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TreeSwift: A massively scalable Python tree package
Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328415/ https://www.ncbi.nlm.nih.gov/pubmed/35903557 http://dx.doi.org/10.1016/j.softx.2020.100436 |
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author | Moshiri, N. |
author_facet | Moshiri, N. |
author_sort | Moshiri, N. |
collection | PubMed |
description | Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees. |
format | Online Article Text |
id | pubmed-9328415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93284152022-07-27 TreeSwift: A massively scalable Python tree package Moshiri, N. SoftwareX Article Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees. 2020 2020-03-04 /pmc/articles/PMC9328415/ /pubmed/35903557 http://dx.doi.org/10.1016/j.softx.2020.100436 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Moshiri, N. TreeSwift: A massively scalable Python tree package |
title | TreeSwift: A massively scalable Python tree package |
title_full | TreeSwift: A massively scalable Python tree package |
title_fullStr | TreeSwift: A massively scalable Python tree package |
title_full_unstemmed | TreeSwift: A massively scalable Python tree package |
title_short | TreeSwift: A massively scalable Python tree package |
title_sort | treeswift: a massively scalable python tree package |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328415/ https://www.ncbi.nlm.nih.gov/pubmed/35903557 http://dx.doi.org/10.1016/j.softx.2020.100436 |
work_keys_str_mv | AT moshirin treeswiftamassivelyscalablepythontreepackage |