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Institute for Computational and Experimental Research in Mathematics (ICERM)

This semester program brings together mathematicians, statisticians, computer scientists, and experimental biologists to address the challenges involved in genome-scale phylogenetic inference. An overarching goal is to unite biologically-sound modeling with algorithms that can be implemented in a computationally tractable manner and that scale as both the number of species and the amount of genetic data increase. Specific topics to be investigated include: theory and practice of model-based inference for trees and networks, combinatorial and algebraic approaches for inferring phylogenies, software and algorithm development for inference and visualization, Bayesian approaches to phylogenetic inference, models for phylogenetic diversity and node dating, and methods for assessing model fit.

ICERM;     Email.: info@icerm.brown.edu

biology, computation, genomic data, phylogenomics, software development, algorithm development

Institute for Computational and Experimental Research in Mathematics (ICERM)

In this workshop, we will consider challenges and best practices in translating theory to software for phylogenetic inference. We will discuss strategies adopted by the tool development community, their relative advantages, and lessons learned through software development. We will discuss a variety of objective functions (parametric and non-parametric) and algorithmic techniques used by inference methods. We will cover parallelization and how it interacts with the choice of objective function and algorithmic technique. Finally, we will discuss best practices for evaluating new and existing software tools, not just to validate the implementation but also to study accuracy and scalability in relation to theory and usage in realistic bioinformatics pipelines. In addition to discussing how theory influences tools, we will examine how tool development can inform the theoretical questions asked.

ICERM;     Email.: info@icerm.brown.edu

bioinformatics, phylogenetic inference, software development