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Related Concept Videos

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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Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Genome Annotation and Assembly

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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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DNA Microarrays

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Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine
10:40

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A greedy, graph-based algorithm for the alignment of multiple homologous gene lists.

Jan Fostier1, Sebastian Proost, Bart Dhoedt

  • 1Department of Information Technology, Ghent University, Ghent, Belgium.

Bioinformatics (Oxford, England)
|January 11, 2011
PubMed
Summary

This study introduces a novel graph-based algorithm for aligning homologous genomic segments, improving accuracy for divergent regions. The method is efficient for large comparative genomics datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Comparative genomics relies on accurate alignment of homologous genomic regions, often represented as gene lists.
  • Progressive alignment methods can propagate errors, particularly with divergent genomic segments.
  • Existing tools may struggle with gene duplication and rearrangement events.

Purpose of the Study:

  • To develop a novel, accurate, and efficient greedy, graph-based algorithm for multiple homologous genomic segment alignment.
  • To address limitations of progressive alignment in handling gene duplication and rearrangement.
  • To improve alignment accuracy for strongly diverged genomic regions.

Main Methods:

  • A greedy, graph-based algorithm is proposed for aligning multiple homologous genomic segments.
  • Heuristics are developed based on graph properties to resolve local alignment conflicts.
  • The algorithm is implemented within the i-ADHoRe 3.0 package.

Main Results:

  • The proposed method demonstrates substantially higher alignment accuracy compared to progressive and earlier graph-based methods, especially for divergent segments.
  • The algorithm is computationally efficient, suitable for large datasets including multiple eukaryotic genomes.
  • Performance was validated using homologous genomic segments from Arabidopsis thaliana.

Conclusions:

  • The novel graph-based algorithm offers improved accuracy and efficiency for aligning homologous genomic segments.
  • This approach is particularly valuable for comparative genomics studies involving divergent sequences and complex genomic structures.
  • The i-ADHoRe 3.0 package provides a robust tool for large-scale genomic analysis.