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Related Concept Videos

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...
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...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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...
Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...

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Related Experiment Video

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Optimised fine and coarse parallelism for sequence homology search.

Xiandong Meng1, Vipin Chaudhary

  • 1Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA. meng@ece.eng.wayne.edu

International Journal of Bioinformatics Research and Applications
|December 1, 2007
PubMed
Summary

Analyzing large biological sequence data is challenging. This study introduces a parallel computing technique for DNA and protein sequence homology searches, significantly improving the Smith-Waterman algorithm

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Biological experiments generate vast amounts of DNA, RNA, and protein sequence data.
  • Analyzing this data using sequence comparison is crucial for understanding biological function.
  • Current analysis methods are often time-consuming, expensive, and impractical due to data volume.

Purpose of the Study:

  • To present an effective technique for sequence homology database searches.
  • To optimize the analysis of large biological sequence datasets.
  • To improve the efficiency of DNA, RNA, and protein sequence comparisons.

Main Methods:

  • Implementation of a technique combining fine and coarse-grain parallelism.
  • Utilizing general-purpose processors for sequence homology database searches.
  • Application of the Smith-Waterman sequence alignment algorithm with multi-level parallel computing.

Main Results:

  • The proposed technique achieves super-linear performance for the Smith-Waterman algorithm.
  • Efficient parallelization is demonstrated on general-purpose processors.
  • The method provides significant performance gains at no additional computational cost.

Conclusions:

  • Parallel computing offers a powerful solution for analyzing large biological sequence datasets.
  • The optimized Smith-Waterman algorithm enhances the efficiency of sequence homology searches.
  • This approach makes the analysis of genomic and proteomic data more practical and cost-effective.