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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 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.
Single Nucleotide Polymorphisms-SNPs01:05

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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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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Genetic algorithms with permutation coding for multiple sequence alignment.

Mohamed Tahar Ben Othman1, Gamil Abdel-Azim

  • 1Qassim University, College of Computer, Saudi Arabia. mtothman@gmail.com

Recent Patents on DNA & Gene Sequences
|September 15, 2012
PubMed
Summary

This study introduces a hybrid genetic algorithm (GA) using permutation coding (PC) to improve multiple sequence alignment (MSA). The novel approach enhances GA efficiency and accuracy for complex bioinformatics tasks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Development

Background:

  • Multiple Sequence Alignment (MSA) is a critical yet computationally challenging problem in bioinformatics, often classified as NP-complete.
  • Existing heuristic algorithms, including Genetic Algorithms (GAs), aim to find optimal alignments but suffer from time consumption and local minima issues.
  • Solution Coding (SC) is a key aspect of GAs for MSA, focusing on maximizing sequence similarities through gap manipulation.

Purpose of the Study:

  • To propose a novel hybrid algorithm combining Genetic Algorithms (GAs) with Permutation Coding (PC) and the 2-opt algorithm for enhanced Multiple Sequence Alignment (MSA).
  • To address the major weaknesses of GAs in MSA, namely time consumption and susceptibility to local minima.
  • To develop an efficient scoring function for MSA based on PC, serving as a fitness function to improve GA performance.

Main Methods:

  • A hybrid algorithm integrating GAs with Permutation Coding (PC) and the 2-opt algorithm was developed.
  • Permutation Coding (PC) was employed to represent and manipulate MSA solutions, enhancing GA resource gain, reliability, and diversity.
  • A novel scoring function based on PC was designed and implemented as the fitness function for the GA.

Main Results:

  • The proposed hybrid GA with PC significantly reduces the time complexity associated with MSA.
  • The PC approach facilitates the application of permutation-based functions for MSA, improving alignment quality.
  • Implementation involved testing various selection strategies and crossover methods, with fixed probabilities for crossover and mutation.

Conclusions:

  • The hybrid GA incorporating Permutation Coding offers a more efficient and effective solution for the complex problem of Multiple Sequence Alignment (MSA).
  • This approach mitigates common GA drawbacks, paving the way for improved computational biology analyses.
  • Further investigation into relevant patents was conducted to contextualize the proposed methodology within existing solutions.