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Sequence Networks of Rotating Machines01:24

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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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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MetaLogo: a heterogeneity-aware sequence logo generator and aligner.

Yaowen Chen1, Zhen He1, Yahui Men1

  • 1Center for Computational Biology, Beijing Institute of Basic Medical Sciences, Beijing, China.

Briefings in Bioinformatics
|February 2, 2022
PubMed
Summary
This summary is machine-generated.

MetaLogo addresses sequence logo limitations by clustering heterogeneous sequences, revealing specific motifs. This tool enhances the visualization of conserved patterns across diverse DNA and protein sequence sets.

Keywords:
MetaLogologo alignmentmultiple sequence logosphylogenetic treeweb server

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence logos are vital for visualizing conserved patterns in DNA and protein sequences.
  • Existing tools often overlook sequence heterogeneity, limiting motif discovery in diverse datasets.
  • Heterogeneity can arise from different evolutionary origins or functional gene families.

Purpose of the Study:

  • To develop a novel tool, MetaLogo, for generating sequence logos from heterogeneous sequence sets.
  • To enable the identification of specific sequence motifs and conservation patterns within distinct sequence groups.
  • To facilitate the annotation of evolutionary positions and gene functions for unknown sequences.

Main Methods:

  • Automated clustering of sequences post-multiple sequence alignment and phylogenetic tree construction.
  • Generation of aligned sequence logos for multiple identified groups.
  • Support for user-defined grouping for fine-grained motif analysis.

Main Results:

  • MetaLogo effectively clusters heterogeneous sequences and generates distinct sequence logos.
  • The tool highlights both homologous and non-homologous sites within sequence alignments.
  • It provides a user-friendly platform for creating customized and informative sequence logos.

Conclusions:

  • MetaLogo overcomes limitations of traditional sequence logo generators by handling sequence heterogeneity.
  • The tool offers a dynamic perspective for investigating functional motifs and evolutionary characteristics.
  • MetaLogo is accessible via a web server and a Python package, empowering users without programming expertise.