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Updated: Jun 9, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Automatic detection of anchor points for multiple sequence alignment.

Florian Pitschi1, Claudine Devauchelle, Eduardo Corel

  • 1Partner Institute for Computational Biology, CAS-MPG, 320 Yue Yang Rd, 200031 Shanghai, China.

BMC Bioinformatics
|September 4, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for generating partial alignment columns to improve automated multiple sequence alignment (MSA) accuracy. Incorporating these anchor points enhances the performance of MSA software like ClustalW.

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Published on: August 16, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Automated multiple sequence alignment (MSA) accuracy benefits from pre-defined anchor points.
  • Current methods for identifying anchor points, such as pairwise similarity searches, may not capture complex biological relationships.
  • Multiple local similarities offer a more biologically relevant approach but can be challenging to integrate into alignment ordering.

Purpose of the Study:

  • To develop a method for generating biologically relevant partial alignment columns using multiple local similarities.
  • To introduce a graph-theoretic approach for refining these partial columns by removing inconsistent positions.
  • To evaluate the impact of these refined partial columns as anchor points on MSA accuracy.

Main Methods:

  • Utilized a novel algorithm to detect multiple local similarities, identifying subsets of positions with similar occurrence contexts.
  • Developed a graph-theoretic algorithm to identify and remove inconsistent positions within potential partial alignment columns.
  • Integrated generated partial columns as anchor points into established MSA programs (ClustalW 2.0, DIALIGN 2, T-Coffee).

Main Results:

  • Successfully generated partial alignment columns representing multiple local similarities.
  • The graph-theoretic algorithm effectively identified and removed inconsistent positions, ensuring column integrity.
  • Testing on the BAliBASE 3 benchmark demonstrated the utility of these partial columns with specific MSA programs.

Conclusions:

  • The proposed partial alignment columns, when used as anchor points, significantly improve the accuracy of the ClustalW aligner on the BAliBASE 3 benchmark.
  • This approach offers a more robust method for incorporating biological expertise into automated MSA.
  • The findings suggest broader applicability for guiding other MSA programs.