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Related Concept Videos

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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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.
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Parallel Processing

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Related Experiment Video

Updated: Jun 17, 2026

DNAzyme 10-23 - Based Nanomachines for Nucleic Acid Recognition
07:16

DNAzyme 10-23 - Based Nanomachines for Nucleic Acid Recognition

Published on: February 9, 2024

Evaluating DNA sequence searching algorithms on multicore.

Ning Weng1, Benfano Soewito

  • 1Department of Electrical and Computer Engineering, Southern Illinois University, 1230 Lincoln Drive, Carbondale, IL 62901, USA. weng@engr.siu.edu

International Journal of Computational Biology and Drug Design
|January 9, 2010
PubMed
Summary
This summary is machine-generated.

This study presents an automated method to optimize DNA sequence searching on multicore processors. The approach simplifies complex parallelization for bioengineering applications, enhancing computational efficiency.

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DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
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DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling
08:04

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

Area of Science:

  • Bioengineering
  • Computational Biology
  • Bioinformatics

Background:

  • Sequence searching is a critical operation in bioengineering.
  • Multicore processors offer potential for performance enhancement.
  • Parallelizing uniprocessor-oriented algorithms on multicore systems is challenging.

Purpose of the Study:

  • To present a methodology for profiling DNA sequence search algorithms.
  • To automate the parallelization of these algorithms onto multicore processors.
  • To analytically evaluate the performance of parallelized algorithms.

Main Methods:

  • Profiling processing requirements for DNA sequence search algorithms.
  • Automated parallelization of algorithms onto multicore architectures.
  • Considering three parallelization approaches: queries, database, and task segmentation.

Main Results:

  • Developed an automated methodology requiring minimal user expertise in algorithms or hardware.
  • Enabled efficient employment of multicore technology for DNA sequence searching.
  • Provided a framework for analytical performance evaluation of parallelized algorithms.

Conclusions:

  • The presented methodology effectively automates the parallelization of DNA sequence search algorithms for multicore systems.
  • This approach enhances computational efficiency in bioengineering by leveraging multicore technology.
  • It simplifies the complex task of parallel computing for users, making advanced techniques more accessible.