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Multiple DNA and protein sequence alignment based on segment-to-segment comparison

B Morgenstern1, A Dress, T Werner

  • 1National Research Center for Environment and Health, Institute of Mammalian Genetics, Neuherberg, Germany.

Proceedings of the National Academy of Sciences of the United States of America
|October 29, 1996
PubMed
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This study introduces a novel alignment method for DNA and protein sequences, focusing on segment similarity rather than individual residues. This approach simplifies gap handling and improves accuracy in identifying functional sites, even in low-similarity sequences.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional sequence alignment methods often rely on residue-to-residue comparisons and explicit gap penalties.
  • These methods can struggle with sequences exhibiting low overall similarity or introduce complexities in parameter selection.

Purpose of the Study:

  • To propose a new framework for sequence alignment based on equivalence relations and segment similarity.
  • To develop an alignment algorithm that avoids explicit gap penalties and improves accuracy in identifying conserved regions.

Main Methods:

  • Developed a novel alignment algorithm based on segment-to-segment comparison.
  • Defined alignments as consistent equivalence relations on sequence positions.
  • Gaps are implicitly handled as unaligned sequence portions.

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Main Results:

  • Successfully aligned functional sites in low-similarity DNA sequences (helix-loop-helix proteins), a unique outcome among tested methods.
  • Identified reading frames as a byproduct of DNA sequence alignment.
  • Achieved high scores in aligning ribonuclease H protein sequences, comparable to other leading methods.
  • Demonstrated parameter-independent protein alignments, unlike other approaches.

Conclusions:

  • The proposed segment-based alignment method offers a robust alternative to residue-based approaches.
  • This method effectively identifies conserved functional regions and simplifies alignment parameterization.
  • It shows promise for analyzing diverse biological sequence datasets, including those with low similarity.