Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution

dc.contributor.authorMurrell, Ben
dc.contributor.authorWeighill, Thomas
dc.contributor.authorBuys, Jan
dc.contributor.authorKetteringham, Robert
dc.contributor.authorMoola, Sasha
dc.contributor.authorBenade, Gerdus
dc.contributor.authordu Buisson, Lise
dc.contributor.authorKaliski, Daniel
dc.contributor.authorHands, Tristan
dc.contributor.authorScheffler, Konrad
dc.identifier.citationMurrell B, Weighill T, Buys J, Ketteringham R, Moola S, et al. (2011) Non-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolution. PLoS ONE 6(12): e28898.en_ZA
dc.descriptionThe orginal publication is at www.plosone.orgen_ZA
dc.description.abstractModels of protein evolution currently come in two flavors: generalist and specialist. Generalist models (e.g. PAM, JTT, WAG) adopt a one-size-fits-all approach, where a single model is estimated from a number of different protein alignments. Specialist models (e.g. mtREV, rtREV, HIVbetween) can be estimated when a large quantity of data are available for a single organism or gene, and are intended for use on that organism or gene only. Unsurprisingly, specialist models outperform generalist models, but in most instances there simply are not enough data available to estimate them. We propose a method for estimating alignment-specific models of protein evolution in which the complexity of the model is adapted to suit the richness of the data. Our method uses non-negative matrix factorization (NNMF) to learn a set of basis matrices from a general dataset containing a large number of alignments of different proteins, thus capturing the dimensions of important variation. It then learns a set of weights that are specific to the organism or gene of interest and for which only a smaller dataset is available. Thus the alignment-specific model is obtained as a weighted sum of the basis matrices. Having been constrained to vary along only as many dimensions as the data justify, the model has far fewer parameters than would be required to estimate a specialist model. We show that our NNMF procedure produces models that outperform existing methods on all but one of 50 test alignments. The basis matrices we obtain confirm the expectation that amino acid properties tend to be conserved, and allow us to quantify, on specific alignments, how the strength of conservation varies across different properties. We also apply our new models to phylogeny inference and show that the resulting phylogenies are different from, and have improved likelihood over, those inferred under standard models.en_ZA
dc.description.sponsorshipEuropeaid grant number SANTE/2007/174-790 from the European Commission.
dc.description.sponsorshipFunding for the UCSD computing cluster was provided by the Joint DMS/NIGMS Mathematical Biology Initiative through Grant NSF-0714991 and the National Institutes of Health grant AI47745.
dc.format.extent11 p. : col. ill.
dc.subjectProteins -- Separationen_ZA
dc.subjectBiomedical researchen_ZA
dc.subjectGeneralist modelsen_ZA
dc.subjectSpecialist modelsen_ZA
dc.subjectNon-negative matrix factorization (NNMF)en_ZA
dc.subjectAmino acid synthesisen_ZA
dc.titleNon-Negative Matrix Factorization for Learning Alignment-Specific Models of Protein Evolutionen_ZA
dc.description.versionPublisher's versionen_ZA
dc.rights.holderThe authors holds the copyrighten_ZA

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