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Per-Unit Sequence Models01:26

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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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Related Experiment Video

Updated: Nov 18, 2025

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear

Hani Z Girgis1, Benjamin T James2, Brian B Luczak3

  • 1Bioinformatics Toolsmith Laboratory, Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, 700 University Boulevard, Kingsville, TX 78363, USA.

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Summary

A new tool called Identity predicts DNA sequence identity scores efficiently using alignment-free methods. It offers a faster and more accurate alternative to traditional alignment algorithms for large datasets and phylogenetic analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Pairwise global alignment is crucial for sequence analysis but computationally expensive for large datasets.
  • Traditional alignment algorithms have quadratic time complexity, limiting their scalability.
  • Many applications require only identity scores, not full alignment visualizations.

Purpose of the Study:

  • To introduce Identity, a novel tool for calculating pairwise DNA sequence identity scores.
  • To enable efficient analysis of large sequence datasets using alignment-free methods.
  • To provide a faster and scalable alternative to existing sequence alignment tools.

Main Methods:

  • Utilizes alignment-free methods and self-supervised general linear models.
  • Predicts pairwise identity scores in linear time and space complexity.
  • Applied to large-scale sequence databases and bacterial genomes.

Main Results:

  • Identity achieves linear time and space complexity for predicting identity scores.
  • Demonstrates superior speed (2-80x faster) compared to BLAST, Mash, MUMmer4, and USEARCH.
  • Exhibits the best performance for low-identity matches and produces the most accurate phylogenetic trees.
  • Successfully analyzes millions-of-nucleotides-long bacterial genomes, a feat impossible for global-alignment-based tools.

Conclusions:

  • Identity offers a significant advancement in scalable pairwise sequence identity scoring.
  • It provides a robust and efficient solution for large-scale genomic data analysis and phylogenetic reconstruction.
  • The tool is particularly effective for identifying distant evolutionary relationships and analyzing massive genomic datasets.