{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T04:41:33Z","timestamp":1784868093748,"version":"3.55.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2016,11,3]],"date-time":"2016-11-03T00:00:00Z","timestamp":1478131200000},"content-version":"vor","delay-in-days":652,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Motivation: 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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Availability and implementation: R software implementing the test is available at https:\/\/github.com\/handetopa\/BBGP .<\/jats:p>\n               <jats:p>Contact: \u00a0hande.topa@aalto.fi , agnes.jonas@vetmeduni.ac.at , carolin.kosiol@vetmeduni.ac.at , antti.honkela@hiit.fi<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv014","type":"journal-article","created":{"date-parts":[[2015,1,23]],"date-time":"2015-01-23T04:40:38Z","timestamp":1421988038000},"page":"1762-1770","source":"Crossref","is-referenced-by-count":47,"title":["Gaussian process test for high-throughput sequencing time series: application to experimental evolution"],"prefix":"10.1093","volume":"31","author":[{"given":"Hande","family":"Topa","sequence":"first","affiliation":[{"name":"1 Helsinki Institute for Information Technology (HIIT), Department of Information and Computer Science, Aalto University, Espoo, Finland, 2 Institut f\u00fcr Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria, 3 Vienna Graduate School of Population Genetics, Wien, Austria and 4 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c1gnes","family":"J\u00f3n\u00e1s","sequence":"additional","affiliation":[{"name":"1 Helsinki Institute for Information Technology (HIIT), Department of Information and Computer Science, Aalto University, Espoo, Finland, 2 Institut f\u00fcr Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria, 3 Vienna Graduate School of Population Genetics, Wien, Austria and 4 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland"},{"name":"1 Helsinki Institute for Information Technology (HIIT), Department of Information and Computer Science, Aalto University, Espoo, Finland, 2 Institut f\u00fcr Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria, 3 Vienna Graduate School of Population Genetics, Wien, Austria and 4 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Kofler","sequence":"additional","affiliation":[{"name":"1 Helsinki Institute for Information Technology (HIIT), Department of Information and Computer Science, Aalto University, Espoo, Finland, 2 Institut f\u00fcr Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria, 3 Vienna Graduate School of Population Genetics, Wien, Austria and 4 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carolin","family":"Kosiol","sequence":"additional","affiliation":[{"name":"1 Helsinki Institute for Information Technology (HIIT), Department of Information and Computer Science, Aalto University, Espoo, Finland, 2 Institut f\u00fcr Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria, 3 Vienna Graduate School of Population Genetics, Wien, Austria and 4 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antti","family":"Honkela","sequence":"additional","affiliation":[{"name":"1 Helsinki Institute for Information Technology (HIIT), Department of Information and Computer Science, Aalto University, Espoo, Finland, 2 Institut f\u00fcr Populationsgenetik, Vetmeduni Vienna, 1210 Wien, Austria, 3 Vienna Graduate School of Population Genetics, Wien, Austria and 4 Helsinki Institute for Information Technology (HIIT), Department of Computer Science, University of Helsinki, Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,1,21]]},"reference":[{"key":"2023051505243129600_btv014-B1","doi-asserted-by":"crossref","DOI":"10.1002\/0471249688","volume-title":"Categorical Data Analysis","author":"Agresti","year":"2002"},{"key":"2023051505243129600_btv014-B2","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1093\/bioinformatics\/btt130","article-title":"Sorad: a systems biology approach to predict and modulate dynamic signaling pathway response from phosphoproteome time-course measurements","volume":"29","author":"\u00c4ij\u00f6","year":"2013","journal-title":"Bioinformatics"},{"key":"2023051505243129600_btv014-B3","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1093\/molbev\/msu048","article-title":"The power to detect quantitative trait loci using resequenced, experimentally evolved populations of diploid, sexual organisms","volume":"31","author":"Baldwin-Brown","year":"2014","journal-title":"Mol. Biol. Evol."