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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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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

Learning scoring schemes for sequence alignment from partial examples.

Eagu Kim1, John Kececioglu

  • 1Department of Computer Science, The University of Arizona, Tucson, AZ 85721, USA.

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|November 8, 2008
PubMed
Summary
This summary is machine-generated.

Inverse parametric sequence alignment finds optimal parameters for biological sequence alignment. This method improves multiple sequence alignment accuracy by up to 25 percent, generalizing across protein families.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Accurate biological sequence alignment is crucial for understanding protein function and evolution.
  • Parameter selection, such as gap penalties, significantly impacts alignment outcomes.
  • Existing methods for parameter optimization have limitations, especially with incomplete alignment data.

Purpose of the Study:

  • To develop and evaluate an improved inverse parametric sequence alignment method.
  • To extend inverse parametric alignment to handle partial example alignments.
  • To find parameter values that optimize alignment accuracy across diverse biological sequences.

Main Methods:

  • Utilizing inverse parametric sequence alignment with a novel formulation.
  • Incorporating partial example alignments with unspecified regions.
  • Minimizing the average error between example alignment scores and optimal alignment scores.
  • Conducting experiments on benchmark biological datasets.

Main Results:

  • Successfully identified parameter values that generalize across different protein families.
  • Demonstrated a significant boost in multiple sequence alignment accuracy, up to 25 percent.
  • Validated the effectiveness of the extended inverse parametric alignment approach.

Conclusions:

  • The proposed inverse parametric alignment method provides a robust approach for parameter optimization.
  • Handling partial alignments enhances the applicability and accuracy of the method.
  • This technique offers a significant advancement for multiple sequence alignment in bioinformatics.