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Related Concept Videos

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...
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%...
RNA-seq03:21

RNA-seq

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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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...
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.

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Related Experiment Video

Updated: May 10, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Leveraging reads that span multiple single nucleotide polymorphisms for haplotype inference from sequencing data.

Wen-Yun Yang1, Farhad Hormozdiari, Zhanyong Wang

  • 1Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, CA 90095, USA.

Bioinformatics (Oxford, England)
|July 5, 2013
PubMed
Summary

This study introduces a new computational framework for haplotype inference from short read sequencing data. The method improves accuracy by utilizing multi-single nucleotide polymorphic reads and a reference panel, outperforming existing approaches.

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

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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Last Updated: May 10, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

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

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Population Genetics

Background:

  • Haplotypes are crucial for genetic analysis but experimentally determined haplotypes are expensive.
  • Traditional haplotype inference relies on computational methods using genotype data and Hidden Markov Models.
  • Short read sequencing generates haplotypic information (multi-single nucleotide polymorphic reads) often ignored by current methods.

Purpose of the Study:

  • To develop a novel framework for haplotype inference from short read sequencing data.
  • To leverage multi-single nucleotide polymorphic reads and reference panels for improved accuracy.
  • To create a computationally efficient method for haplotype phasing.

Main Methods:

  • Developed a new probabilistic model to identify the most likely haplotype segments from a reference panel that explain observed short read sequencing data.
  • Implemented an efficient sampling method within the probabilistic model.
  • Validated the approach using simulated sequencing reads from HapMap and 1000 Genomes project data.

Main Results:

  • The proposed method achieves high accuracy and computational efficiency in haplotype inference.
  • Haplotype predictions showed approximately 20% improvement in accuracy over the basic haplotype copying model.
  • The method improved accuracy by approximately 10% over Hap-SeqX with reduced computational time and memory usage.

Conclusions:

  • The novel framework effectively infers haplotypes from short read sequencing data by integrating multi-single nucleotide polymorphic reads.
  • The method offers a significant improvement in accuracy and computational efficiency compared to existing approaches.
  • This approach has the potential to enhance various genetic analyses relying on accurate haplotype information.