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Updated: Jul 7, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

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

Published on: August 16, 2017

Local reliability measures from sets of co-optimal multiple sequence alignments.

Giddy Landan1, Dan Graur

  • 1Department of Biology & Biochemistry, University of Houston, Houston, TX 77204, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|January 31, 2008
PubMed
Summary
This summary is machine-generated.

We developed a new method to quantify uncertainties in multiple sequence alignments. This approach identifies alignment regions with varying reliability, improving the accuracy of evolutionary analyses.

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Jul 7, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group
07:49

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

Published on: August 16, 2017

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Assessing multiple sequence alignment (MSA) quality is crucial for downstream analyses.
  • Practical methods for quantifying alignment reliability in real-world scenarios are limited.

Purpose of the Study:

  • To present a novel method for identifying and quantifying uncertainties in MSAs.
  • To provide practical reliability measures for evaluating alignment quality.

Main Methods:

  • The method quantifies uncertainty by identifying co-optimal alignment regions.
  • For pairwise alignments, it compares high-road and low-road alignments to define the co-optimality envelope.
  • For progressive MSAs, it generates a set of co-optimal alignments to derive local reliability measures.

Main Results:

  • The proposed method effectively quantifies irreducible uncertainty in alignments.
  • Local reliability measures derived from co-optimal alignments can predict and classify alignment errors.
  • Simulation studies confirm the superior performance of these reliability measures.

Conclusions:

  • The developed method offers a practical approach to assess MSA reliability.
  • Quantifying alignment uncertainty enhances the accuracy and trustworthiness of evolutionary inferences.
  • These reliability measures are valuable tools for bioinformaticians and evolutionary biologists.