{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T13:49:25Z","timestamp":1767275365197,"version":"3.37.3"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T00:00:00Z","timestamp":1602633600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2019-06131"],"award-info":[{"award-number":["RGPIN-2019-06131"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The combinatorial sequential Monte Carlo (CSMC) has been demonstrated to be an efficient complementary method to the standard Markov chain Monte Carlo (MCMC) for Bayesian phylogenetic tree inference using biological sequences. It is appealing to combine the CSMC and MCMC in the framework of the particle Gibbs (PG) sampler to jointly estimate the phylogenetic trees and evolutionary parameters. However, the Markov chain of the PG may mix poorly for high dimensional problems (e.g. phylogenetic trees). Some remedies, including the PG with ancestor sampling and the interacting particle MCMC, have been proposed to improve the PG. But they either cannot be applied to or remain inefficient for the combinatorial tree space.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We introduce a novel CSMC method by proposing a more efficient proposal distribution. It also can be combined into the PG sampler framework to infer parameters in the evolutionary model. The new algorithm can be easily parallelized by allocating samples over different computing cores. We validate that the developed CSMC can sample trees more efficiently in various PG samplers via numerical experiments.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The implementation of our method and the data underlying this article\u00a0are available at https:\/\/github.com\/liangliangwangsfu\/phyloPMCMC.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa867","type":"journal-article","created":{"date-parts":[[2020,9,24]],"date-time":"2020-09-24T19:20:38Z","timestamp":1600975238000},"page":"642-649","source":"Crossref","is-referenced-by-count":5,"title":["Particle Gibbs sampling for Bayesian phylogenetic inference"],"prefix":"10.1093","volume":"37","author":[{"given":"Shijia","family":"Wang","sequence":"first","affiliation":[{"name":"School of Statistic and Data Science, LPMC and KLMDASR, Nankai University , Nankai Qu 300071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8509-7985","authenticated-orcid":false,"given":"Liangliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Statistics and Actuarial Science, Simon Fraser University , Burnaby, BC V5A 1S6, Canada"}]}],"member":"286","published-online":{"date-parts":[[2020,10,14]]},"reference":[{"key":"2023051704120229900_btaa867-B1","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1111\/j.1467-9868.2009.00736.x","article-title":"Particle Markov chain Monte Carlo methods","volume":"72","author":"Andrieu","year":"2010","journal-title":"J. R. Stat. Soc. B"},{"key":"2023051704120229900_btaa867-B2","doi-asserted-by":"crossref","first-page":"e1006650","DOI":"10.1371\/journal.pcbi.1006650","article-title":"BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis","volume":"15","author":"Bouckaert","year":"2019","journal-title":"PLoS Comput. Biol"},{"key":"2023051704120229900_btaa867-B3","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1093\/sysbio\/syr131","article-title":"Phylogenetic inference via sequential Monte Carlo","volume":"61","author":"Bouchard-C\u00f4t\u00e9","year":"2012","journal-title":"Syst. Biol"},{"key":"2023051704120229900_btaa867-B4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/BF01734101","article-title":"Mitochondrial DNA sequences of primates: tempo and mode of evolution","volume":"18","author":"Brown","year":"1982","journal-title":"J. Mol. Evol"},{"key":"2023051704120229900_btaa867-B5","doi-asserted-by":"crossref","first-page":"2385","DOI":"10.1214\/009053604000000698","article-title":"Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference","volume":"32","author":"Chopin","year":"2004","journal-title":"Ann. Stat"},{"key":"2023051704120229900_btaa867-B6","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1111\/j.1467-9868.2006.00553.x","article-title":"Sequential Monte Carlo samplers","volume":"68","author":"Del Moral","year":"2006","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"2023051704120229900_btaa867-B7","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1093\/sysbio\/syx087","article-title":"Online Bayesian phylogenetic inference: theoretical foundations via sequential Monte Carlo","volume":"67","author":"Dinh","year":"2018","journal-title":"Syst. Biol"},{"volume-title":"Handbook of Nonlinear Filtering","year":"2011","author":"Doucet","key":"2023051704120229900_btaa867-B8"},{"key":"2023051704120229900_btaa867-B9","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3437-9","volume-title":"Sequential Monte Carlo Methods in Practice","author":"Doucet","year":"2001"},{"key":"2023051704120229900_btaa867-B10","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1093\/biomet\/asu075","article-title":"Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator","volume":"102","author":"Doucet","year":"2015","journal-title":"Biometrika"},{"key":"2023051704120229900_btaa867-B11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1186\/1741-7007-8-114","article-title":"Bayesian random local clocks, or one rate to rule them all","volume":"8","author":"Drummond","year":"2010","journal-title":"BMC Biology"},{"key":"2023051704120229900_btaa867-B12","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/s11222-019-09903-y","article-title":"Sequential Bayesian inference for mixture models and the coalescent using sequential Monte Carlo samplers with transformations","volume":"30","author":"Everitt","year":"2020","journal-title":"Stat. Comput"},{"key":"2023051704120229900_btaa867-B13","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1111\/1467-9868.00421","article-title":"On-line inference for hidden Markov models via particle