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Multi-species Conserved Sequences

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Introductory Analysis and Validation of CUT&RUN Sequencing Data
04:58

Introductory Analysis and Validation of CUT&RUN Sequencing Data

Published on: December 13, 2024

A min-cut algorithm for the consistency problem in multiple sequence alignment.

Eduardo Corel1, Florian Pitschi, Burkhard Morgenstern

  • 1Georg-August-Universität, Institut für Mikrobiologie und Genetik, Goldschmidtstrasse 1, 37077 Göttingen, Germany. ecorel@gwdg.de

Bioinformatics (Oxford, England)
|March 2, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel graph-theoretical method for local multiple sequence alignment, improving upon standard pairwise alignment techniques. The new approach enhances accuracy by better identifying weak homologies, outperforming existing methods on benchmark data.

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07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment is crucial for understanding protein and nucleic acid function.
  • Current methods relying solely on pairwise local alignments can miss weak homologies, leading to misalignments.
  • Existing approaches may struggle with incorporating local similarities effectively into a global alignment.

Purpose of the Study:

  • To develop a graph-theoretical approach for improved local multiple sequence similarity detection.
  • To overcome limitations of pairwise-based methods in capturing weak evolutionary relationships.
  • To enhance the accuracy of multiple sequence alignments by integrating local similarities.

Main Methods:

  • Utilizes a graph-theoretical framework based on pairwise alignments generated by DIALIGN.
  • Employs a min-cut algorithm to identify potential alignment columns.
  • Constructs a final multiple alignment using the identified columns.

Main Results:

  • The proposed graph-theoretical method consistently outperforms the standard DIALIGN algorithm.
  • Demonstrated superior performance on both real and simulated benchmark datasets.
  • Successfully identifies and incorporates weak homologies missed by purely pairwise comparisons.

Conclusions:

  • The graph-theoretical approach offers a significant advancement in local multiple sequence alignment.
  • This method provides a more robust and accurate way to construct multiple sequence alignments.
  • The improved alignment accuracy has implications for evolutionary analysis and functional inference.