},{"key":"2023051505243129600_btv014-B4","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1038\/nature08480","article-title":"Genome evolution and adaptation in a long-term experiment with \n              Escherichia coli","volume":"461","author":"Barrick","year":"2009","journal-title":"Nature"},{"key":"2023051505243129600_btv014-B5","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1534\/genetics.107.085019","article-title":"Estimation of 2Nes from temporal allele frequency data","volume":"179","author":"Bollback","year":"2008","journal-title":"Genetics"},{"key":"2023051505243129600_btv014-B6","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1038\/nature09352","article-title":"Genome-wide analysis of a long-term evolution experiment with Drosophila","volume":"467","author":"Burke","year":"2010","journal-title":"Nature"},{"key":"2023051505243129600_btv014-B7","first-page":"4913","article-title":"What paths do advantageous alleles take during short-term evolutionary change? \n              Mol","volume":"21","author":"Burke","year":"2012","journal-title":"Ecol."},{"key":"2023051505243129600_btv014-B8","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1186\/1471-2105-12-399","article-title":"Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements","volume":"12","author":"Cooke","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023051505243129600_btv014-B9","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.gene.2010.04.015","article-title":"Drosophila melanogaster\n               recombination rate calculator","volume":"463","author":"Fiston-Lavier","year":"2010","journal-title":"Gene"},{"key":"2023051505243129600_btv014-B10","doi-asserted-by":"crossref","first-page":"i70","DOI":"10.1093\/bioinformatics\/btn278","article-title":"Gaussian process modelling of latent chemical species: applications to inferring transcription factor activities","volume":"24","author":"Gao","year":"2008","journal-title":"Bioinformatics"},{"key":"2023051505243129600_btv014-B11","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1186\/1471-2105-14-252","article-title":"Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters","volume":"14","author":"Hensman","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2023051505243129600_btv014-B12","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1017\/S0016672300010156","article-title":"The effect of linkage on limits to artificial selection","volume":"8","author":"Hill","year":"1966","journal-title":"Genet. Res."},{"key":"2023051505243129600_btv014-B13","doi-asserted-by":"crossref","first-page":"7793","DOI":"10.1073\/pnas.0914285107","article-title":"Model-based method for transcription factor target identification with limited data","volume":"107","author":"Honkela","year":"2010","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"2023051505243129600_btv014-B14","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1093\/molbev\/msr289","article-title":"Quantifying selection acting on a complex trait using allele frequency time series data","volume":"29","author":"Illingworth","year":"2012","journal-title":"Mol. Biol. Evol."},{"key":"2023051505243129600_btv014-B15","doi-asserted-by":"crossref","first-page":"20120616","DOI":"10.1098\/rsif.2012.0616","article-title":"Evolutionary inference for function-valued traits: Gaussian process regression on phylogenies","volume":"10","author":"Jones","year":"2013","journal-title":"J. R. Soc. Interface"},{"key":"2023051505243129600_btv014-B16","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1186\/1471-2105-12-180","article-title":"A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression","volume":"12","author":"Kalaitzis","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"2023051505243129600_btv014-B17","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.tree.2012.06.001","article-title":"Experimental evolution","volume":"27","author":"Kawecki","year":"2012","journal-title":"Trends. Ecol. Evol."},{"key":"2023051505243129600_btv014-B18","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1093\/bioinformatics\/btp139","article-title":"Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data","volume":"25","author":"Kirk","year":"2009","journal-title":"Bioinformatics"},{"key":"2023051505243129600_btv014-B19","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1093\/molbev\/mst221","article-title":"A guide for the design of evolve and resequencing studies","volume":"31","author":"Kofler","year":"2014","journal-title":"Mol. Biol. Evol."