{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T01:59:13Z","timestamp":1776131953740,"version":"3.50.1"},"reference-count":25,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2018,5,22]],"date-time":"2018-05-22T00:00:00Z","timestamp":1526947200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008367","name":"SNF","doi-asserted-by":"publisher","award":["CR32I3_166258"],"award-info":[{"award-number":["CR32I3_166258"]}],"id":[{"id":"10.13039\/100008367","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ETH Z\u00fcrich Postdoctoral Fellowship Program"},{"name":"Marie Curie Actions for People COFUND Program"},{"DOI":"10.13039\/100010663","name":"European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Seventh Framework Programme of the European Commission","award":["335529"],"award-info":[{"award-number":["335529"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,11,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>The structured coalescent is widely applied to study demography within and migration between sub-populations from genetic sequence data. Current methods are either exact but too computationally inefficient to analyse large datasets with many sub-populations, or make strong approximations leading to severe biases in inference. We recently introduced an approximation based on weaker assumptions to the structured coalescent enabling the analysis of larger datasets with many different states. We showed that our approximation provides unbiased migration rate and population size estimates across a wide parameter range.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We extend this approach by providing a new algorithm to calculate the probability of the state of internal nodes that includes the information from the full phylogenetic tree. We show that this algorithm is able to increase the probability attributed to the true sub-population of a node. Furthermore we use improved integration techniques, such that our method is now able to analyse larger datasets, including a H3N2 dataset with 433 sequences sampled from five different locations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The presented methods are part of the BEAST2 package MASCOT, the Marginal Approximation of the Structured COalescenT. This package can be downloaded via the BEAUti package manager. The source code is available at https:\/\/github.com\/nicfel\/Mascot.git.<\/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\/bty406","type":"journal-article","created":{"date-parts":[[2018,5,16]],"date-time":"2018-05-16T07:09:43Z","timestamp":1526454583000},"page":"3843-3848","source":"Crossref","is-referenced-by-count":119,"title":["MASCOT: parameter and state inference under the marginal structured coalescent approximation"],"prefix":"10.1093","volume":"34","author":[{"given":"Nicola F","family":"M\u00fcller","sequence":"first","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"},{"name":"Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland"}]},{"given":"David","family":"Rasmussen","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"},{"name":"Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland"},{"name":"Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC, USA"},{"name":"Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA"}]},{"given":"Tanja","family":"Stadler","sequence":"additional","affiliation":[{"name":"Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland"},{"name":"Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2018,5,22]]},"reference":[{"key":"2023012712352727800_bty406-B1","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1093\/sysbio\/syx060","article-title":"Taming the BEAST\u2014A community teaching material resource for BEAST 2","volume":"67","author":"Barido-Sottani","year":"2017","journal-title":"Systemat. 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