Instability in progressive multiple sequence alignment algorithms
|Title:||Instability in progressive multiple sequence alignment algorithms||Authors:||Boyce, Kieran; Sievers, Fabian; Higgins, Desmond G||Permanent link:||http://hdl.handle.net/10197/7311||Date:||9-Oct-2015||Online since:||2015-12-16T10:39:03Z||Abstract:||Background: Progressive alignment is the standard approach used to align large numbers of sequences. As with all heuristics, this involves a trade off between alignment accuracy and computation time. Results: We examine this trade off 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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||BioMed Central||Journal:||Algorithms for molecular biology||Volume:||10||Issue:||26||Start page:||1||End page:||10||Copyright (published version):||2015 the Authors||Keywords:||Clustal; Kalign; Mafft; Muscle; Pfam; Multiple sequence alignment; Large scale alignment; Sequence order||DOI:||10.1186/s13015-015-0057-1||Language:||en||Status of Item:||Peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Conway Institute Research Collection|
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