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dc.contributor.authorSantos, Miguel A.
dc.contributor.authorTurinsky, Andrei L.
dc.contributor.authorOng, Serene
dc.contributor.authorTsai, Jennifer
dc.contributor.authorBerger, Michael F.
dc.contributor.authorBadis, Gwenael
dc.contributor.authorTalukder, Shaheynoor
dc.contributor.authorGehrke, Andrew R.
dc.contributor.authorHughes, Timothy R.
dc.contributor.authorWodak, Shoshana J.
dc.contributor.authorBulyk, Martha L.
dc.date.accessioned2012-06-01T16:35:19Z
dc.date.available2012-06-01T16:35:19Z
dc.date.issued2010-08
dc.date.submitted2010-07
dc.identifier.issn0305-1048
dc.identifier.issn1362-4962
dc.identifier.urihttp://hdl.handle.net/1721.1/70985
dc.description.abstractClassifying proteins into subgroups with similar molecular function on the basis of sequence is an important step in deriving reliable functional annotations computationally. So far, however, available classification procedures have been evaluated against protein subgroups that are defined by experts using mainly qualitative descriptions of molecular function. Recently, in vitro DNA-binding preferences to all possible 8-nt DNA sequences have been measured for 178 mouse homeodomains using protein-binding microarrays, offering the unprecedented opportunity of evaluating the classification methods against quantitative measures of molecular function. To this end, we automatically derive homeodomain subtypes from the DNA-binding data and independently group the same domains using sequence information alone. We test five sequence-based methods, which use different sequence-similarity measures and algorithms to group sequences. Results show that methods that optimize the classification robustness reflect well the detailed functional specificity revealed by the experimental data. In some of these classifications, 73–83% of the subfamilies exactly correspond to, or are completely contained in, the function-based subtypes. Our findings demonstrate that certain sequence-based classifications are capable of yielding very specific molecular function annotations. The availability of quantitative descriptions of molecular function, such as DNA-binding data, will be a key factor in exploiting this potential in the future.en_US
dc.description.sponsorshipCanadian Institutes of Health Research (MOP#82940)en_US
dc.description.sponsorshipSickkids Foundationen_US
dc.description.sponsorshipOntario Research Funden_US
dc.description.sponsorshipNational Science Foundation (U.S.)en_US
dc.description.sponsorshipNational Human Genome Research Institute (U.S.) (R01 HG003985)en_US
dc.language.isoen_US
dc.publisherOxford University Press (OUP)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1093/nar/gkq714en_US
dc.rightsCreative Commons Attribution Non-Commercialen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc/2.5en_US
dc.sourceOxforden_US
dc.titleObjective sequence-based subfamily classifications of mouse homeodomains reflect their in vitro DNA-binding preferencesen_US
dc.typeArticleen_US
dc.identifier.citationSantos, M. A. et al. “Objective Sequence-based Subfamily Classifications of Mouse Homeodomains Reflect Their in Vitro DNA-binding Preferences.” Nucleic Acids Research 38.22 (2010): 7927–7942. Web. 1 June 2012.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.approverBulyk, Martha L.
dc.contributor.mitauthorBulyk, Martha L.
dc.relation.journalNucleic Acids Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsSantos, M. A.; Turinsky, A. L.; Ong, S.; Tsai, J.; Berger, M. F.; Badis, G.; Talukder, S.; Gehrke, A. R.; Bulyk, M. L.; Hughes, T. R.; Wodak, S. J.en
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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