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

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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

Abstract

Background:

Progressive alignment is the standard approach used to align large numbers of sequences. As with all heuristics, this involves a tradeoff between alignment accuracy and computation time.

Results:

We examine this tradeoff and find that, because of a loss of information in the early steps of the approach, the alignments generated by the most common multiple sequence alignment programs are inherently unstable, and simply reversing the order of the sequences in the input file will cause a different alignment to be generated. Although this effect is more obvious with larger numbers of sequences, it can also be seen with data sets in the order of one hundred sequences. We also outline the means to determine the number of sequences in a data set beyond which the probability of instability will become more pronounced.

Conclusions:

This has major ramifications for both the designers of large-scale multiple sequence alignment algorithms, and for the users of these alignments.

Keywords:

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

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