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Updated: Apr 26, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

ReformAlign: improved multiple sequence alignments using a profile-based meta-alignment approach.

Dimitrios P Lyras¹, Dirk Metzler

¹Faculty of Biology, Department II, Ludwig-Maximilians Universität München, Planegg-Martinsried 82152, Germany. lyras@biologie.uni-muenchen.de.

BMC Bioinformatics

|August 8, 2014

Summary

This summary is machine-generated.

ReformAlign, a novel meta-alignment method, enhances biological sequence alignment accuracy by re-aligning sequences against a summary profile. This approach improves upon existing methods, especially for lower-quality initial alignments.

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Last Updated: Apr 26, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis

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Published on: July 12, 2022

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

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

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:

Accurate sequence alignment is crucial for biological data analysis.
Multiple sequence alignment is computationally intractable, leading to heuristic methods.
Existing heuristic methods lack flexibility and can compromise alignment quality.

Purpose of the Study:

Introduce ReformAlign, a novel meta-alignment approach to improve alignment quality.
Address limitations of progressive alignment methods by offering flexibility.
Enhance the accuracy of alignments generated by popular aligners.

Main Methods:

ReformAlign is a meta-aligner requiring an initial alignment.
Constructs a profile from the initial alignment.
Re-aligns each sequence individually against the profile.
Infers the final alignment by merging individual sub-alignments.

Main Results:

ReformAlign significantly improves alignment accuracy compared to starting methods.
Performance gains are more substantial with lower-quality initial alignments.
Demonstrated effectiveness across ten leading alignment methods with real-world data.

Conclusions:

ReformAlign is a promising profile-based meta-alignment approach.
Computationally efficient and compatible with various alignment methods.
Offers significant improvements in generated biological sequence alignments.