Systematic exploration of guide-tree topology effects for small protein alignments

Hdl Handle:
http://hdl.handle.net/10147/332668
Title:
Systematic exploration of guide-tree topology effects for small protein alignments
Authors:
Sievers, Fabian; Hughes, Graham M; Higgins, Desmond G
Citation:
BMC Bioinformatics. 2014 Oct 04;15(1):338
Issue Date:
4-Oct-2014
URI:
http://dx.doi.org/10.1186/1471-2105-15-338; http://hdl.handle.net/10147/332668
Abstract:
Abstract Background Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods. Results We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments. Conclusions Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.
Language:
en
Local subject classification:
BIOINFORMATICS

Full metadata record

DC FieldValue Language
dc.contributor.authorSievers, Fabianen_GB
dc.contributor.authorHughes, Graham Men_GB
dc.contributor.authorHiggins, Desmond Gen_GB
dc.date.accessioned2014-10-10T14:40:37Z-
dc.date.available2014-10-10T14:40:37Z-
dc.date.issued2014-10-04-
dc.identifier.citationBMC Bioinformatics. 2014 Oct 04;15(1):338en_GB
dc.identifier.urihttp://dx.doi.org/10.1186/1471-2105-15-338-
dc.identifier.urihttp://hdl.handle.net/10147/332668-
dc.description.abstractAbstract Background Guide-trees are used as part of an essential heuristic to enable the calculation of multiple sequence alignments. They have been the focus of much method development but there has been little effort at determining systematically, which guide-trees, if any, give the best alignments. Some guide-tree construction schemes are based on pair-wise distances amongst unaligned sequences. Others try to emulate an underlying evolutionary tree and involve various iteration methods. Results We explore all possible guide-trees for a set of protein alignments of up to eight sequences. We find that pairwise distance based default guide-trees sometimes outperform evolutionary guide-trees, as measured by structure derived reference alignments. However, default guide-trees fall way short of the optimum attainable scores. On average chained guide-trees perform better than balanced ones but are not better than default guide-trees for small alignments. Conclusions Alignment methods that use Consistency or hidden Markov models to make alignments are less susceptible to sub-optimal guide-trees than simpler methods, that basically use conventional sequence alignment between profiles. The latter appear to be affected positively by evolutionary based guide-trees for difficult alignments and negatively for easy alignments. One phylogeny aware alignment program can strongly discriminate between good and bad guide-trees. The results for randomly chained guide-trees improve with the number of sequences.-
dc.language.isoenen
dc.subject.otherBIOINFORMATICSen_GB
dc.titleSystematic exploration of guide-tree topology effects for small protein alignmentsen_GB
dc.language.rfc3066en-
dc.rights.holderFabian Sievers et al.; licensee BioMed Central Ltd.-
dc.description.statusPeer Reviewed-
dc.date.updated2014-10-08T23:03:57Z-
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