analyzing DNA sequencing data directly, without relying on explicit genotyping.
  • To enable accurate genetic analyses in scenarios with uncertain sequence data, such as low-coverage sequencing or somatic mutation detection.

Main Methods:

  • A novel statistical framework was developed to process raw sequencing data.
  • The framework integrates methods for single nucleotide polymorphism (SNP) calling, somatic mutation discovery, population genetics parameter inference, and association testing.
  • The approach operates directly on sequencing data, avoiding intermediate genotyping or imputation steps.

Main Results:

  • The proposed method demonstrates comparable accuracy to existing techniques for estimating site allele counts and allele frequency spectrum.
  • Association mapping accuracy is also comparable to alternative methods.
  • The study highlights the importance of symmetric datasets for accurate somatic mutation detection and identifies mismapping as a key error source for rare event discovery.

Conclusions:

  • The developed statistical framework offers a powerful alternative for DNA sequence analysis when genotype data is uncertain or unavailable.
  • This approach enhances the utility of NGS data in various applications, including population genetics and disease research.
  • The findings underscore the need to account for data uncertainty and potential errors like mismapping in sequence analysis.