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Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...
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Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

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Primer Extension Capture: Targeted Sequence Retrieval from Heavily Degraded DNA Sources
15:28

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Published on: September 3, 2009

essaMEM: finding maximal exact matches using enhanced sparse suffix arrays.

Michaël Vyverman1, Bernard De Baets, Veerle Fack

  • 1Department of Applied Mathematics and Computer Science and Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Ghent B-9000, Belgium. Michael.Vyverman@UGent.be

Bioinformatics (Oxford, England)
|January 26, 2013
PubMed
Summary

We created essaMEM, a faster tool for finding maximal exact matches in genome comparison and read mapping. This bioinformatics method enhances sparse suffix arrays for improved performance without increasing memory usage.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genome comparison and read mapping are crucial in bioinformatics.
  • Efficient algorithms are needed to handle large genomic datasets.
  • Existing methods like sparse suffix arrays offer a balance between speed and memory.

Purpose of the Study:

  • To develop an enhanced tool, essaMEM, for finding maximal exact matches.
  • To improve the speed of maximal exact match finding in sequence data analysis.
  • To maintain or improve the memory efficiency of suffix array implementations.

Main Methods:

  • Development of essaMEM, a tool leveraging sparse suffix arrays.
  • Enhancement of sparse suffix array implementation with a sparse child array.
  • Algorithmic optimization for maximal exact match identification.

Main Results:

  • essaMEM demonstrates significantly faster performance in finding maximal exact matches.
  • The enhanced algorithm maintains the same memory footprint as the original implementation.
  • Sparse suffix arrays with essaMEM remain competitive against compressed suffix arrays.

Conclusions:

  • essaMEM offers a substantial speed improvement for maximal exact match finding.
  • The tool provides an efficient solution for genome comparison and read mapping tasks.
  • This enhancement reinforces the utility of sparse suffix arrays in computational genomics.