},{"key":"2023051505243129600_btv014-B20","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1093\/bioinformatics\/btr589","article-title":"PoPoolation2: identifying differentiation between populations using sequencing of pooled DNA samples (Pool-Seq)","volume":"27","author":"Kofler","year":"2011","journal-title":"Bioinformatics"},{"key":"2023051505243129600_btv014-B21","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1038\/nature12344","article-title":"Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations","volume":"500","author":"Lang","year":"2013","journal-title":"Nature"},{"key":"2023051505243129600_btv014-B22","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1093\/bioinformatics\/btq022","article-title":"Estimating replicate time shifts using Gaussian process regression","volume":"26","author":"Liu","year":"2010","journal-title":"Bioinformatics"},{"key":"2023051505243129600_btv014-B23","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1093\/bioinformatics\/btr658","article-title":"Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile","volume":"28","author":"Liu","year":"2012","journal-title":"Bioinformatics"},{"key":"2023051505243129600_btv014-B24","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511809071","volume-title":"Introduction to Information Retrieval","author":"Manning","year":"2008"},{"key":"2023051505243129600_btv014-B25","doi-asserted-by":"crossref","first-page":"4931","DOI":"10.1111\/j.1365-294X.2012.05673.x","article-title":"Adaptation of Drosophila to a novel laboratory environment reveals temporally heterogeneous trajectories of selected alleles","volume":"21","author":"Orozco-terWengel","year":"2012","journal-title":"Mol. Ecol."},{"key":"2023051505243129600_btv014-B26","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1111\/biom.12003","article-title":"Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies","volume":"69","author":"Palacios","year":"2013","journal-title":"Biometrics"},{"key":"2023051505243129600_btv014-B27","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen","year":"2006"},{"key":"2023051505243129600_btv014-B28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1089\/cmb.2009.0175","article-title":"A robust Bayesian two-sample test for detecting intervals of differential gene expression in microarray time series","volume":"17","author":"Stegle","year":"2010","journal-title":"J. Comput. Biol."},{"key":"2023051505243129600_btv014-B29","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/1752-0509-6-53","article-title":"Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison","volume":"6","author":"Titsias","year":"2012","journal-title":"BMC Syst. Biol."},{"key":"2023051505243129600_btv014-B30","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1093\/molbev\/mst205","article-title":"Massive habitat-specific genomic response in \n              D. melanogaster\n               populations during experimental evolution in hot and cold environments","volume":"31","author":"Tobler","year":"2014","journal-title":"Mol. Biol. Evol."},{"key":"2023051505243129600_btv014-B31","doi-asserted-by":"crossref","first-page":"e1001336","DOI":"10.1371\/journal.pgen.1001336","article-title":"Population-based resequencing of experimentally evolved populations reveals the genetic basis of body size variation in \n              Drosophila melanogaster","volume":"7","author":"Turner","year":"2011","journal-title":"PLoS Genet."},{"key":"2023051505243129600_btv014-B32","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1016\/j.csda.2005.11.017","article-title":"Flexible temporal expression profile modelling using the Gaussian process","volume":"51","author":"Yuan","year":"2006","journal-title":"Comput. Statist. Data Anal."},{"key":"2023051505243129600_btv014-B33","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1073\/pnas.1010643108","article-title":"Experimental selection of hypoxia-tolerant \n              Drosophila melanogaster","volume":"7","author":"Zhou","year":"2011","journal-title":"Proc. Natl Acad. Sci. USA"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/11\/1762\/50314391\/bioinformatics_31_11_1762.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/31\/11\/1762\/50314391\/bioinformatics_31_11_1762.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T05:25:51Z","timestamp":1684128351000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/31\/11\/1762\/2364815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,1,21]]},"references-count":33,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2015,6,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btv014","relation":{},"ISSN":["1367-4811","1367-4803"],"issn-type":[{"value":"1367-4811","type":"electronic"},{"value":"1367-4803","type":"print"}],"subject":[],"published-other":{"date-parts":[[2015,6,1]]},"published":{"date-parts":[[2015,1,21]]}}}