Treemmer: a tool to reduce large phylogenetic datasets with minimal loss of diversity

Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Here we present Treemmer, a...

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Bibliographic Details
Published in:BMC bioinformatics 2018-05, Vol.19 (1), p.164-164, Article 164
Main Authors: Menardo, Fabrizio, Loiseau, Chloé, Brites, Daniela, Coscolla, Mireia, Gygli, Sebastian M, Rutaihwa, Liliana K, Trauner, Andrej, Beisel, Christian, Borrell, Sonia, Gagneux, Sebastien
Format: Article
Language:English
Subjects:
Algorithms
Biogeography
Clone elimination
Computational Biology - methods
Control
Databases, Genetic
DNA sequencing
Humans
Influenza A virus - genetics
Information Storage and Retrieval
Large phylogenetic trees
Mycobacterium tuberculosis - genetics
Nucleotide sequencing
Phylogenetic trees
Phylogeny
Redundancy reduction
Representative sample
Sampling bias
Selection bias
Size reduction
Software
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Summary:Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-018-2164-8