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dc.contributor.authorTopa, Hande
dc.contributor.authorJónás, Ágnes
dc.contributor.authorKofler, Robert
dc.contributor.authorKosiol, Carolin
dc.contributor.authorHonkela, Antti
dc.date.accessioned2017-02-09T13:30:21Z
dc.date.available2017-02-09T13:30:21Z
dc.date.issued2015-06-01
dc.identifier249098690
dc.identifierea59bf6e-c324-4200-b2f2-1d360c02bd11
dc.identifier25614471
dc.identifier84941687465
dc.identifier.citationTopa , H , Jónás , Á , Kofler , R , Kosiol , C & Honkela , A 2015 , ' Gaussian process test for high-throughput sequencing time series : application to experimental evolution ' , Bioinformatics , vol. 31 , no. 11 , pp. 1762-1770 . https://doi.org/10.1093/bioinformatics/btv014en
dc.identifier.issn1367-4803
dc.identifier.otherPubMedCentral: PMC4443671
dc.identifier.urihttps://hdl.handle.net/10023/10258
dc.descriptionThe work was supported under the European ERASysBio+ initiative project ‘SYNERGY’ through the Academy of Finland [135311]. A.H. was also supported by the Academy of Finland [259440] and H.T. was supported by Alfred Kordelin Foundation. R.K. was supported by ERC (ArchAdapt). A.J. is member of the Vienna Graduate School of Population Genetics which is supported by a grant of the Austrian Science Fund (FWF) [W1225-B20].en
dc.description.abstractMOTIVATION: Recent advances in high-throughput sequencing (HTS) have made it possible to monitor genomes in great detail. New experiments not only use HTS to measure genomic features at one time point but also monitor them changing over time with the aim of identifying significant changes in their abundance. In population genetics, for example, allele frequencies are monitored over time to detect significant frequency changes that indicate selection pressures. Previous attempts at analyzing data from HTS experiments have been limited as they could not simultaneously include data at intermediate time points, replicate experiments and sources of uncertainty specific to HTS such as sequencing depth. RESULTS: We present the beta-binomial Gaussian process model for ranking features with significant non-random variation in abundance over time. The features are assumed to represent proportions, such as proportion of an alternative allele in a population. We use the beta-binomial model to capture the uncertainty arising from finite sequencing depth and combine it with a Gaussian process model over the time series. In simulations that mimic the features of experimental evolution data, the proposed method clearly outperforms classical testing in average precision of finding selected alleles. We also present simulations exploring different experimental design choices and results on real data from Drosophila experimental evolution experiment in temperature adaptation. AVAILABILITY AND IMPLEMENTATION: R software implementing the test is available at https://github.com/handetopa/BBGP.
dc.format.extent9
dc.format.extent635024
dc.language.isoeng
dc.relation.ispartofBioinformaticsen
dc.subjectAllelesen
dc.subjectAnimalsen
dc.subjectDrosophilaen
dc.subjectGene frequencyen
dc.subjectGenomicsen
dc.subjectHigh-throughput nucleotide sequencingen
dc.subjectQH301 Biologyen
dc.subjectQH426 Geneticsen
dc.subjectDASen
dc.subjectBDCen
dc.subjectR2Cen
dc.subject.lccQH301en
dc.subject.lccQH426en
dc.titleGaussian process test for high-throughput sequencing time series : application to experimental evolutionen
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1093/bioinformatics/btv014
dc.description.statusPeer revieweden


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