DendroPy: a Python library for phylogenetic computing

DendroPy: a Python library for phylogenetic computing

Advance Access publication April 25, 2010 | Jeet Sukumaran* and Mark T. Holder
DendroPy is a cross-platform Python library designed for managing, manipulating, and analyzing 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 tree manipulation, structural changes, and tree simulation. It also includes user-friendly applications like 'SumTrees' for summarizing support for splits or clades. DendroPy interoperates with other libraries such as ETE, PyCogent, and APE, facilitating seamless data exchange and integration. The library is available for download and installation through the Python Package Index and GitHub.DendroPy is a cross-platform Python library designed for managing, manipulating, and analyzing 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 tree manipulation, structural changes, and tree simulation. It also includes user-friendly applications like 'SumTrees' for summarizing support for splits or clades. DendroPy interoperates with other libraries such as ETE, PyCogent, and APE, facilitating seamless data exchange and integration. The library is available for download and installation through the Python Package Index and GitHub.
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