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  • program_project
  • nrc
  • whocc
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  • tool
  • patent
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  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Full Professor
  • Graduate Student
  • Lab assistant
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  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
  • Operational and administrative manager
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Scientific Fields
Diseases
Organisms
Applications
Technique
Starting Date
19
Jul 2018
Status
Ongoing
Members
3
Structures
2

About

PastML is a tool for ancestral character reconstruction on phylogenetic trees.

The reconstruction of ancestral scenarios is widely used to study the evolution of characters along a phylogenetic tree, for example to infer ancestral molecular characters and their changes in time, or in phylogeography to trace back the geographical locations and moves of species. Standard methods are based on parsimony and likelihood. In the likelihood framework one assumes a probabilistic evolutionary model and commonly uses the marginal posterior probabilities of the character states, and the joint reconstruction of the most likely scenario. Both approaches are somewhat unsatisfactory. Marginal reconstructions provide users with state probabilities, but these are difficult to interpret and use, while joint reconstructions select a unique state for every tree node and thus do not reflect the uncertainty of inferences.

We propose a fast and simple approach, which is in between these two extremes. We use decision-theory concepts and the Brier criterion to associate each node in the tree to a set of likely states. In the tree regions where the uncertainty is low, a unique state is predicted for the nodes. In the uncertain parts, typically around the tree root, several states are predicted. The algorithm has linear time complexity and applies to very large trees. To visualize the results, we cluster the neighboring nodes associated to the same states and use graph visualization tools.

Try PastML at pastml.pasteur.fr!