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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.
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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,...
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Related Experiment Video

Updated: May 16, 2026

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

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Published on: December 10, 2012

Parameterised algorithms of the individual haplotyping problem with gaps.

Minzhu Xie1, Jing Wang

  • 1College of Physics and Information Science, Hunan Normal University, Changsha 410081, China.

International Journal of Bioinformatics Research and Applications
|December 5, 2012
PubMed
Summary

This study presents efficient algorithms for the individual haplotyping problem, which reconstructs two haplotypes from DNA fragments. The new computational methods effectively solve Minimum SNP Removal (MSR) and Minimum Fragment Removal (MFR) problems.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genetics

Background:

  • The individual haplotyping problem is crucial for understanding genetic variation.
  • Existing computational models for haplotyping include Minimum SNP Removal (MSR) and Minimum Fragment Removal (MFR).

Purpose of the Study:

  • To develop efficient parameterized algorithms for solving the MSR and MFR problems in individual haplotyping.
  • To analyze the time complexity of these novel algorithms.

Main Methods:

  • Proposed parameterized algorithms for MSR and MFR.
  • Analyzed algorithm performance based on parameters: m DNA fragments, n SNPs, maximum fragment length (k1), maximum fragments per SNP (k2), and maximum holes (k).

Main Results:

  • Developed algorithms with time complexities O(2^knk1k2+m log m+nk2+mk1) for MSR and O(mk1k2^2k+2^3kmk2^2+m log m+nk2+mk1) for MFR.
  • Demonstrated practical efficiency due to small, real-world values of k1 and k2.

Conclusions:

  • The proposed algorithms offer efficient solutions for the individual haplotyping problem.
  • These algorithms are applicable in practical computational biology and genetics research.