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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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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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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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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

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Published on: July 25, 2013

Optimizing multiple sequence alignments using a genetic algorithm based on three objectives: structural information,

Francisco M Ortuño1, Olga Valenzuela, Fernando Rojas

  • 1Department of Computer Architecture and Computer Technology, CITIC-UGR, Department of Applied Mathematics, University of Granada, Granada, Spain. fortuno@ugr.es

Bioinformatics (Oxford, England)
|June 25, 2013
PubMed
Summary
This summary is machine-generated.

A new multiobjective algorithm optimizes multiple sequence alignments (MSAs) using structural information for improved accuracy. This bioinformatics tool outperforms existing methods, offering a more reliable approach to sequence alignment.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Multiple sequence alignments (MSAs) are crucial for predicting protein structure, function, and evolutionary relationships.
  • Current MSA tools often yield suboptimal alignments, especially for distantly related sequences, leading to inconsistencies in evaluation.
  • 3D structural information is increasingly utilized to improve MSA accuracy due to its higher conservation compared to sequence data.

Purpose of the Study:

  • To develop a novel multiobjective algorithm for optimizing multiple sequence alignments.
  • To integrate structural information into the alignment process for enhanced accuracy, particularly for divergent sequences.
  • To provide a more reliable method for evaluating and generating high-quality MSAs.

Main Methods:

  • A multiobjective algorithm based on the non-dominated sorting genetic algorithm was employed.
  • The algorithm jointly optimizes the STRIKE score, non-gaps percentage, and totally conserved columns.
  • Structural information was incorporated into the objective function to guide the alignment process.

Main Results:

  • The algorithm was rigorously assessed on the BAliBASE benchmark, showing statistically significant improvements (P < 0.01).
  • It outperformed several established aligners, including ClustalW, HMMT, and PIMA (P < 0.05).
  • Performance was comparable to 3D-COFFEE, with the advantage of requiring fewer structural inputs.

Conclusions:

  • The proposed algorithm offers a superior approach to generating accurate multiple sequence alignments.
  • Integrating structural information enhances the evaluation of distant sequence relationships.
  • The tool provides a valuable advancement for bioinformatics research, aiding in structure prediction and functional analysis.