Profile-based short linear protein motif discovery

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Show simple item record Haslam, Niall J. Shields, Denis C. 2012-09-05T15:37:09Z 2012-09-05T15:37:09Z 2012 Haslam and Shields en 2012-05-18
dc.identifier.citation BMC Bioinformatics en
dc.identifier.issn 1471-2105
dc.description.abstract Background Short linear protein motifs are attracting increasing attention as functionally independent sites, typically 3-10 amino acids in length that are enriched in disordered regions of proteins. Multiple methods have recently been proposed to discover over-represented motifs within a set of proteins based on simple regular expressions. Here, we extend these approaches to profile-based methods, which provide a richer motif representation. Results The profile motif discovery method MEME performed relatively poorly for motifs in disordered regions of proteins. However, when we applied evolutionary weighting to account for redundancy amongst homologous proteins, and masked out poorly conserved regions of disordered proteins, the performance of MEME is equivalent to that of regular expression methods. However, the two approaches returned different subsets within both a benchmark dataset, and a more realistic discovery dataset. Conclusions Profile-based motif discovery methods complement regular expression based methods. Whilst profile-based methods are computationally more intensive, they are likely to discover motifs currently overlooked by regular expression methods. en
dc.description.sponsorship Science Foundation Ireland en
dc.language.iso en en
dc.publisher BioMed Central en
dc.relation.requires Conway Institute Research Collection en
dc.relation.requires Medicine & Medical Science Research Collection en
dc.subject Profile en
dc.subject Linear-motif en
dc.subject Slim en
dc.subject.lcsh Protein-protein interactions en
dc.subject.lcsh Proteins en
dc.title Profile-based short linear protein motif discovery en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.status Peer reviewed en
dc.identifier.volume 13 en
dc.identifier.issue 104 en
dc.identifier.doi 10.1186/1471-2105-13-104
dc.neeo.contributor Haslam|Niall J.|aut|
dc.neeo.contributor Shields|Denis C.|aut|

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