Title
MASCOT: parameter and state inference under the marginal structured coalescent approximation.
Abstract
Motivation: 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. Results: 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.
Year
DOI
Venue
2018
10.1093/bioinformatics/bty406
BIOINFORMATICS
Field
DocType
Volume
Data mining,Coalescent theory,Inference,Source code,Computer science,Population size,Data sequences,Bioinformatics,Mascot
Journal
34
Issue
ISSN
Citations 
22
1367-4803
1
PageRank 
References 
Authors
0.36
5
3
Name
Order
Citations
PageRank
Nicola F. Müller110.36
David A. Rasmussen2224.05
Tanja Stadler311.38