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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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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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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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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...
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Related Experiment Video

Updated: Mar 28, 2026

Novel Sequence Discovery by Subtractive Genomics
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Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

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SW#db: GPU-Accelerated Exact Sequence Similarity Database Search.

Matija Korpar1, Martin Šošić1, Dino Blažeka1

  • 1University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, HR 10000 Zagreb, Croatia.

Plos One
|January 1, 2016
PubMed
Summary

SW#db accelerates protein similarity searches by enabling fast, exact local alignments on reduced databases. This tool leverages GPU and CPU parallelization for significant speedups compared to existing methods.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The exponential growth in sequencing data necessitates efficient protein similarity search algorithms.
  • Exact local alignment methods like Smith-Waterman are computationally intensive, posing challenges for large-scale database searches.
  • Current heuristic-based approaches reduce candidate sequences but still require efficient alignment phases.

Purpose of the Study:

  • To introduce SW#db, a novel tool and library for rapid, exact protein similarity searching.
  • To provide an efficient solution for the local alignment phase after database reduction.
  • To enhance the speed and sensitivity of large-scale sequence database analysis.

Main Methods:

  • Development of the SW#db tool utilizing both GPU and CPU parallelization.
  • Implementation of exact local alignment algorithms optimized for speed.
  • Benchmarking SW#db against established tools like SSEARCH, CUDASW++, and SSW.

Main Results:

  • SW#db demonstrates significant speed improvements, being 4-5x faster than SSEARCH, 6-25x faster than CUDASW++, and over 20x faster than SSW.
  • The tool achieves performance comparable to BLAST as a standalone tool.
  • SW#db excels in the critical exact local alignment phase for pre-filtered databases.

Conclusions:

  • SW#db offers a substantial advancement in the speed of exact local protein similarity searches.
  • Its parallel processing capabilities make it highly effective for analyzing large biological sequence datasets.
  • The tool is a valuable addition for researchers needing efficient and sensitive sequence alignment in bioinformatics pipelines.