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Updated: Apr 1, 2026

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

Instability in progressive multiple sequence alignment algorithms.

Kieran Boyce¹, Fabian Sievers¹, Desmond G Higgins¹

¹Conway Institute of Biomolecular and Biomedical Research and UCD School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland.

Algorithms for Molecular Biology : AMB

|October 13, 2015

Summary

This summary is machine-generated.

Progressive alignment, a common method for aligning many sequences, suffers from instability due to information loss. Reversing sequence order can alter results, impacting large-scale multiple sequence alignment (MSA) design and use.

Keywords:

Clustal Kalign Large scale alignment Mafft Multiple sequence alignment Muscle Pfam Sequence order

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Progressive alignment is a standard heuristic for aligning large sequence datasets.
This method balances alignment accuracy with computational time.
Heuristics inherently involve trade-offs.

Purpose of the Study:

To examine the tradeoff between accuracy and computation time in progressive alignment.
To identify and explain the inherent instability in common multiple sequence alignment (MSA) programs.
To provide a method for determining when sequence dataset size increases instability.

Main Methods:

Analysis of the information loss in early stages of progressive alignment.
Testing the effect of input sequence order on alignment outcomes.
Developing criteria to predict instability based on dataset size.

Main Results:

Common MSA programs produce inherently unstable alignments due to early information loss.
Reversing the input sequence order demonstrably alters alignment results.
Instability is observable even with datasets of around one hundred sequences.
A method is proposed to identify dataset sizes prone to increased instability.

Conclusions:

The inherent instability of progressive alignment has significant implications for algorithm design.
Users of large-scale MSA tools must be aware of potential alignment variability.
Findings necessitate re-evaluation of current MSA practices and algorithm development.