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Related Concept Videos

Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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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.
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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.
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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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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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Genotypic Inference of HIV-1 Tropism Using Population-based Sequencing of V3
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Joint haplotype assembly and genotype calling via sequential Monte Carlo algorithm.

Soyeon Ahn1, Haris Vikalo2

  • 1Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, 78712, Texas, USA. soyeon.ahn@utexas.edu.

BMC Bioinformatics
|July 17, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces ParticleHap, a novel algorithm for accurate haplotype assembly and genotype calling. It improves upon existing methods by utilizing probabilistic sequencing data, leading to more reliable genetic variation analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genetic variations influence disease predisposition, complex disease development, and drug metabolism.
  • Haplotypes, ordered lists of SNPs, provide comprehensive individual DNA variation data.
  • High-throughput sequencing generates erroneous data, impacting SNP and genotype calling accuracy for haplotyping.

Purpose of the Study:

  • To develop a method for joint genotype calling and haplotype assembly.
  • To address the limitations of existing haplotyping techniques that assume correct genotype information.

Main Methods:

  • Developed ParticleHap, a haplotype assembly algorithm using probabilistic sequencing data.
  • Employed a deterministic sequential Monte Carlo algorithm for joint genotype inference and haplotype assembly.
  • Utilized genotype likelihoods instead of called genotypes to enhance accuracy.

Main Results:

  • ParticleHap achieves highly accurate haplotype assembly and genotype calling.
  • The algorithm is computationally efficient and scalable, outperforming existing methods in accuracy and speed.
  • Demonstrated effectiveness on 1000 Genomes Project data and simulation studies.

Conclusions:

  • The probabilistic framework and Monte Carlo algorithm enable efficient joint haplotype assembly and genotyping.
  • ParticleHap provides fast and accurate haplotype assembly by re-evaluating erroneous genotypes.
  • A C code implementation of ParticleHap will be publicly available.