STEM: species tree estimation using maximum likelihood for gene trees under coalescence. Summary: STEM is a software package written in the C language to obtain maximum likelihood (ML) estimates for phylogenetic species trees given a sample of gene trees under the coalescent model. It includes options to compute the ML species tree, search the space of all species trees for the k trees of highest likelihood and compute ML branch lengths for a user-input species tree. Availability: The STEM package, including source code, is freely available at http://www.stat.osu.edu/ lkubatko/software/STEM/.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Richards, Andrew; Kubatko, Laura: Bayesian-weighted triplet and quartet methods for species tree inference (2021)
- Allman, Elizabeth S.; Long, Colby; Rhodes, John A.: Species tree inference from genomic sequences using the log-det distance (2019)
- Disanto, Filippo; Miglionico, Pasquale; Narduzzi, Guido: On the unranked topology of maximally probable ranked gene tree topologies (2019)
- Allman, Elizabeth S.; Degnan, James H.; Rhodes, John A.: Split probabilities and species tree inference under the multispecies coalescent model (2018)
- Roch, Sebastien; Steel, Mike: Likelihood-based tree reconstruction on a concatenation of aligned sequence data sets can be statistically inconsistent (2015)
- Allman, Elizabeth S.; Degnan, James H.; Rhodes, John A.: Identifying the rooted species tree from the distribution of unrooted gene trees under the coalescent (2011)
- Kubatko, Laura Salter; Carstens, Bryan C.; Knowles, L. Lacey: STEM: species tree estimation using maximum likelihood for gene trees under coalescence (2009) ioport