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Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

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.
Challenges of the Maxam-Gilbert Method
The...
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.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...
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...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
DNA as a Genetic Template02:05

DNA as a Genetic Template

Two structural features of the DNA molecule provide a basis for the mechanisms of heredity: the four nucleotide bases and its double-stranded nature. The Watson-Crick model of double-helical DNA structure, proposed in 1952, drew heavily upon the X-ray crystallography work of researchers Rosalind Franklin and Maurice Wilkins. Watson, Crick, and Wilkins jointly received the Nobel Prize in Physiology or Medicine for their work in 1962. Franklin was, controversially, excluded from the prize for...

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Updated: Jun 15, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A quantum-inspired genetic algorithm based on probabilistic coding for multiple sequence alignment.

Hong-Wei Huo1, Vojislav Stojkovic, Qiao-Luan Xie

  • 1School of Computer Science and Technology, Xidian University, Xi'an 710071, China. hwhuo@mail.xidian.edu.cn

Journal of Bioinformatics and Computational Biology
|February 26, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces QGMALIGN, a quantum-inspired genetic algorithm for multiple sequence alignment. It leverages quantum parallelism for faster computation and improved accuracy compared to classical methods.

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Last Updated: Jun 15, 2026

A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
09:10

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Area of Science:

  • Computational Biology
  • Quantum Computing
  • Bioinformatics

Background:

  • Quantum parallelism enables exponential speedups in computation.
  • Genetic algorithms are effective for complex optimization problems.
  • Multiple sequence alignment is crucial for understanding protein evolution and function.

Purpose of the Study:

  • To develop a novel quantum-inspired genetic algorithm for multiple sequence alignment.
  • To enhance computational efficiency and accuracy in biological sequence analysis.
  • To explore the application of quantum computing principles in bioinformatics.

Main Methods:

  • Developed QGMALIGN, a probabilistic coding-based quantum-inspired genetic algorithm.
  • Utilized a quantum rotation gate as a mutation operator for quantum state evolution.
  • Designed six genetic operators to optimize solutions during the evolutionary process.

Main Results:

  • QGMALIGN demonstrated competitive performance against established methods like CLUSTALX and SAGA.
  • The algorithm performed well on biological datasets, indicating its practical utility.
  • Incorporating genetic operators reduced the overall running time of the quantum-inspired algorithm.

Conclusions:

  • Quantum-inspired algorithms offer a promising approach for accelerating multiple sequence alignment.
  • QGMALIGN provides an efficient and accurate tool for bioinformatics research.
  • Further integration of quantum principles can enhance computational biology tools.