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

Grammar-based distance in progressive multiple sequence alignment.

David J Russell1, Hasan H Otu, Khalid Sayood

  • 1Department of Electrical Engineering, University of Nebraska-Lincoln, 209N WSEC, Lincoln, NE 68588-0511, USA. drussell@engr.unl.edu

BMC Bioinformatics
|July 12, 2008
PubMed
Summary
This summary is machine-generated.

A new multiple sequence alignment (MSA) algorithm offers comparable alignment quality to existing methods but significantly reduces execution time, especially for large biological datasets.

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An Integrated Approach for Microprotein Identification and Sequence Analysis
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple Sequence Alignment (MSA) is crucial for understanding biological sequence relationships.
  • Existing MSA algorithms face challenges with large datasets, impacting execution time and scalability.

Purpose of the Study:

  • To introduce and evaluate a novel, computationally efficient MSA algorithm.
  • To compare the proposed algorithm's alignment quality and execution speed against established methods.

Main Methods:

  • The proposed algorithm employs a grammar-based distance metric for progressive alignment.
  • Sequences are progressively aligned in pairs, integrating new sequences with existing alignments.

Main Results:

  • The new algorithm demonstrates alignment quality comparable to ClustalW, T-Coffee, MAFFT, MUSCLE, Kalign, and PSAlign.
  • Significant improvements in running time were observed, particularly for large-scale sequence datasets.
  • Validation was performed using the BAliBASE 3.0 database and synthetically generated long sequences.

Conclusions:

  • A computationally efficient progressive alignment algorithm has been developed.
  • The grammar-based distance metric makes this algorithm particularly suitable for aligning large biological datasets.