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This document provides a comprehensive guide on the STEM software for inferring maximum likelihood species trees from estimated gene trees under the coalescent model. It includes installation instructions,
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How to fill out stem species tree estimation

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How to fill out STEM: Species Tree Estimation using Maximum Likelihood

01
Gather genetic data from the species of interest.
02
Prepare your data in an appropriate format (e.g., aligned sequences).
03
Choose a suitable model of evolution for your data.
04
Install and configure the STEM software on your computer.
05
Load your data into the STEM interface.
06
Specify the parameters for the analysis, including the number of species and the model parameters.
07
Run the analysis to compute the species tree using Maximum Likelihood.
08
Review the output for the estimated species tree and associated statistics.
09
Interpret the results in the context of your research question.

Who needs STEM: Species Tree Estimation using Maximum Likelihood?

01
Evolutionary biologists conducting phylogenetic studies.
02
Researchers exploring species divergence and relationships.
03
Conservationists needing insights on species genetics.
04
Ecologists studying interspecies interactions.
05
Geneticists focused on understanding hereditary traits.
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STEM is a computational method used to estimate species trees from molecular data by utilizing maximum likelihood techniques, allowing researchers to infer evolutionary relationships among species.
Researchers and scientists involved in phylogenetic studies who are estimating species trees from genetic data are required to use STEM to present their findings.
To fill out STEM, researchers must input their molecular data, specify the model of evolution, and use the software tools associated with maximum likelihood estimation to run the analysis.
The purpose of STEM is to provide an accurate and statistically robust method for inferring species trees, improving our understanding of biodiversity and evolutionary history.
Researchers must report molecular data, model selection, likelihood scores, bootstrap support values, and any relevant metadata that supports the analysis.
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