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Protein Networks

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Maxam-Gilbert Sequencing

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
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Quantum computing in bioinformatics: a systematic review mapping.

Katarzyna Nałęcz-Charkiewicz¹, Kamil Charkiewicz², Robert M Nowak¹

¹Artificial Intelligence Division, Institute of Computer Science, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.

Briefings in Bioinformatics

|August 14, 2024

Summary

This summary is machine-generated.

Quantum computing (QC) is advancing bioinformatics, offering new algorithms and methods. This review maps current QC in bioinformatics, guiding future research and highlighting limitations.

Keywords:

bioinformatics mapping review quantum computing

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Area of Science:

Bioinformatics and Computational Biology
Quantum Information Science

Background:

Classical algorithms in bioinformatics face limitations with increasing data complexity.
Quantum computing (QC) presents a novel paradigm for computational challenges in life sciences.
The intersection of QC and bioinformatics is an emerging research frontier.

Purpose of the Study:

To provide a comprehensive mapping review of QC methods and algorithms in bioinformatics.
To identify current state-of-the-art solutions and future research directions.
To highlight limitations of existing QC approaches in the bioinformatics domain.

Main Methods:

Systematic literature review of QC applications in bioinformatics.
Classification and description of identified QC algorithms and methods.
Analysis of advantages and disadvantages of current approaches.

Main Results:

A broad overview of QC methods applied to bioinformatics problems.
Identification of key research areas and trends at the QC-bioinformatics interface.
A categorized list of QC algorithms with detailed descriptions, classifications, and comparative analysis.

Conclusions:

Quantum computing holds significant potential to revolutionize bioinformatics.
Further research is needed to overcome current limitations and fully leverage QC capabilities.
This review serves as a foundational resource for researchers entering this interdisciplinary field.