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Related Concept Videos

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.
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

Updated: Jun 12, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Optimal algorithms for haplotype assembly from whole-genome sequence data.

Dan He1, Arthur Choi, Knot Pipatsrisawat

  • 1Department of Computer Science, University of California Los Angeles, Los Angeles, CA 90095, USA. danhe@cs.ucla.edu

Bioinformatics (Oxford, England)
|June 10, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a novel dynamic programming algorithm for optimal haplotype assembly, crucial for genetic variation analysis. While direct assembly from current sequencing reads is impractical, combining this method with traditional phasing aids in constructing haplotypes with common and rare variants.

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jun 12, 2026

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies
12:08

Hybrid De Novo Genome Assembly for the Generation of Complete Genomes of Urinary Bacteria using Short- and Long-read Sequencing Technologies

Published on: August 20, 2021

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Haplotype inference is vital for analyzing genetic variation.
  • Traditional methods rely on population genotype data.
  • High-throughput sequencing enables haplotype assembly from sequence fragments (reads).
  • Haplotype assembly is NP-hard, especially with sequencing errors.
  • Existing algorithms lack guaranteed optimality.

Purpose of the Study:

  • To develop an optimal algorithm for haplotype assembly.
  • To address the computational challenges of haplotype assembly from sequencing reads.

Main Methods:

  • Proposed a dynamic programming algorithm for optimal haplotype assembly.
  • Reduced the haplotype assembly problem to maximum satisfiability.
  • Analyzed time complexity as O(m x 2(k) x n).

Main Results:

  • The dynamic programming approach guarantees optimal haplotype assembly.
  • Assembly using current sequencing read lengths is computationally impractical.
  • A hybrid approach combining the new algorithm with traditional phasing is practical.
  • This hybrid method can construct haplotypes including common and rare variants.

Conclusions:

  • The developed dynamic programming algorithm provides an optimal solution for haplotype assembly.
  • Direct haplotype assembly from short reads is not yet feasible.
  • Combining computational and traditional phasing methods offers a practical solution for haplotype construction.
  • This integrated approach is effective for incorporating both common and rare genetic variants.