Advance Access publication April 25, 2010 | Jeet Sukumaran* and Mark T. Holder
DendroPy is a cross-platform Python library designed for the management, manipulation, and analysis of phylogenetic tree and character data. It supports a wide range of file formats (NEXUS, PHYLIP, FASTA, NeXML, etc.) and provides an object-oriented data model that includes rich taxon objects, trees, and character matrices. DendroPy uses a splits-hash mapping for efficient tree distance calculations and offers simulation routines for various phylogenetic and coalescent models. The library is designed to handle diverse datasets and supports advanced tree operations, such as structural manipulation, tree comparison, and statistical analysis. It also includes user-friendly applications like 'SumTrees' for non-parametric bootstrap support analysis. DendroPy interoperates with other phylogenetic libraries and can exchange data with R-based tools like APE and Geiger. The library is available for download from the Python Package Index and GitHub, and its documentation includes a 'cookbook' and extensive Python 'doctstrings'.DendroPy is a cross-platform Python library designed for the management, manipulation, and analysis of phylogenetic tree and character data. It supports a wide range of file formats (NEXUS, PHYLIP, FASTA, NeXML, etc.) and provides an object-oriented data model that includes rich taxon objects, trees, and character matrices. DendroPy uses a splits-hash mapping for efficient tree distance calculations and offers simulation routines for various phylogenetic and coalescent models. The library is designed to handle diverse datasets and supports advanced tree operations, such as structural manipulation, tree comparison, and statistical analysis. It also includes user-friendly applications like 'SumTrees' for non-parametric bootstrap support analysis. DendroPy interoperates with other phylogenetic libraries and can exchange data with R-based tools like APE and Geiger. The library is available for download from the Python Package Index and GitHub, and its documentation includes a 'cookbook' and extensive Python 'doctstrings'.