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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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Extraction: Partition and Distribution Coefficients

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Fast Fourier Transform01:10

Fast Fourier Transform

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Bulk Modulus01:21

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A Simple Protocol for Mapping the Plant Root System Architecture Traits
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A motif extraction algorithm based on hashing and modulo-4 arithmetic.

Huitao Sheng1, Kishan Mehrotra, Chilukuri Mohan

  • 1Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA. hsheng@syr.edu

International Journal of Computational Biology and Drug Design
|January 12, 2010
PubMed
Summary

This study introduces a new algorithm for identifying cis-elements in gene promoter regions. The developed computational tool effectively discovers regulatory motifs, outperforming existing methods in simulations and real-world data analysis.

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Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Identifying cis-elements in gene promoter regions is crucial for understanding gene regulation.
  • Existing motif discovery tools face challenges in accuracy and efficiency.

Purpose of the Study:

  • To develop a novel algorithm for accurate identification of cis-elements in coregulated gene promoter regions.
  • To enhance the discovery of functional regulatory motifs in DNA sequences.

Main Methods:

  • Algorithm development utilizing hash tables and modulo arithmetic for efficient subsequence searching.
  • Incorporation of profile matrices and higher-order Markov background models for motif evaluation.
  • Comparative analysis with established tools like MDScan and AlignACE.

Main Results:

  • The developed algorithm demonstrated superior performance in discovering known motifs compared to MDScan and AlignACE in simulations.
  • Application to real biological datasets yielded promising results, identifying numerous known cis-regulatory motifs.
  • The algorithm effectively accounts for sequence variations in motif detection.

Conclusions:

  • The novel algorithm provides a powerful and efficient method for cis-element discovery in promoter regions.
  • This tool has significant potential for advancing research in gene regulation and functional genomics.
  • The findings suggest improved capabilities for motif identification in complex biological sequences.