filters","volume":"65","author":"Fearnhead","year":"2003","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"2023051704120229900_btaa867-B14","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/BF01734359","article-title":"Evolutionary trees from DNA sequences: a maximum likelihood approach","volume":"17","author":"Felsenstein","year":"1981","journal-title":"J. Mol. Evol"},{"key":"2023051704120229900_btaa867-B15","first-page":"490","article-title":"Effective online Bayesian phylogenetics via sequential Monte Carlo with guided proposals","author":"Fourment","year":"2017","journal-title":"Syst. Biol"},{"key":"2023051704120229900_btaa867-B16","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1093\/sysbio\/22.3.240","article-title":"Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters","volume":"22","author":"Felsenstein","year":"1973","journal-title":"Syst. Biol"},{"year":"2009","author":"G\u00f6r\u00fcr","key":"2023051704120229900_btaa867-B17"},{"key":"2023051704120229900_btaa867-B18","first-page":"440","article-title":"Scalable inference on Kingman\u2019s coalescent using pair similarity","volume":"22","author":"G\u00f6r\u00fcr","year":"2012","journal-title":"J. Mach. Learn. Res"},{"first-page":"638","year":"2014","author":"Hajiaghayi","key":"2023051704120229900_btaa867-B19"},{"key":"2023051704120229900_btaa867-B20","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1093\/bioinformatics\/17.8.754","article-title":"MRBAYES: Bayesian inference of phylogenetic trees","volume":"17","author":"Huelsenbeck","year":"2001","journal-title":"Bioinformatics"},{"key":"2023051704120229900_btaa867-B21","first-page":"132","article-title":"Evolution of protein molecules","volume":"3","author":"Jukes","year":"1969","journal-title":"Mammalian Protein Metab"},{"key":"2023051704120229900_btaa867-B22","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/BF01731581","article-title":"A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences","volume":"16","author":"Kimura","year":"1980","journal-title":"J. Mol. Evol"},{"key":"2023051704120229900_btaa867-B23","doi-asserted-by":"crossref","first-page":"e1000520","DOI":"10.1371\/journal.pcbi.1000520","article-title":"Bayesian phylogeography finds its roots","volume":"5","author":"Lemey","year":"2009","journal-title":"PLoS Comput. Biol"},{"key":"2023051704120229900_btaa867-B24","first-page":"2145","article-title":"Particle Gibbs with ancestor sampling","volume":"15","author":"Lindsten","year":"2014","journal-title":"J. Mach. Learn. Res"},{"volume-title":"Monte Carlo Strategies in Scientific Computing","year":"2001","author":"Liu","key":"2023051704120229900_btaa867-B25"},{"first-page":"2616","year":"2016","author":"Rainforth","key":"2023051704120229900_btaa867-B26"},{"key":"2023051704120229900_btaa867-B27","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1007\/BFb0102690","volume-title":"Combinatorial Mathematics VI","author":"Robinson","year":"1979"},{"key":"2023051704120229900_btaa867-B28","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/S0022-5193(05)80104-3","article-title":"The general stochastic model of nucleotide substitution","volume":"142","author":"Rodriguez","year":"1990","journal-title":"J. Theor. Biol"},{"key":"2023051704120229900_btaa867-B29","doi-asserted-by":"crossref","first-page":"1572","DOI":"10.1093\/bioinformatics\/btg180","article-title":"MrBayes 3: Bayesian phylogenetic inference under mixed models","volume":"19","author":"Ronquist","year":"2003","journal-title":"Bioinformatics"},{"key":"2023051704120229900_btaa867-B30","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1093\/sysbio\/sys029","article-title":"MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space","volume":"61","author":"Ronquist","year":"2012","journal-title":"Syst. Biol"},{"key":"2023051704120229900_btaa867-B31","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1093\/molbev\/msx124","article-title":"Infectious disease dynamics inferred from genetic data via sequential Monte Carlo","volume":"34","author":"Smith","year":"2017","journal-title":"Mol. Biol. Evol"},{"key":"2023051704120229900_btaa867-B32","doi-asserted-by":"crossref","first-page":"2047","DOI":"10.1093\/bioinformatics\/btl175","article-title":"BAli-Phy: simultaneous Bayesian inference of alignment and phylogeny","volume":"22","author":"Suchard","year":"2006","journal-title":"Bioinformatics"},{"key":"2023051704120229900_btaa867-B33","doi-asserted-by":"crossref","first-page":"vey016","DOI":"10.1093\/ve\/vey016","article-title":"Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10","volume":"4","author":"Suchard","year":"2018","journal-title":"Virus Evol"},{"year":"2008","author":"Teh","key":"2023051704120229900_btaa867-B34"},{"key":"2023051704120229900_btaa867-B35","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1080\/01621459.2015.1054487","article-title":"Bayesian phylogenetic inference using a combinatorial sequential Monte Carlo method","volume":"110","author":"Wang","year":"2015","journal-title":"J. Am. Stat. Assoc"},{"key":"2023051704120229900_btaa867-B36","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1093\/sysbio\/syz028","article-title":"An annealed sequential Monte Carlo method for Bayesian phylogenetics","volume":"69","author":"Wang","year":"2020","journal-title":"Syst. Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaa867\/34841323\/btaa867.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/5\/642\/50357504\/btaa867.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/5\/642\/50357504\/btaa867.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,17]],"date-time":"2023-05-17T04:12:51Z","timestamp":1684296771000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/5\/642\/5921169"}},"subtitle":[],"editor":[{"given":"Schwartz","family":"Russell","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,10,14]]},"references-count":36,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2021,5,5]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaa867","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2021,3,1]]},"published":{"date-parts":[[2020,10,14]]}}}